This document describes a framework for representing and reasoning with geographic semantics. The framework enables visualizing knowledge discovery processes, automating tool selection to solve user-defined geographic problems, and evaluating semantic changes. Methods, data, and human experts are described using ontologies. An expert system then selects the optimal combination of resources to solve a given problem based on semantic descriptions. The resulting solution represents the formation history of any new information products and the semantic changes involved.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
A Review On Semantic Relationship Based Applicationsijfcstjournal
This document summarizes a research paper on semantic relationship-based applications. It discusses:
1. A wide range of semantic relationships that have been exploited in literature and computational theories, including content-dependent, content-independent, spatial, temporal and participation relationships.
2. How semantic relationships can be used for reasoning, information retrieval, and resolving inconsistencies by classifying concepts and providing more semantics.
3. Different classifications of semantic relationships used in domains like literature, computer science, biology, and how relationships are used for reasoning based on their properties.
An optimal unsupervised text data segmentation 3prj_publication
This document summarizes a research paper that proposes an unsupervised text data segmentation model using genetic algorithms (OUTDSM) to improve clustering accuracy. The OUTDSM uses an encoding strategy, fitness function, and genetic operators to evolve optimal text clusters. Experimental results presented in the research paper demonstrate that OUTDSM can arrive at global optima and prevent stagnation at local optima due to its biologically diverse population. Key areas of related work discussed include text mining techniques, clustering methods, and prior uses of optimization algorithms like genetic algorithms for text mining tasks.
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONijdms
This document discusses fuzzy statistical databases and their physical organization. It introduces the concept of fuzzy cardinality for counting elements in a fuzzy set or fuzzy relation. A fuzzy statistical database is proposed to store fuzzy statistics generated from fuzzy relational databases. This fuzzy statistical database contains fuzzy statistical tables, which can be type-1 or type-2 depending on the complexity of the fuzzy attributes. The physical organization of these fuzzy statistical tables is discussed to facilitate efficient storage and retrieval of the imprecise statistical data.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
REPRESENTATION OF UNCERTAIN DATA USING POSSIBILISTIC NETWORK MODELScscpconf
Uncertainty is a pervasive in real world environment due to vagueness, is associated with the
difficulty of making sharp distinctions and ambiguity, is associated with situations in which the
choices among several precise alternatives cannot be perfectly resolved. Analysis of large
collections of uncertain data is a primary task in the real world applications, because data is
incomplete, inaccurate and inefficient. Representation of uncertain data in various forms such
as Data Stream models, Linkage models, Graphical models and so on, which is the most simple,
natural way to process and produce the optimized results through Query processing. In this
paper, we propose the Uncertain Data model can be represented as Possibilistic data model
and vice versa for the process of uncertain data using various data models such as possibilistic
linkage model, Data streams, Possibilistic Graphs. This paper presents representation and
process of Possiblistic Linkage model through Possible Worlds with the use of product-based
operator.
A Review On Semantic Relationship Based Applicationsijfcstjournal
This document summarizes a research paper on semantic relationship-based applications. It discusses:
1. A wide range of semantic relationships that have been exploited in literature and computational theories, including content-dependent, content-independent, spatial, temporal and participation relationships.
2. How semantic relationships can be used for reasoning, information retrieval, and resolving inconsistencies by classifying concepts and providing more semantics.
3. Different classifications of semantic relationships used in domains like literature, computer science, biology, and how relationships are used for reasoning based on their properties.
An optimal unsupervised text data segmentation 3prj_publication
This document summarizes a research paper that proposes an unsupervised text data segmentation model using genetic algorithms (OUTDSM) to improve clustering accuracy. The OUTDSM uses an encoding strategy, fitness function, and genetic operators to evolve optimal text clusters. Experimental results presented in the research paper demonstrate that OUTDSM can arrive at global optima and prevent stagnation at local optima due to its biologically diverse population. Key areas of related work discussed include text mining techniques, clustering methods, and prior uses of optimization algorithms like genetic algorithms for text mining tasks.
FUZZY STATISTICAL DATABASE AND ITS PHYSICAL ORGANIZATIONijdms
This document discusses fuzzy statistical databases and their physical organization. It introduces the concept of fuzzy cardinality for counting elements in a fuzzy set or fuzzy relation. A fuzzy statistical database is proposed to store fuzzy statistics generated from fuzzy relational databases. This fuzzy statistical database contains fuzzy statistical tables, which can be type-1 or type-2 depending on the complexity of the fuzzy attributes. The physical organization of these fuzzy statistical tables is discussed to facilitate efficient storage and retrieval of the imprecise statistical data.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
The document presents a new model for intelligent social networks based on semantic tag ranking. It uses a multi-agent system approach with agents performing indexing and ranking. For indexing, it uses an enhanced Latent Dirichlet Allocation (E-LDA) model that optimizes LDA parameters. Tags above a threshold from E-LDA output are ranked using Tag Rank. Simulation results showed improvements in indexing and ranking over conventional methods. The model introduces semantics to social networks to improve search and link recommendation.
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
A comprehensive survey of link mining and anomalies detectioncsandit
This document provides an overview of link mining and its application to anomalies detection. It discusses the emergence of link mining, key link mining tasks including object-related, graph-related and link-related tasks. Challenges of link mining are described along with applications. Different types of anomalies are defined and three main approaches to anomalies detection - supervised, semi-supervised and unsupervised - are outlined along with common methods like nearest neighbor, clustering, statistical and information-based approaches.
A Deep Learning Model to Predict Congressional Roll Call Votes from Legislati...mlaij
This document describes a deep learning model called the Predict Text Classification Network (PTCN) that was developed to predict the outcome (pass/fail) of congressional roll call votes based solely on the text of legislation. The PTCN uses a hybrid convolutional and long short-term memory neural network architecture to analyze legislative texts and predict whether a vote will pass or fail. The model was tested on legislative texts from 2000-2019 and achieved an average prediction accuracy of 67.32% using 10-fold cross-validation, suggesting it can recognize patterns in language that correlate with congressional voting behaviors.
Trajectory Data Fuzzy Modeling : Ambulances Management Use Caseijdms
Data captured through mobile devices and sensors represent valuable information for organizations. This
collected information comes in huge volume and usually carry uncertain data. Due to this quality issue
difficulties occur in analyzing the trajectory data warehouse. Moreover, the interpretation of the analysis
can vary depending on the background of the user and this will make it difficult to fulfill the analytical
needs of an enterprise. In this paper, we will show the benefits of fuzzy logic in solving the challenges
related to mobility data by integrating fuzzy concepts into the conceptual and the logical model. We use the
ambulance management use case to illustrate our contributions.
This document discusses cognitive automation of data science tasks. It proposes that a cognitive system would incorporate knowledge from various structured and unstructured sources, past experiences, and user interactions to guide the machine learning process. It provides examples of how such a system could reason about issues like overfitting and user preferences to select appropriate algorithms and configurations. Key challenges for building such a cognitive system include knowledge representation, knowledge acquisition from multiple sources, and performing probabilistic reasoning on the knowledge to guide the automation process.
Mobile information collectors trajectory data warehouse designIJMIT JOURNAL
To analyze complex phenomena which involve moving objects, Trajectory Data Warehouse (TDW) seems to be an answer for many recent decision problems related to various professions (physicians, commercial representatives, transporters, ecologists …) concerned with mobility. This work aims to make trajectories as a first class concept in the trajectory data conceptual model and to design a TDW, in which data resulting from mobile information collectors’ trajectory are gathered. These data will be analyzed, according to trajectory characteristics, for decision making purposes, such as new products commercialization, new commerce implementation, etc.
The document presents an overview of searching in metric spaces. It discusses how similarity searching is needed for unstructured data like text, images, and audio, where exact matching is not possible. It describes how similarity is modeled using a distance function between objects in a metric space. The document surveys existing solutions from different fields that address proximity searching in metric spaces and vector spaces. It aims to provide a unified framework to analyze and categorize existing algorithms.
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
Blended intelligence of FCA with FLC for knowledge representation from cluste...IJECEIAES
Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
The document describes latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by distributions over words. It is a three-level hierarchical Bayesian model where documents are generated by first sampling a per-document topic distribution from a Dirichlet prior, then repeatedly sampling topics and words from these distributions. LDA addresses limitations of previous models by capturing statistical structure within and between documents through the hierarchical Bayesian formulation.
SEMANTIC VISUALIZATION AND NAVIGATION IN TEXTUAL CORPUSijistjournal
This document presents a semantic visualization and navigation approach for textual corpora. The approach aims to offer users three search modes: precise search, connotative search, and thematic search. It proposes new interaction paradigms to support the semantic aspects of the information space and guide users in their searches. The approach is based on semantic annotation of documents and represents information using a graph structure and fisheye visualization techniques. It allows navigation guided by a domain ontology for thematic searches or based on concept association relations for connotative searches. The goal is to help users locate relevant information and discover new knowledge.
The document introduces the visual mapping sentence (MS) methodology for multifaceted research design and analysis. The MS is a visual representation that classifies research variables into facets. It guides hypothesis generation and systematic data collection/analysis. The MS links components of a research domain to suggest relationships between variables and facets. It maps all relevant variables and provides information about excluded variables. Two examples of MS are included in figures to illustrate their use. The MS methodology is presented as a valuable tool that can help address limitations of other research methods by emphasizing conceptual definition and structure of content areas.
This document summarizes a research paper on clustering algorithms in data mining. It begins by defining clustering as an unsupervised learning technique that organizes unlabeled data into groups of similar objects. The document then reviews different types of clustering algorithms and methods for evaluating clustering results. Key steps in clustering include feature selection, algorithm selection, and cluster validation to assess how well the derived groups represent the underlying data structure. A variety of clustering algorithms exist and must be chosen based on the problem characteristics.
Challenging Issues and Similarity Measures for Web Document ClusteringIOSR Journals
This document discusses challenging issues and similarity measures for web document clustering. It begins with an introduction to text mining and document clustering. It then reviews related work on similarity approaches and measures. Some key challenging issues in web document clustering are discussed, such as measuring semantic similarity between words and evaluating cluster validity. Various types of similarity measures are also described, including string-based measures like Jaro-Winkler distance and corpus-based measures like latent semantic analysis. The conclusion states that accurate clustering requires a precise definition of similarity between document pairs and discusses different similarity measures that can be used.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
A TWO-STAGE HYBRID MODEL BY USING ARTIFICIAL NEURAL NETWORKS AS FEATURE CONST...IJDKP
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simpleneural network structure as the new feature construction tool in the firststage, thenthe newly created features are used asthe additional input variables in logistic regression in the second stage. The modelis compared with the traditional onestage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under ROC curve, andKS statistic. By creating new features with theneural network technique, the underlying nonlinear relationships between variables are identified. Furthermore, by using a verysimple neural network structure, the model could overcome the drawbacks of neural networks interms of its long training time, complex topology, and limited interpretability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses criteria interaction modeling in life cycle assessment (LCA) analysis. It examines using multi-criteria decision analysis (MCDA) methods to aggregate LCA impact categories in a more flexible way that accounts for interactions between criteria. Currently, LCA often uses simple weighted sums that assume criteria independence and full compensation between impacts. The document reviews different MCDA approaches and emphasizes methods that can model criteria interactions to improve LCA validity. It argues a single aggregation method is not ideal and proposes a framework combining techniques to minimize information loss when aggregating diverse LCA impacts.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
The document presents a new model for intelligent social networks based on semantic tag ranking. It uses a multi-agent system approach with agents performing indexing and ranking. For indexing, it uses an enhanced Latent Dirichlet Allocation (E-LDA) model that optimizes LDA parameters. Tags above a threshold from E-LDA output are ranked using Tag Rank. Simulation results showed improvements in indexing and ranking over conventional methods. The model introduces semantics to social networks to improve search and link recommendation.
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
A comprehensive survey of link mining and anomalies detectioncsandit
This document provides an overview of link mining and its application to anomalies detection. It discusses the emergence of link mining, key link mining tasks including object-related, graph-related and link-related tasks. Challenges of link mining are described along with applications. Different types of anomalies are defined and three main approaches to anomalies detection - supervised, semi-supervised and unsupervised - are outlined along with common methods like nearest neighbor, clustering, statistical and information-based approaches.
A Deep Learning Model to Predict Congressional Roll Call Votes from Legislati...mlaij
This document describes a deep learning model called the Predict Text Classification Network (PTCN) that was developed to predict the outcome (pass/fail) of congressional roll call votes based solely on the text of legislation. The PTCN uses a hybrid convolutional and long short-term memory neural network architecture to analyze legislative texts and predict whether a vote will pass or fail. The model was tested on legislative texts from 2000-2019 and achieved an average prediction accuracy of 67.32% using 10-fold cross-validation, suggesting it can recognize patterns in language that correlate with congressional voting behaviors.
Trajectory Data Fuzzy Modeling : Ambulances Management Use Caseijdms
Data captured through mobile devices and sensors represent valuable information for organizations. This
collected information comes in huge volume and usually carry uncertain data. Due to this quality issue
difficulties occur in analyzing the trajectory data warehouse. Moreover, the interpretation of the analysis
can vary depending on the background of the user and this will make it difficult to fulfill the analytical
needs of an enterprise. In this paper, we will show the benefits of fuzzy logic in solving the challenges
related to mobility data by integrating fuzzy concepts into the conceptual and the logical model. We use the
ambulance management use case to illustrate our contributions.
This document discusses cognitive automation of data science tasks. It proposes that a cognitive system would incorporate knowledge from various structured and unstructured sources, past experiences, and user interactions to guide the machine learning process. It provides examples of how such a system could reason about issues like overfitting and user preferences to select appropriate algorithms and configurations. Key challenges for building such a cognitive system include knowledge representation, knowledge acquisition from multiple sources, and performing probabilistic reasoning on the knowledge to guide the automation process.
Mobile information collectors trajectory data warehouse designIJMIT JOURNAL
To analyze complex phenomena which involve moving objects, Trajectory Data Warehouse (TDW) seems to be an answer for many recent decision problems related to various professions (physicians, commercial representatives, transporters, ecologists …) concerned with mobility. This work aims to make trajectories as a first class concept in the trajectory data conceptual model and to design a TDW, in which data resulting from mobile information collectors’ trajectory are gathered. These data will be analyzed, according to trajectory characteristics, for decision making purposes, such as new products commercialization, new commerce implementation, etc.
The document presents an overview of searching in metric spaces. It discusses how similarity searching is needed for unstructured data like text, images, and audio, where exact matching is not possible. It describes how similarity is modeled using a distance function between objects in a metric space. The document surveys existing solutions from different fields that address proximity searching in metric spaces and vector spaces. It aims to provide a unified framework to analyze and categorize existing algorithms.
An Overview of Information Extraction from Mobile Wireless Sensor NetworksM H
Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.
Blended intelligence of FCA with FLC for knowledge representation from cluste...IJECEIAES
Formal concept analysis is the process of data analysis mechanism with emergent attractiveness across various fields such as data mining, robotics, medical, big data and so on. FCA is helpful to generate the new learning ontology based techniques. In medical field, some growing kids are facing the problem of representing their knowledge from their gathered prior data which is in the form of unordered and insufficient clustered data which is not supporting them to take the right decision on right time for solving the uncertainty based questionnaires. In the approach of decision theory, many mathematical replicas such as probability-allocation, crisp set, and fuzzy based set theory were designed to deals with knowledge representation based difficulties along with their characteristic. This paper is proposing new ideological blended approach of FCA with FLC and described with major objectives: primarily the FCA analyzes the data based on relationships between the set of objects of prior-attributes and the set of attributes based prior-data, which the data is framed with data-units implicated composition which are formal statements of idea of human thinking with conversion of significant intelligible explanation. Suitable rules are generated to explore the relationship among the attributes and used the formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making. Secondly how the FLC derive the fuzzification, rule-construction and defuzzification methods implicated for representing the accurate knowledge for uncertainty based questionnaires. Here the FCA is projected to expand the FCA based conception with help of the objective based item set notions considered as the target which is implicated with the expanded cardinalities along with its weights which is associated through the fuzzy based inference decision rules. This approach is more helpful for medical experts for knowing the range of patient’s memory deficiency also for people whose are facing knowledge explorer deficiency.
Data mining is an integrated field, depicted technologies in combination to the areas having database, learning by machine, statistical study, and recognition in patterns of same type, information regeneration, A.I networks, knowledge-based portfolios, artificial intelligence, neural network, and data determination. In real terms, mining of data is the investigation of provisional data sets for finding hidden connections and to gather the information in peculiar form which are justifiable and understandable to the owner of gather or mined data. An unsupervised formula which differentiate data components into collections by which the components in similar group are more allied to one other and items in rest of cluster seems to be non-allied, by the criteria of measurement of equality or predictability is called process of clustering. Cluster analysis is a relegating task that is utilized to identify same group of object and it is additionally one of the most widely used method for many practical application in data mining. It is a method of grouping objects, where objects can be physical, such as a student or may be a summary such as customer comportment, handwriting. It has been proposed many clustering algorithms that it falls into the different clustering methods. The intention of this paper is to provide a relegation of some prominent clustering algorithms.
The document describes latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by distributions over words. It is a three-level hierarchical Bayesian model where documents are generated by first sampling a per-document topic distribution from a Dirichlet prior, then repeatedly sampling topics and words from these distributions. LDA addresses limitations of previous models by capturing statistical structure within and between documents through the hierarchical Bayesian formulation.
SEMANTIC VISUALIZATION AND NAVIGATION IN TEXTUAL CORPUSijistjournal
This document presents a semantic visualization and navigation approach for textual corpora. The approach aims to offer users three search modes: precise search, connotative search, and thematic search. It proposes new interaction paradigms to support the semantic aspects of the information space and guide users in their searches. The approach is based on semantic annotation of documents and represents information using a graph structure and fisheye visualization techniques. It allows navigation guided by a domain ontology for thematic searches or based on concept association relations for connotative searches. The goal is to help users locate relevant information and discover new knowledge.
The document introduces the visual mapping sentence (MS) methodology for multifaceted research design and analysis. The MS is a visual representation that classifies research variables into facets. It guides hypothesis generation and systematic data collection/analysis. The MS links components of a research domain to suggest relationships between variables and facets. It maps all relevant variables and provides information about excluded variables. Two examples of MS are included in figures to illustrate their use. The MS methodology is presented as a valuable tool that can help address limitations of other research methods by emphasizing conceptual definition and structure of content areas.
This document summarizes a research paper on clustering algorithms in data mining. It begins by defining clustering as an unsupervised learning technique that organizes unlabeled data into groups of similar objects. The document then reviews different types of clustering algorithms and methods for evaluating clustering results. Key steps in clustering include feature selection, algorithm selection, and cluster validation to assess how well the derived groups represent the underlying data structure. A variety of clustering algorithms exist and must be chosen based on the problem characteristics.
Challenging Issues and Similarity Measures for Web Document ClusteringIOSR Journals
This document discusses challenging issues and similarity measures for web document clustering. It begins with an introduction to text mining and document clustering. It then reviews related work on similarity approaches and measures. Some key challenging issues in web document clustering are discussed, such as measuring semantic similarity between words and evaluating cluster validity. Various types of similarity measures are also described, including string-based measures like Jaro-Winkler distance and corpus-based measures like latent semantic analysis. The conclusion states that accurate clustering requires a precise definition of similarity between document pairs and discusses different similarity measures that can be used.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
A TWO-STAGE HYBRID MODEL BY USING ARTIFICIAL NEURAL NETWORKS AS FEATURE CONST...IJDKP
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simpleneural network structure as the new feature construction tool in the firststage, thenthe newly created features are used asthe additional input variables in logistic regression in the second stage. The modelis compared with the traditional onestage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under ROC curve, andKS statistic. By creating new features with theneural network technique, the underlying nonlinear relationships between variables are identified. Furthermore, by using a verysimple neural network structure, the model could overcome the drawbacks of neural networks interms of its long training time, complex topology, and limited interpretability.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses criteria interaction modeling in life cycle assessment (LCA) analysis. It examines using multi-criteria decision analysis (MCDA) methods to aggregate LCA impact categories in a more flexible way that accounts for interactions between criteria. Currently, LCA often uses simple weighted sums that assume criteria independence and full compensation between impacts. The document reviews different MCDA approaches and emphasizes methods that can model criteria interactions to improve LCA validity. It argues a single aggregation method is not ideal and proposes a framework combining techniques to minimize information loss when aggregating diverse LCA impacts.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document reviews research on classifying crops from remotely sensed images. It discusses how multispectral and hyperspectral imagery have been used with both supervised and unsupervised classification techniques. Multispectral imagery provides good information for overall vegetation mapping but has limitations differentiating similar crops. Hyperspectral imagery can help overcome these limitations by identifying fine spectral differences. The document also discusses how microwave remote sensing, which is unaffected by clouds, can complement optical imagery by improving classification accuracy when data is fused. Overall, the review finds that remote sensing is useful for crop monitoring but challenges remain in identifying multiple crop types and differentiating similar crops.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This study investigated the relationship between arc welding quality in small-scale metalworking businesses in Kenya and artisans' education levels, training modes, and business locations. 72 artisans participated, divided into groups based on primary or secondary education, formal training or on-the-job training, and urban or rural business locations. Artisans fabricated steel products which were assessed for welding quality. The study found that informally trained artisans with secondary education working in urban areas had the highest quality, while informally trained primary educated artisans in rural areas had the lowest. Generally, secondary educated artisans performed better. Formal training improved quality for rural artisans. Business location only affected informally trained artisans' quality.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document proposes using a genetic algorithm approach to parallelize cryptographic algorithms and identify encryption keys. It describes generating random numbers using a linear congruential equation, then applying crossover and mutation operators from genetic algorithms to the numbers. The encrypted data and key are transmitted over the network. Decryption reverses the encryption process. Testing on different core machines showed the parallelized encryption had faster execution times than serial encryption, with greater speedups on more cores. The authors conclude the genetic algorithm operators improve performance and security compared to other algorithms.
O documento discute a transformação IHS para descrever propriedades de cor em imagens e sua aplicação para fusão de imagens de diferentes resoluções espaciais, apresentando exemplos de imagens LANDSAT, Quickbird, WorldView-2 e Geoeye.
Este documento describe la evolución de los dispositivos móviles y su convergencia con otras tecnologías, lo que ha permitido una variedad de aplicaciones. Explica algunos usos actuales de los teléfonos celulares en la sociedad como banca electrónica, comercio electrónico y redes sociales. También discute el uso creciente de teléfonos celulares en el aula, con aplicaciones como mensajes de texto, redes sociales y funciones de cámara y grabación.
O documento resume a 12a edição do evento ConVivência, que discute temas relacionados a idosos. O evento contou com palestras e workshops sobre saúde, comunicação, políticas públicas e inclusão digital para idosos. Também teve shows nacionais e expositores de segmentos variados. O público do evento foi majoritariamente da região Sul do Brasil.
This document contains character sheets for tracking ability scores, skills, attacks, and other attributes of player characters in a roleplaying game. It lists over 30 skills and their associated abilities. The sheets provide spaces to record characteristics like name, class, race, ability scores, hit points, armor class, speeds, skills and their modifiers, attacks, saving throws, and equipment.
O documento discute a crise econômica na Europa e seus impactos no Brasil, incluindo a guerra fiscal e cambial. A crise teve origem na Grécia em 2010 devido a dívidas públicas, afetando países como Portugal, Irlanda, Itália e Espanha. Isso reduziu o comércio entre Brasil e União Europeia e gerou insegurança nas empresas brasileiras em relação a impostos e dupla tributação.
O documento descreve a Idade Média Árabe-Islâmica, incluindo os costumes dos beduínos na Península Arábica, a vida e pregações do profeta Maomé, e o surgimento e expansão do Islã como religião monoteísta.
This document discusses the importance of Irish companies reviewing and strengthening their policies for managing foreign exchange (FX) risk given upcoming volatility in the British pound sterling rate due to the Brexit vote. It notes that many Irish SMEs that export to the UK either do not have a formal FX risk management policy or do not actively follow their policy. The document outlines elements that should be included in a strong FX risk management policy, such as identifying exposure levels, setting hedging targets, and making FX management a board-level issue rather than just a finance department responsibility. It emphasizes that an effective policy can help companies protect profits by reducing risks from currency fluctuations.
Este álbum de fotografías contiene imágenes de varios miembros de la familia como el papá, la mamá, el hermano, la prima, la tía, la abuela y el abuelo.
Este documento describe algunos principios clave para planificar y desarrollar experiencias educativas basadas en la tecnología. La calidad educativa depende del método y las actividades de aprendizaje, no de la tecnología en sí. Las TIC deben usarse de manera socioconstructiva para que los estudiantes construyan conocimiento colaborando entre sí. La tecnología permite manipular y compartir información fácilmente, por lo que los estudiantes deben desarrollar habilidades para hacer un uso adecuado de ella y resolver problemas. Las
El estrés es una respuesta del cuerpo a condiciones externas que perturban el equilibrio de una persona y causan ansiedad, dolor de cabeza, insomnio e indigestión entre otros síntomas. El estrés produce cambios químicos en el organismo a través de la liberación de hormonas que aumentan la frecuencia cardíaca, la presión arterial y los niveles de insulina. El tratamiento del estrés generalmente implica métodos combinados de medicamentos y terapia psicológica, pero lo más recomendable es identificar y cambiar la causa
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
A semantic framework and software design to enable the transparent integratio...Patricia Tavares Boralli
This document proposes a conceptual framework to unify representations of natural systems knowledge. The framework is based on separating the ontological nature of an object of study from the context of its observation. Each object is associated with a concept defined in an ontology and an observation context describing aspects like location and time. Models and data are treated as generic knowledge sources with a semantic type and observation context. This allows flexible integration and calculation of states across heterogeneous sources by composing their observation contexts and resolving semantic compatibility. The framework aims to simplify knowledge representation by abstracting away complexity related to data format and scale.
Concept integration using edit distance and n gram match ijdms
Information is growing more rapidly on the World Wide Web (WWW) has made it necessary to make all
this information not only available to people but also to the machines. Ontology and token are widely being
used to add the semantics in data processing or information processing. A concept formally refers to the
meaning of the specification which is encoded in a logic-based language, explicit means concepts,
properties that specification is machine readable and also a conceptualization model how people think
about things of a particular subject area. In modern scenario more ontologies has been developed on
various different topics, results in an increased heterogeneity of entities among the ontologies. The concept
integration becomes vital over last decade and a tool to minimize heterogeneity and empower the data
processing. There are various techniques to integrate the concepts from different input sources, based on
the semantic or syntactic match values. In this paper, an approach is proposed to integrate concept
(Ontologies or Tokens) using edit distance or n-gram match values between pair of concept and concept
frequency is used to dominate the integration process. The proposed techniques performance is compared
with semantic similarity based integration techniques on quality parameters like Recall, Precision, FMeasure
& integration efficiency over the different size of concepts. The analysis indicates that edit
distance value based interaction outperformed n-gram integration and semantic similarity techniques.
ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia
This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
Data mining , knowledge discovery is the process
of analyzing data from different perspectives and summarizing it
into useful information - information that can be used to increase
revenue, cuts costs, or both. Data mining software is one of a
number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize
it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among
dozens of fields in large relational databases. The goal of
clustering is to determine the intrinsic grouping in a set of
unlabeled data. But how to decide what constitutes a good
clustering? It can be shown that there is no absolute “best”
criterion which would be independent of the final aim of the
clustering. Consequently, it is the user which must supply this
criterion, in such a way that the result of the clustering will suit
their needs.
For instance, we could be interested in finding
representatives for homogeneous groups (data reduction), in
finding “natural clusters” and describe their unknown properties
(“natural” data types), in finding useful and suitable groupings
(“useful” data classes) or in finding unusual data objects (outlier
detection).Of late, clustering techniques have been applied in the
areas which involve browsing the gathered data or in categorizing
the outcome provided by the search engines for the reply to the
query raised by the users. In this paper, we are providing a
comprehensive survey over the document clustering.
Great model a model for the automatic generation of semantic relations betwee...ijcsity
The
large
a
v
ailable
am
ou
n
t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
information
in
a
decision making
pro
-
cess.
Differe
n
t
pr
o
jects
and t
o
ols
h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
t
y
b
y
classifying,
summarizing
or
annotating
the
texts.
P
artic
-
ularl
y
,
text
summary
strategies
h
av
e
pr
ov
en
to
b
e
v
ery
useful
to
pr
o
vide
a
compact
view
of
an
original
text.
H
ow
e
v
er,
the
a
v
ailable
strategies
to
generate
these
summaries
do
not
fit
v
ery
w
ell
within
the
domains
that
require
ta
k
e
i
n
to
consideration
the
tem
p
oral
dimension
of
the
text
(e.g.
a
rece
n
t
piece
of
text
in
a
medical
record
is
more
im
p
orta
n
t
than
a
pre
-
vious
one)
and
the
profile
of
the
p
erson
who
requires
the
summary
(e.g
the
medical
s
p
ecialization).
T
o
co
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these
limitations
this
pa
p
er
prese
n
ts
”GRe
A
T”
a
m
o
del
for
automatic
summary
generation
that
re
-
lies
on
natural
language
pr
o
cessing
and
text
mining
te
c
hniques
to
extract
the
most
rele
v
a
n
t
information
from
narrati
v
e
texts
and
disc
o
v
er
new
in
-
formation
from
the
detection
of
related
information. GRe
A
T
M
o
del
w
as impleme
n
ted
on
sof
tw
are
to
b
e
v
alidated
in
a
health
institution
where
it
has
sh
o
wn
to
b
e
v
ery
useful
to displ
a
y
a
preview
of
the
information
a
b
ou
t
medical
health
records
and
disc
o
v
er
new
facts
and
h
y
p
otheses
within
the
information.
Se
v
eral
tests
w
ere
executed
su
c
h
as
F
unctional
-
i
t
y
,
Usabili
t
y
and
P
erformance
regarding
to
the
impleme
n
ted
sof
t
w
are.
In
addition,
precision
and
recall
measures
w
ere
applied
on
the
results
ob
-
tained
through
the
impleme
n
ted
t
o
ol,
as
w
ell
as
on
the
loss
of
information
obtained
b
y
pr
o
viding
a
text
more
shorter than
the
original
Document Retrieval System, a Case StudyIJERA Editor
In this work we have proposed a method for automatic indexing and retrieval. This method will provide as a
result the most likelihood document which is related to the input query. The technique used in this project is
known as singular-value decomposition, in this method a large term by document matrix is analyzed and
decomposed into 100 factors. Documents are represented by 100 item vector of factor weights. On the other
hand queries are represented as pseudo-document vectors, which are formed from weighed combinations of
terms.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
This document describes a proposed Optimal Frequent Patterns System (OFPS) that uses a genetic algorithm to discover optimal frequent patterns from transactional databases more efficiently. The OFPS is a three-fold system that first prepares data through cleaning, integration and transformation. It then constructs a Frequent Pattern Tree to discover frequent patterns. Finally, it applies a genetic algorithm to generate optimal frequent patterns, simulating biological evolution to find the best solutions. The proposed system aims to overcome limitations of conventional association rule mining approaches and efficiently discover optimal patterns from large, changing datasets.
Query Optimization Techniques in Graph Databasesijdms
Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for
storing and managing data with graph-like structure. Today, they represent a requirementfor many
applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize
queries in graph databases, have been used in traditional databases, distribution systems,… or they are
inspired from graph theory. However, their reuse in graph databases should take care of the main
characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to
efficiently access data relationships. In this paper, we survey the query optimization techniques in graph
databases. In particular,we focus on the features they have in
Analysis of Opinionated Text for Opinion Miningmlaij
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
Running head Multi-actor modelling system 1Multi-actor mod.docxtodd581
Running head: Multi-actor modelling system 1
Multi-actor modelling system3
Multi-actor modelling system
Yogesh Dagwale
University of the Cumberland’s
Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., & Kettenis, D. L. (2004). A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of environmental management, 72(1-2), 43-55.
They talk about the potential and restrictions of the MAS to manufacture models that empower spatial organizers to incorporate the 'actor factor' in their examination. Their structure system contemplates actors who assume a functioning job in the spatial planning. They included actors who can watch and see a spatial domain. Using these perceptions and discernment they produce an inclination for a preferred spatial situation. Actors at that point present and discuss their inclinations amid their exchanges with different actors.
The inclinations of the actor fill in as inputs for an official choice making. Finally, ultimate conclusions are actualized in the spatial framework. They found that MAS can produce space utilization designs in light of a portrayal of a multi-actor planning process. It additionally can clear up the impacts of actors under the administration of various planning styles on the space utilization and prove how the relations between actors change amid a planning process and under different orders of coming up with decisions. Unlike the work by Parker, Manson, Janssen, Hoffman & Deadman,2003, cited below, this paper did not include the various challenges associated with the use of MAS.
Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the association of American Geographers, 93(2), 314-337.
In this paper, they studied different models. These models, however, were not thorough enough and therefore they took into account the multi-actor system, dynamic spatial Simulation, which has two components, that is, a cellular model that speaks to biogeophysical and biological parts of a demonstrated framework and an actor-based model to speak to human conclusion making. Because of its nature and ability to model complex situations, they highlighted some of the areas that MAS can be applied where other models cannot be able to deliver. Such areas are modeling of emergent phenomena whereby MAS can model landscape plans, due to its flexibility, MAS can represent complex land use/ cover systems, and they can be used to model dynamic paths. They also outlined the various challenges to Multi-actor systems. Such challenges include an understanding of complexity, individual decision making, empirical parameterization and model validation, and communication.
Faber, N. R., & Jorna, R. J. (2011, June). The use of multi-actor systems for studying social sustainability: Theoretical backgrounds and pseudo-specifications. In Com.
Running head Multi-actor modelling system 1Multi-actor mod.docxglendar3
Running head: Multi-actor modelling system 1
Multi-actor modelling system3
Multi-actor modelling system
Yogesh Dagwale
University of the Cumberland’s
Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., & Kettenis, D. L. (2004). A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of environmental management, 72(1-2), 43-55.
They talk about the potential and restrictions of the MAS to manufacture models that empower spatial organizers to incorporate the 'actor factor' in their examination. Their structure system contemplates actors who assume a functioning job in the spatial planning. They included actors who can watch and see a spatial domain. Using these perceptions and discernment they produce an inclination for a preferred spatial situation. Actors at that point present and discuss their inclinations amid their exchanges with different actors.
The inclinations of the actor fill in as inputs for an official choice making. Finally, ultimate conclusions are actualized in the spatial framework. They found that MAS can produce space utilization designs in light of a portrayal of a multi-actor planning process. It additionally can clear up the impacts of actors under the administration of various planning styles on the space utilization and prove how the relations between actors change amid a planning process and under different orders of coming up with decisions. Unlike the work by Parker, Manson, Janssen, Hoffman & Deadman,2003, cited below, this paper did not include the various challenges associated with the use of MAS.
Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the association of American Geographers, 93(2), 314-337.
In this paper, they studied different models. These models, however, were not thorough enough and therefore they took into account the multi-actor system, dynamic spatial Simulation, which has two components, that is, a cellular model that speaks to biogeophysical and biological parts of a demonstrated framework and an actor-based model to speak to human conclusion making. Because of its nature and ability to model complex situations, they highlighted some of the areas that MAS can be applied where other models cannot be able to deliver. Such areas are modeling of emergent phenomena whereby MAS can model landscape plans, due to its flexibility, MAS can represent complex land use/ cover systems, and they can be used to model dynamic paths. They also outlined the various challenges to Multi-actor systems. Such challenges include an understanding of complexity, individual decision making, empirical parameterization and model validation, and communication.
Faber, N. R., & Jorna, R. J. (2011, June). The use of multi-actor systems for studying social sustainability: Theoretical backgrounds and pseudo-specifications. In Com.
Ontology Based PMSE with Manifold PreferenceIJCERT
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Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
Ontology may be a conceptualization of a website into a human understandable, however machine-
readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the
intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
An improved technique for ranking semantic associationst07IJwest
The primary focus of the search techniques in the first generation of the Web is accessing relevant
documents from the Web. Though it satisfies user requirements, but it is insufficient as the user sometimes
wishes to access actionable information involvin
g complex relationships between two given entities.
Finding such complex relationships (also known as semantic associations) is especially useful in
applications
such as
National Security, Pharmacy, Business Intelligence etc. Therefore the next frontier is
discovering relevant semantic associations between two entities present in large semantic metadata
repositories. Given two entities, there exist a huge number of semantic associations between two entities.
Hence ranking of these associations is required i
n order to find more relevant associations. For this
Aleman Meza et al. proposed a method involving six metrics viz. context, subsumption, rarity, popularity,
association length and trust. To compute the overall rank of the associations this method compute
s
context, subsumption, rarity and popularity values for each component of the association and for all the
associations. However it is obvious that, many components appears repeatedly in many associations
therefore it is not necessary to compute context, s
ubsumption, rarity
,
popularity
,
and
trust
values of the
components every time for each association rather the previously computed values may be used while
computing the overall rank of the associations. Thi
s paper proposes a method to re
use the previously
computed values using a hash data structure thus reduce the execution time. To demonstrate the
effectiveness of the proposed method, experiments were conducted on SWETO ontology. Results show
that the proposed method is more efficient than the other existi
ng methods
.
A Survey On Ontology Agent Based Distributed Data MiningEditor IJMTER
With the increased complexity in number of applications and due to large volume
of availability of data from heterogeneous sources, there is a need for the development of
suitable ontology, which can handle the large data set and present the mined outcomes for
evaluation intelligently. In the era of intensive data driven applications distributed data mining can
meet the challenges with the support of agents. This paper discusses the underlying principles for
effectiveness of modern agent-based systems for distributed data mining
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
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How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
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BPSC-105 important questions for june term end exam
Dc32644652
1. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
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A Novel Expert System for Reasoning Based on Semantic Cancer
Taghi Karimi
Department of Mathematics, Payame Noor University, Tehran, Iran
Abstract
This paper describes a programmatic framework
for representing, manipulating and reasoning with
geographic semantics. The framework enables
visualizing knowledge discovery, automating tool
selection for user defined geographic problem
solving, and evaluating semantic change in
knowledge discovery environments. Methods,
data, and human experts (our resources) are
described using ontologies. An entity’s ontology
describes, where applicable: uses, inputs, outputs,
and semantic changes. These ontological
descriptions are manipulated by an expert system
to select methods, data and human experts to solve
a specific user-defined problem; that is, a
semantic description of the problem is compared
to the services that each entity can provide to
construct a graph of potential solutions. A
minimal spanning tree representing the optimal
(least cost) solution is extracted from this graph,
and displayed in real-time. The semantic
change(s) that result from the interaction of data,
methods and people contained within the resulting
tree are determined via expressions of
transformation semantics represented within the
JESS expert system shell. The resulting
description represents the formation history of
each new information product (such as a map or
overlay) and can be stored, indexed and searched
as required. Examples are presented to show (1)
the construction and visualization of information
products, (2) the reasoning capabilities of the
system to find alternative ways to produce
information products from a set of data methods
and expertise, given certain constraints and (3) the
representation of the ensuing semantic changes by
which an information product is synthesized.
I. Introduction
The importance of semantics in geographic
information is well documented [1-4]. Semantics are
a key component of interoperability between GIS;
there are now robust technical solutions to
interoperate geographic information in a syntactic
and schematic sense (e.g. OGC, NSDI) but these fail
to take account of any sense of meaning associated
with the information. describe how exchanging data
between systems often fails due to confusion in the
meaning of concepts. Such confusion, or semantic
heterogeneity, significantly hinders collaboration if
groups cannot agree on a common lexicon for core
concepts. Semantic heterogeneity is also blamed for
the inefficient exchange of geographic concepts and
information between groups of people with differing
ontologies [5-9].
Semantic issues pervade the creation, use and re-
purposing of geographic information. In an
information economy we can identify the roles of
information producer and information consumer, and
in some cases, in national mapping agencies for
example, datasets are often constructed incrementally
by different groups of people with an implicit (but
not necessarily recorded) goal. The overall meaning
of the resulting information products are not always
obvious to those outside that group, existing for the
most part in the creators‘ mental model. When
solving a problem, a user may gather geospatial
information from a variety of sources without ever
encountering an explicit statement about what the
data mean, or what they are (and are not) useful for.
Without capturing the semantics of the data
throughout the process of creation, the data may be
misunderstood, be used inappropriately, or not used
at all when they could be. The consideration of
geospatial semantics needs to explicitly cater for the
particular way in which geospatial tasks are
undertaken [10]. As a result, the underlying
assumptions about methods used with data, and the
roles played by human expertise need to be
represented in some fashion so that a meaningful
association can be made between appropriate
methods, people and data to solve a problem. It is not
the role of this paper to present a definitive taxonomy
of geographic operations or their semantics. To do so
would trivialize the difficulties of defining
geographic semantics
II. Background and Aims
This paper presents a programmatic
framework for representing, manipulating and
reasoning with geographic semantics. In general
semantics refers to the study of the relations between
symbols and what they represent [11]. In the
framework outlined in this paper, semantics have two
valuable and specific roles. Firstly, to determine the
most appropriate resources (method, data or human
expert) to use in concert to solve a geographic
problem, and secondly to act as a measure of change
in meaning when data are operated on by methods
and human experts. Both of these roles are discussed
in detail in Section 3. The framework draws on a
number of different research fields, specifically:
geographical semantics [11-13]ontologies
computational semantics, constraint-based reasoning
and expert systems and visualization to represent
2. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
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645 | P a g e
aspects of these resources. The framework sets out to
solve a multi-layered problem of visualizing
knowledge discovery, automating tool selection for
user defined geographic problem solving and
evaluating semantic change in knowledge discovery
environments. The end goal of the framework is to
associate with geospatial information products the
details of their formation history and tools by which
to browse, query and ultimately understand this
formation history, thereby building a better
understanding of meaning and appropriate use of the
information.
The problem of semantic heterogeneity arises due to
the varying interpretations given to the terms used to
describe facts and concepts. Semantic heterogeneity
exists in two forms, cognitive and naming. Cognitive
semantic heterogeneity results from no common base
of definitions between two (or more) groups. As an
example, think of these as groups of scientists
attempting to collaborate. If the two groups cannot
agree on definitions for their core concepts then
collaboration between them will be problematic.
Defining such points of agreement amounts to
constructing a shared ontology, or at the very least,
points of overlap [12-17].
Naming semantic heterogeneity occurs when the
same name is used for different concepts or different
names are used for the same concept. It is not
possible to undertake any semantic analysis until
problems of semantic heterogeneity are resolved.
Ontologies, described below, are widely
recommended as a means of rectifying semantic
heterogeneity. The framework presented in this paper
utilizes that work and other ontological research to
solve the problem of semantic heterogeneity.
The use of an expert system for automated reasoning
fits well with the logical semantics utilized within the
framework. The Java Expert System Shell (JESS) is
used to express diverse semantic aspects about
methods, data, and human experts. JESS performs
string comparisons of resource attributes (parsed
from ontologies) using backward chaining to
determine interconnections between resources.
Backward chaining is a goal driven problem solving
methodology, starting from the set of possible
solutions and attempting to derive the problem. If the
conditions for a rule to be satisfied are not found
within that rule, the engine searches for other rules
that have the unsatisfied rule as their conclusion,
establishing dependencies between rules. JESS
functions as a mediator system, with a foundation
layer where the methods, data, and human experts are
described (the domain ontology), a mediation layer
with a view of the system (the task ontology) and a
user interface layer (for receiving queries and
displaying results).
1.1 Ontology
In philosophy, Ontology is the ―study of the kinds of
things that exist‖ In the artificial intelligence
community, ontology has one of two meanings, as a
representation vocabulary, typically specialized to
some domain or subject matter and as a body of
knowledge describing some domain using such a
representation vocabulary [18]. The goal of sharing
knowledge can be accomplished by encoding domain
knowledge using a standard vocabulary based on an
ontology. The framework described here utilizes both
definitions of ontology.
The representation vocabulary embodies the
conceptualizations that the terms in the vocabulary
are intended to capture. Relationships described
between conceptual elements in this ontology allow
Figure 1 – Interrelationships between different types of
ontology.
Figure 2 – Information products are
derived from the interaction of entities.
3. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
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646 | P a g e
for the production of rules governing how these
elements can be ―connected‖ or ―wired together‖ to
solve a geographic problem. In our case these
elements are methods, data and human experts, each
with their own ontology. In the case of datasets, a
domain ontology describes salient properties such as
location, scale, date, format, etc., as currently
captured in meta-data descriptions (see Figure 1). In
the case of methods, a domain ontology describes the
services a method provides in terms of a
transformation from one semantic state to another. In
the case of human experts the simplest representation
is again a domain ontology that shows the
contribution that a human can provide in terms of
steering or configuring methods and data. However,
it should also be possible to represent a two-way flow
of knowledge as the human learns from situations
and thereby expands the number of services they can
provide (we leave this issue for future work). The
synthesis of a specific information product is
specified via a task ontology that must fuse together
elements of the domain and application ontologies to
attain its goal. An innovation of this framework is the
dynamic construction of the solution network,
analogous to the application ontology. In order for
resources to be useful in solving a problem, their
ontologies must also overlap. Ontology is a useful
metaphor for describing the genesis of the
information product. A body of knowledge described
using the domain ontology is utilized in the initial
phase of setting up the expert system. A task
ontology is created at the conclusion of the
automated process specifically defining the concepts
that are available. An information product is derived
from the use of data extracted from databases and
knowledge from human experts in methods, as shown
in figure 2.
By forming a higher level ontology which describes
the relationships between each of these resources it is
possible to describe appropriate interactions. As a
simple example (figure 3), assume a user wishes to
evaluate landuse/landcover change over a period of
time. Classifying two LandsatTM images from 1990
and 2000 using reference data and expert knowledge,
the user can compare the two resulting images to
produce a map of the area(s) which have changed
during that time. One interesting feature
demonstrated in this example is the ability of human
experts to gain experience through repeated exposure
to similar situations. Even in this simple example a
basic semantic structure is being constructed and a
lineage of the data can be determined. Arguably an
additional intermediate data product exists between
the classifiers and the comparison; it has been
removed for clarity.
1.2 Semantics
While the construction of the information product is
important, a semantic layer sits above the operations
and information (figure 4). The geospatial knowledge
obtained during the creation of the product is
captured within this layer. The capture of this
semantic information describes the transformations
that the geospatial information undergoes, facilitating
better understanding and providing a measure of
repeatability of analysis, and improving
communication in the hope of promoting best
practice in bringing geospatial information to bear.
Egenhofer (2002) notes that the challenge remains of
how best to make these semantics available to the
user via a search interface. Pundt and Bishr, (2002)
outline a process in which a user searches for data to
solve a problem. This search methodology is also
applicable for the methods and human experts to be
used with the data. This solution fails when multiple
sources are available and nothing is known of their
content, structure and semantics. The use of pre-
defined ontologies aids users by reducing the
available search space. Ontological concepts relevant
to a problem domain are supplied to the user allowing
them to focus their query. A more advanced interface
would take the user‘s query in their own terms and
map that to an underlying domain ontology (Bishr,
1998).
As previously noted, the meaning of geospatial
information is constructed, shaped and changed by
the interaction of people and systems. Subsequently
the interaction of human experts, methods and data
needs to be carefully planned. A product created as a
result of these interactions is dependent on the
ontology of the data and methods and the
Figure 3 – A concrete example of the interaction of
methods, data and human experts to produce an
information product.
4. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652
647 | P a g e
epistemologies and ontologies of the human experts.
In light of this, the knowledge framework outlined
below focuses on each of the resources involved
(data, methods and human experts) and the roles they
play in the evolution of a new information product.
In addition, the user‘s goal that produced the product,
and any constraints placed on the process are
recorded to capture aspects of intention and situation
that also have an impact on meaning. This process
and the impact of constraint based searches are
discussed in more detail in the following section.
III. Knowledge framework
The problem described in the introduction has
been implemented as three components. The first,
and the simplest, is the task of visualizing the
network of interactions by which new information
products are synthesized. The second, automating
the construction of such a network for a user-defined
task, is interdependent with the third, evaluating
semantic change in a knowledge discovery
environment, and both utilize functionality of the
first. An examination of the abstract properties of
data, methods and experts is followed by an
explanation of these components and their inter-
relationships.
1.3 Formal representation of components and
changes
This section explains how the abstract
properties of data, methods and experts are
represented, and then employed to track semantic
changes as information products are produced
utilizing tools described above. From the description
in Section 2 it should be evident that such changes
are a consequence of the arrangement of data,
computational methods and expert interaction applied
to data. At an abstract level above that of the data
and methods used, we wish to represent some
characteristics of these three sets of components in a
formal sense, so that we can describe the effects
deriving from their interaction. One strong caveat
here is that our semantic description (described
below) does not claim to capture all senses of
meaning attached to data, methods or people, and in
fact as a community of researchers we are still
learning about which facets of semantics are
important and how they might be described. It is not
currently possible to represent all aspects of meaning
and knowledge within a computer, so we aim instead
to provide descriptions that are rich enough to allow
users to infer aspects of meaning that are important
for specific tasks from the visualizations or reports
that we can synthesize. In this sense our own
descriptions of semantics play the role of a
signifier—the focus is on conveying meaning to the
reader rather than explicitly carrying intrinsic
meaning per-se.
The formalization of semantics based on ontologies
and operated on using a language capable of
representing relations provides for powerful
semantic modelling (Kuhn, 2002). The framework,
rules, and facts used in the Solution Synthesis
Engine (see below) function in this way.
Relationships are established between each of the
entities, by calculating their membership within a set
of objects capable of synthesizing a solution. We
extend the approach of Kuhn by allowing the user to
narrow a search for a solution based on the specific
semantic attributes of entities. Using the minimal
spanning tree produced from the solution synthesis it
is possible to retrace the steps of the process to
calculate semantic change. As each fact is asserted it
contains information about the rule that created it (the
method) and the data and human experts that were
identified as resources required. If we are able to
describe the change to the data (in terms of abstract
semantic properties) imbued by each of the processes
through which it passes, then it is possible to
represent the change between the start state and the
finish state by differencing the two.
Although the focus of our description is on
semantics, there are good reasons for including
syntactic and schematic information about data and
methods also, since methods generally are designed
to work in limited circumstances, using and
producing very specific data types (pre-conditions
and post-conditions). Hence from a practical
perspective it makes sense to represent and reason
with these aspects in addition to semantics, since they
will limit which methods can be connected together
and dictate where additional conversion methods are
required. Additional potentially useful properties
arise when the computational and human
infrastructure is distributed e.g. around a network.
By encoding such properties we can extend our
reasoning capabilities to address problems that arise
Figure 4 – Interaction of the semantic layer and
operational layer.
5. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
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648 | P a g e
when resources must be moved from one node to
another to solve a problem.
IV. Description of data
As mentioned in Section 2, datasets are
described in general terms using a domain ontology
drawn from generic metadata descriptions. Existing
metadata descriptions hold a wealth of such practical
information that can be readily associated with
datasets; for example the FGDC (1998) defines a mix
of semantic, syntactic and schematic metadata
properties. These include basic semantics (abstract
and purpose), syntactic (data model information, and
projection), and schematic (creator, theme, temporal
and spatial extents, uncertainty, quality and lineage).
We explicitly represent and reason with a subset of
these properties in the work described here and could
easily expand to represent them all, or any other
given metadata description that can be expressed
symbolically. Formally, we represent the set of n
properties of a dataset D as: npppD ,,, 21
(Gahegan, 1996).
V. Describing Methods
While standards for metadata descriptions
are already mature and suit our purposes,
complementary mark-up languages for methods are
still in their infancy. It is straightforward to represent
the signature of a method in terms of the format of
data entering and leaving the method, and knowing
that a method requires data to be in a certain format
will cause the system to search for and insert
conversion methods automatically where they are
required. So, for example, if a coverage must be
converted from raster format to vector format before
it can be used as input to a surface flow accumulation
method, then the system can insert appropriate data
conversion methods into the evolving query tree to
connect to appropriate data resources that would
otherwise not be compatible. Similarly, if an image
classification method requires data at a nominal scale
of 1:100,000 or a pixel size of 30m, any data at finer
scales might be generalized to meet this requirement
prior to use. Although such descriptions have great
practical benefit, they say nothing about the role the
method plays or the transformation it imparts to the
data; in short they do not enable any kind of semantic
assessment to be made.
A useful approach to representing what GIS
methods do, in a conceptual sense, centers on a
typology (e.g. Albrecht‘s 20 universal GIS operators,
1994). Here, we extend this idea to address a number
of different abstract properties of a dataset, in terms
of how the method invoked changes these properties
(Pascoe & Penny, 1995; Gahegan, 1996). In a
general sense, the transformation performed by a
method (M) can be represented by pre-conditions and
post-conditions, as is common practice with interface
specification and design in software engineering.
Using the notation above, our semantic description
takes the
form:
',,','',,,: 2121 n
Operation
n pppDpppDM
, where Operation is a generic description of the role
or function the method provides, drawn from a
typology.
For example, a cartographic generalization method
changes the scale at which a dataset is most
applicable, a supervised classifier transforms an array
of numbers into a set of categorical labels, an
extrapolation method might produce a map for next
year, based on maps of the past. Clearly, there are
any number of key dimensions over which such
changes might be represented; the above examples
highlight spatial scale, conceptual ‘level’ (which at a
basic syntactic level could be viewed simply as
statistical scale) and temporal applicability, or simply
time. Others come to light following just a cursory
exploration of GIS functionality: change in spatial
extents, e.g. windowing and buffering, change in
uncertainty (very difficult in practice to quantify but
easy to show in an abstract sense that there has been a
change).
Again, we have chosen not to restrict ourselves to a
specific set of properties, but rather to remain flexible
in representing those that are important to specific
application areas or communities. We note that as
Web Services become more established in the GIS
arena, such an enhanced description of methods will
be a vital component in identifying potentially useful
functionality.
VI. Describing People
Operations may require additional
configuration or expertise in order to carry out their
task. People use their expertise to interact with data
and methods in many ways, such as gathering,
creating and interpreting data, configuring methods
and interpreting results. These activities are typically
structured around well-defined tasks where the
desired outcome is known, although as in the case of
knowledge discovery, they may sometimes be more
speculative in nature. In our work we have cast the
various skills that experts possess in terms of their
ability to help achieve some desired goal. This, in
turn, can be re-expressed as their suitability to
oversee the processing of some dataset by some
method, either by configuring parameters, supplying
judgment or even performing the task explicitly. For
example, an image interpretation method may require
identification of training examples that in turn
necessitate local field knowledge; such knowledge
can also be specified as a context of applicability
using the time, space, scale and theme parameters
that are also used to describe datasets. As such, a
given expert may be able to play a number of roles
6. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
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649 | P a g e
that are required by the operations described above,
with each role described as:
n
Operation
pppE ,,,: 21 , meaning that
expert E can provide the necessary knowledge to
perform Operation within the context of p1…, pn. So
to continue the example of image interpretation, p1…,
pn might represent (say) floristic mapping of Western
Australia, at a scale of 1:100,000 in the present day.
At the less abstract schematic level, location
parameters can also be used to express the need to
move people to different locations in order to conduct
an analysis, or to bring data and methods distributed
throughout cyberspace to the physical location of a
person.
Another possibility here, that we have not yet
implemented, is to acknowledge that a person‘s
ability to perform a task can increase as a result of
experience. So it should be possible for a system to
keep track of how much experience an expert has
accrued by working in a specific context (described
as p1…, pn). (In this case the expert expression
would also require an experience or suitability score
as described for constraint management described
below in section 3.3). We could then represent a
feedback from the analysis exercise to the user,
modifying their experience score.
1.4 Visualization of knowledge discovery
The visualization of the knowledge discovery
process utilizes a self organising graph package
(TouchGraph) written in Java. TouchGraph enables
users to interactively construct an ontology utilizing
concepts (visually represented as shapes) and
relationships (represented as links between shapes).
Each of the concepts and relationships can have
associated descriptions that give more details for each
of the entity types (data, methods, and people). A
sample of the visualization environment is shown
below in figure 5.
Touchgraph supports serialization allowing the
development of the information product to be
recorded and shared among collaborators.
Serialization is the process of storing and converting
an object into a form that can be readily reused or
transported. For example, an ontology can be
serialized and transported over the Internet. At the
other end, deserialization reconstructs the object from
the input stream. Information products described
using this tool are stored as DAML+OIL objects so
that the interrelationships between concepts can be
described semantically. The DAML+OIL architecture
was chosen as the goal of the DARPA Agent Markup
Language component (DAML) is to capture term
meanings, and thereby providing a Web ontology
language. The Ontology Interchange Language (OIL)
contains formal semantics and efficient reasoning
support, epistemological rich modeling primitives,
and a standard proposal for syntactical exchange
notations (http://www.ontoknowledge.org/oil/).
1.5 Solution Synthesis Engine
The automated tool selection process or
solution synthesis is more complex relying on domain
ontologies of the methods, data and human experts
(resources) that are usable to solve a problem. The
task of automated tool selection can be divided into a
number of phases. First is the user‘s specification of
the problem, either using a list of ontological
keywords or in their own terms which are mapped to
an underlying ontology. Second ontologies of
methods, data and human experts need to be
processed to determine which resources overlap with
the problem ontology. Third, a description of the
user‘s problem and any associated constraints is
parsed into an expert system to define rules that
describe the problem. Finally networks of resources
that satisfy the rules need to be selected and
displayed.
Defining a complete set of characteristic attributes for
real world entities (such as data, methods and human
experts) is difficult due to problems selecting
attributes that accurately describe the entity. Bishr‘s
solution of using cognitive semantics to solve this
problem, by referring to entities based on their
function, is implemented in this framework. Methods
utilize data or are utilized by human experts and are
subject to conditions regarding their use such as data
format, scale or a level of human knowledge. The
rules describe the requirements of the methods (‗if‘)
and the output(s) of the methods (‗then‘). Data and
human experts, specified by facts, are arguably more
passive and the rules of methods are applied to or by
them respectively. A set of properties governing how
rules may use them are defined for data, (e.g. format,
spatial, and temporal extents) and human experts
(e.g. roles and abilities) using an XML schema and
parsed into facts.
Figure 5 – A sample of the visualization environment.
7. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652
650 | P a g e
The first stage of the solution synthesis is the user
specification of the problem using concepts and
keywords derived from a problem ontology. The
problem ontology, derived from the methods, data
and human expert ontologies, consist of concepts
describing the intended uses of each of the resources.
This limitation was introduced to ensure the
framework had access to the necessary entities to
solve a user‘s problem. A more advanced version of
the problem specification is proposed which uses
natural language parsing to allow the user to specify a
problem. This query would then be mapped to the
problem ontology allowing the user to use their own
semantics instead of being governed by those of the
system.
The second stage of the solution synthesis process
parses the rules and facts describing relationships
between data, methods, and human experts. The
JESS rule, compare (Table 1), illustrates the
interaction between the rule (or method)
requirements and the facts (data and human experts).
Sections of the rule not essential for illustrating its
function have been removed. It is important to note
that these rules do not perform the operations
described rather they mimic the semantic change that
would accompany such an operation. The future
work section outlines the goal of running this system
in tandem with a codeless programming environment
to run the selected toolset automatically.
Table 1 - JESS Sample code
(1) defrule compare ;; compare two data sets
(2) (need-comparison_result $?)
(3) (datasource_a ?srcA)
(4) (datasource_b ?srcB)
(5) intersection_result <- (intersect ?srcA ?srcB)
(6) union_result <- (union ?srcA ?srcB)
(7) => ;; THEN
(8) (assert (comparison_result (inputA ?srcA)
(inputB ?srcB) (intersect ?intersection_result) (union
?union_result) ;; ―perform‖ the operation
With all of the resource rules defined, the missing
link is the problem to be solved using these rules. The
problem ontology is parsed into JESS to create a set
of facts. These facts form the ―goal‖ rule which
mirrors the user‘s problem specification. Each of the
facts in the ‗if‘ component of the goal rule are in the
form ‗need-method_x‘. The JESS engine now has the
requisite components for tool selection.
Utilizing backward-chaining JESS searches for rules
which satisfy the left hand side (LHS) of the rule. In
the case of dependencies (rules preceded by ―need-‖)
JESS searches for rules that satisfy the ―need-‖
request and runs them prior to running the rule
generating the request. The compare rule (above)
runs only when a previous rule requires a
comparison_result fact to be asserted in order for that
rule to be completed.
The compare rule (Table 1) has dependencies on
rules that collect data sources (used for comparisons)
and the rules that accomplish those comparisons
(intersection and union). If each of these rules can be
satisfied on the ―if‖ side of the clause, then the results
of the comparison rules are stored, together with the
data sources that were used in the comparison and the
products of the comparison. The results of the rule
―firing‖ are stored in a list that will be used to form a
minimal spanning tree for graphing [10-30].
As the engine runs, each of the rules ―needed‖ are
satisfied using backward chaining, the goal is
fulfilled, and a network of resources is constructed.
As each rule fires and populates the network a set of
criteria is added to a JESS fact describing each of the
user criteria that limits the network. Each of these
criteria is used to create a minimal spanning tree of
operations. User criteria are initially based upon the
key spatial concepts of identity, location, direction,
distance, magnitude, scale, time availability,
operation time, and semantic change.
Users specify the initial constraints, via the user
interface (figure 6) prior to the automated selection of
tools. As an example, a satellite image is required for
an interpretation task, but the only available data is
30 days old and data from the next orbit over the
region will not be available for another 8 hours. Is it
―better‖ to wait for that data to become available or is
it more crucial to achieve a solution in a shorter time
using potentially out of date data? It is possible that
the user will request a set of limiting conditions that
are too strict to permit a solution. In these cases all
possible solutions will be displayed allowing the user
to modify their constraints. The user specified
constraints are used to prune the network of resources
constructed (i.e. all possible solutions to the problem)
to a minimal spanning tree which is the solution that
satisfies all of the user‘s constraints.
VII. Results
This section presents the results of the framework‘s
Figure 6 – Interface showing constraint selection.
8. Taghi Karimi / International Journal of Engineering Research and Applications (IJERA)
ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.644-652
651 | P a g e
solution synthesis and representation of semantic
change. The results of the knowledge discovery
visualization are implicit in this discussion as that
component is used for the display of the minimal
spanning tree.
A sample problem, finding a home location with a
sunset view is used to demonstrate the solution
synthesis. In order to solve this problem, raster
(DEM) and vector (road network) data needs to be
integrated. A raster overlay, using map algebra,
followed by buffer operations is required to find
suitable locations, from height, slope and aspect data.
The raster data of potential sites needs to be
converted to a vector layer to enable a buffering
operation with vector road data. Finally a viewshed
analysis is performed to determine how much of the
landscape is visible from candidate sites.
The problem specification was simplified by hard-
coding the user requirements into a set of facts loaded
from an XML file. The user‘s problem specification
was reduced to selecting pre-defined problems from a
menu.
A user constraint of scale was set to ensure that data
used by the methods in the framework was at a
consistent scale and appropriate data layers were
selected based on their metadata and format. With the
user requirements parsed into JESS and a problem
selected, the solution engine selected the methods,
data and human experts required to solve the
problem. The solution engine constructed a set of all
possible combinations and then determined the
shortest path by summing the weighted constraints
specified by the user. Utilizing the abstract notation
from above, with methods specifying change thus:
',,','',,,: 21211 n
Operation
n pppDpppDM
, the user weights were included and summed for all
modified data sets:
',,','',...',,','' 221122111 nnnnn pupupuDpupupuD
. As a result of this process the solution set is pruned
until only the optimal solution remains (based on user
constraints).
VIII. Future Work
The ultimate goal of this project is to
integrate the problem solving environment with the
codeless programming environment GEOVISTA
Studio currently under development at Pennsylvania
State University. The possibility of supplying data to
the framework and determining the types of questions
which could be answered with it is also an interesting
problem.
A final goal is the use of natural language parsing of
the user‘s problem specification.
IX. Conclusions
This paper outlined a framework for
representing, manipulating and reasoning with
geographic semantics. The framework enables
visualizing knowledge discovery, automating tool
selection for user defined geographic problem
solving, and evaluating semantic change in
knowledge discovery environments. A minimal
spanning tree representing the optimal (least cost)
solution was extracted from this graph, and can be
displayed in real-time. The semantic change(s) that
result from the interaction of data, methods and
people contained within the resulting tree represents
the formation history of each new information
product (such as a map or overlay) and can be stored,
indexed and searched as required.
X. Acknowledgements
Our thanks go to Sachin Oswal, who helped
with the customization of the TouchGraph concept
visualization tool used here. This work is partly
funded by NSF grants: ITR (BCS)-0219025 and ITR
Geosciences Network (GEON).
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652 | P a g e
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