Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
An approach for transforming of relational databases to owl ontologyIJwest
Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches based on transforming relational database to ontology are proposed. In this paper we propose a method for automatic ontology construction based on relational database. Mining and obtaining further components from relational database leads to obtain knowledge with high semantic power and more expressiveness. Triggers are one of the database components which could be transformed to the ontology model and increase the amount of power and expressiveness of knowledge by presenting part of the knowledge dynamically.
AUTOMATIC CONVERSION OF RELATIONAL DATABASES INTO ONTOLOGIES: A COMPARATIVE A...IJwest
Constructing ontologies from relational databases is an active research topic in the Semantic Web domain.
While conceptual mapping rules/principles of relational databases and ontology structures are being
proposed, several software modules or plug-ins are being developed to enable the automatic conversion of
relational databases into ontologies. However, the correlation between the resulting ontologies built
automatically with plug-ins from relational databases and the database-toontology mapping principles has
been given little attention. This study reviews and applies two Protégé plug-ins, namely, DataMaster and
OntoBase to automatically construct ontologies from a relational database. The resulting ontologies are
further analysed to match their structures against the database-to-ontology mapping principles. A
comparative analysis of the matching results reveals that OntoBase outperforms DataMaster in applying
the database-to-ontology mapping principles for automatically converting relational databases into
ontologies
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
An approach for transforming of relational databases to owl ontologyIJwest
Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches based on transforming relational database to ontology are proposed. In this paper we propose a method for automatic ontology construction based on relational database. Mining and obtaining further components from relational database leads to obtain knowledge with high semantic power and more expressiveness. Triggers are one of the database components which could be transformed to the ontology model and increase the amount of power and expressiveness of knowledge by presenting part of the knowledge dynamically.
AUTOMATIC CONVERSION OF RELATIONAL DATABASES INTO ONTOLOGIES: A COMPARATIVE A...IJwest
Constructing ontologies from relational databases is an active research topic in the Semantic Web domain.
While conceptual mapping rules/principles of relational databases and ontology structures are being
proposed, several software modules or plug-ins are being developed to enable the automatic conversion of
relational databases into ontologies. However, the correlation between the resulting ontologies built
automatically with plug-ins from relational databases and the database-toontology mapping principles has
been given little attention. This study reviews and applies two Protégé plug-ins, namely, DataMaster and
OntoBase to automatically construct ontologies from a relational database. The resulting ontologies are
further analysed to match their structures against the database-to-ontology mapping principles. A
comparative analysis of the matching results reveals that OntoBase outperforms DataMaster in applying
the database-to-ontology mapping principles for automatically converting relational databases into
ontologies
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
An Approach to Owl Concept Extraction and Integration Across Multiple Ontolog...dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for
the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be
applied for making the process of ontology development through reuse a viable option for ontology
developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are
reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic
Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on
cost as well as development life as ontology engineering requires expert domain skills and is time taking
process. We have devised a framework to address challenges associated with reusing ontologies from the
Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across
multiple ontologies.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASEScscpconf
Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Schema mapping is a technique that is used by all existing approaches for ontology building from RDB.However, most of those methods use poor transformation rules that prevent advanced database mining for building rich ontologies. In this paper, we propose transformation rules for building owl ontologies from RDBs. It allows transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by analyzing stored data to detect disjointness and
totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied to any RDB. The
proposed rules were evaluated using a normalized and open RDB. The obtained ontology is richer in terms of non- taxonomic relationships.
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
Clustering of Deep WebPages: A Comparative Studyijcsit
The internethas massive amount of information. This information is stored in the form of zillions of
webpages. The information that can be retrieved by search engines is huge, and this information constitutes
the ‘surface web’.But the remaining information, which is not indexed by search engines – the ‘deep web’,
is much bigger in size than the ‘surface web’, and remains unexploited yet.
Several machine learning techniques have been commonly employed to access deep web content. Under
machine learning, topic models provide a simple way to analyze large volumes of unlabeled text. A ‘topic’is
a cluster of words that frequently occur together and topic models can connect words with similar
meanings and distinguish between words with multiple meanings. In this paper, we cluster deep web
databases employing several methods, and then perform a comparative study. In the first method, we apply
Latent Semantic Analysis (LSA) over the dataset. In the second method, we use a generative probabilistic
model called Latent Dirichlet Allocation(LDA) for modeling content representative of deep web
databases.Both these techniques are implemented after preprocessing the set of web pages to extract page
contents and form contents.Further, we propose another version of Latent Dirichlet Allocation (LDA) to the
dataset. Experimental results show that the proposed method outperforms the existing clustering methods.
USING RELATIONAL MODEL TO STORE OWL ONTOLOGIES AND FACTScsandit
The storing and the processing of OWL instances are important subjects in database modeling.
Many research works have focused on the way of managing OWL instances efficiently. Some
systems store and manage OWL instances using relational models to ensure their persistence.
Nevertheless, several approaches keep only RDF triplets as instances in relational tables
explicitly, and the manner of structuring instances as graph and keeping links between concepts
is not taken into account. In this paper, we propose an architecture that permits relational
tables behave as an OWL model by adapting relational tables to OWL instances and an OWL
hierarchy structure. Therefore, two kinds of tables are used: facts or instances relational tables.
The tables hold instances and the OWL table holds a specification of how the concepts are
structured. Instances tables should conform to OWLtable to be valid. A mechanism of
construction of OWLtable and instances tables is defined in order to enable and enhance
inference and semantic querying of OWL in relational model context.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Facial Feature Recognition Using Biometricsijbuiiir1
Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. Biometric requires no physical interaction on behalf of the user. Biometric allows to perform passive identification in a one to many environments. Passwords and PINs are hard to remember and can be stolen or guessed; cards, tokens, keys and the like can be misplaced, forgotten, purloined or duplicated; magnetic cards can become corrupted and unreadable. However individuals biological traits cannot be misplaced, forgotten, stolen or forged.
Partial Image Retrieval Systems in Luminance and Color Invariants : An Empiri...ijbuiiir1
Color of the surface is one of the most imperative characteristics in the process of recognition as well as classification of the object which is based on camera. On the other hand, color of the object sometimes differs a lot due to the difference in illumination as well as the conditions of the surface. Utilization of the diverse features of the color gets impeded due to such variations. However, Characterization of the color of the object is possible with a controlling tool known as color invariants without considering the factors such asillumination and conditions of the surface. In the research proposal, analysis has been done on the estimation procedure related to RGB images color invariants. Object color is an imperative descriptor that can find the corresponding matching object in applications based on image matching as well as search, like- Object searching based on template and CBIR otherwise known as Content Based Image Retrieval. But, many times it has been observed that the apparent color of different objects gets varied significantly due to illumination, conditions of the surface as well as observation (Finlayson et al., 1996)
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Similar to Towards Ontology Development Based on Relational Database
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
An Approach to Owl Concept Extraction and Integration Across Multiple Ontolog...dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for
the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be
applied for making the process of ontology development through reuse a viable option for ontology
developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are
reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic
Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on
cost as well as development life as ontology engineering requires expert domain skills and is time taking
process. We have devised a framework to address challenges associated with reusing ontologies from the
Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across
multiple ontologies.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
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. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASEScscpconf
Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Schema mapping is a technique that is used by all existing approaches for ontology building from RDB.However, most of those methods use poor transformation rules that prevent advanced database mining for building rich ontologies. In this paper, we propose transformation rules for building owl ontologies from RDBs. It allows transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by analyzing stored data to detect disjointness and
totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied to any RDB. The
proposed rules were evaluated using a normalized and open RDB. The obtained ontology is richer in terms of non- taxonomic relationships.
Novel Database-Centric Framework for Incremental Information Extractionijsrd.com
Information extraction (IE) has been an active research area that seeks techniques to uncover information from a large collection of text. IE is the task of automatically extracting structured information from unstructured and/or semi structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in document processing like automatic annotation and content extraction could be seen as information extraction. Many applications call for methods to enable automatic extraction of structured information from unstructured natural language text. Due to the inherent challenges of natural language processing, most of the existing methods for information extraction from text tend to be domain specific. In this project a new paradigm for information extraction. In this extraction framework, intermediate output of each text processing component is stored so that only the improved component has to be deployed to the entire corpus. Extraction is then performed on both the previously processed data from the unchanged components as well as the updated data generated by the improved component. Performing such kind of incremental extraction can result in a tremendous reduction of processing time and there is a mechanism to generate extraction queries from both labeled and unlabeled data. Query generation is critical so that casual users can specify their information needs without learning the query language.
Clustering of Deep WebPages: A Comparative Studyijcsit
The internethas massive amount of information. This information is stored in the form of zillions of
webpages. The information that can be retrieved by search engines is huge, and this information constitutes
the ‘surface web’.But the remaining information, which is not indexed by search engines – the ‘deep web’,
is much bigger in size than the ‘surface web’, and remains unexploited yet.
Several machine learning techniques have been commonly employed to access deep web content. Under
machine learning, topic models provide a simple way to analyze large volumes of unlabeled text. A ‘topic’is
a cluster of words that frequently occur together and topic models can connect words with similar
meanings and distinguish between words with multiple meanings. In this paper, we cluster deep web
databases employing several methods, and then perform a comparative study. In the first method, we apply
Latent Semantic Analysis (LSA) over the dataset. In the second method, we use a generative probabilistic
model called Latent Dirichlet Allocation(LDA) for modeling content representative of deep web
databases.Both these techniques are implemented after preprocessing the set of web pages to extract page
contents and form contents.Further, we propose another version of Latent Dirichlet Allocation (LDA) to the
dataset. Experimental results show that the proposed method outperforms the existing clustering methods.
USING RELATIONAL MODEL TO STORE OWL ONTOLOGIES AND FACTScsandit
The storing and the processing of OWL instances are important subjects in database modeling.
Many research works have focused on the way of managing OWL instances efficiently. Some
systems store and manage OWL instances using relational models to ensure their persistence.
Nevertheless, several approaches keep only RDF triplets as instances in relational tables
explicitly, and the manner of structuring instances as graph and keeping links between concepts
is not taken into account. In this paper, we propose an architecture that permits relational
tables behave as an OWL model by adapting relational tables to OWL instances and an OWL
hierarchy structure. Therefore, two kinds of tables are used: facts or instances relational tables.
The tables hold instances and the OWL table holds a specification of how the concepts are
structured. Instances tables should conform to OWLtable to be valid. A mechanism of
construction of OWLtable and instances tables is defined in order to enable and enhance
inference and semantic querying of OWL in relational model context.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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Today, Cybercrime has caused lot of damages to individuals, organizations and even the Government. Cybercrime detection methods and classification methods have came up with varying levels of success for preventing and protecting data from such attacks. Several laws and methods have been introduced in order to prevent cybercrime and the penalties are laid down to the criminals. However, the study shows that there are many countries facing this problem even today and United States of America is leading with maximum damage due to the cybercrimes over the years. According to the recent survey carried out it was noticed that year 2013 saw the monetary damage of nearly 781.84 million U.S. dollars. This paper describes about the common areas where cybercrime usually occurs and the different types of cybercrimes that are committed today. The paper also shows the studies made on e-mail related crimes as email is the most common medium through which the cybercrimes occur. In addition, some of the case studies related to cybercrimes are also laid down
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The content downloading is internet based service and the expectation of this services are highly popular in wireless communication it will be supporting for road side communication. We are focusing in content downloading system for both infrastructure-to-vehicle and vehicle-to-vehicle communication. The goal to improving system throughput and formulating a max-flow problem including the channel contention and data transfer paradigm. A system communication while transferring the files or downloading some application in road side environment there is the possibility of getting disconnected. The purpose of this study used to avoid the in conventional connection at the road side environment while using system or mobile based internet connection used for content or file downloader using MILP(Mixed Integer Linear programming) for max flow problem. The bounding box technique will be used to get the proper signal from base station. To avoid the traffic and access the quick response from the server the bounding box will used. The mail goal of the mobility management service is to trace the location where the subscribers are, allowing calls, SMS and other mobile phone services to be delivered to them. First we can analysing the data and select for correct location.. It will be provide challenging in vehicular networks, that is the transmission speed of the nodes will even more efficient though the area surrounded of buildings and many other architectural infrastructures of the radio signal.
SPOC: A Secure and Private-pressuring Opportunity Computing Framework for Mob...ijbuiiir1
Today we have an abundant increase in the development of Information and Technology, which inturn made the Humans body even to carry a Mini- Computer in their Palms with Screen touch, Ex: Smart phone�s & Tablets etc., and parallel with the rich Enhancement in the Wireless Body Sensor Units, it is quite useful to the Enrichment of the Medical Treatment to be perfectly useful, comfortable via Smart Phones Using the networks (2G & 3G) carriers and made the treatment very easy even to the Common person in the society with the low cost money. With these the healthcare Authorities can treat the Patients (medical users) remotely where the patients reside at home or company or school or college or anywhere or at various places they work. This type of a treatment called for MHealthcare (Mobile- Healthcare). Although in the mhealthcare service there are many security and data Private problems to be overcome. Here we have A Secure and Private- Preserving Opportunistic Computing Framework called M-HealthCare, for Mobile-Healthcare Emergency. Using the Smart phone and SPOC, the Software or Hardware like computing power and energy can be gathered opportunistically to process the intensive Personal Health Information (PHI) of the medical user when he/she is in critical situation with minimal Private Disclosure. And also we introduce an efficient usercentric Private access control in SPOC Framework which is based on attribute access control and a new privatepreserving scalar product computation (PPSPC) technique and Makes a medical user (patient) to participate in opportunistic computing in transmitting his PHI data. Elaborated security analysis describes that the proposed SPOC framework can efficiently achieve usercentric Private access control in M-Healthcare emergency. In this paper we introduce Private-Preserving Support for Mobile Healthcare using Message Digest where we have used MD5 algorithm ,which can certainly achieves an efficient way and minimizes the memory consumed and the large amount of PHI data of the medical user (patient) is reduced to a fixed amount of size compared to AES which parallels increases the Speed of the data to be sent to TA without any delay which in-turn the professionals at Healthcare center can get exactly the Recent tablet user PHI data and can save their lives in correct time. As the algorithm is provided tight security in transmitting the patients PHI to TA. In respective performance evaluations with extensive simulations explains the MD (message digest) effectiveness in-term of providing high-reliable Personal Health Information (PHI) process and transmission while reducing the Private disclosure during Mobile-Healthcare emergency
A Survey on Implementation of Discrete Wavelet Transform for Image Denoisingijbuiiir1
Image Denoising has been a well studied problem in the field of image processing. Images are often received in defective conditions due to poor scanning and transmitting devices. Consequently, it creates problems for the subsequent process to read and understand such images. Removing noise from the original signal is still a challenging problem for researchers because noise removal introduces artifacts and causes blurring of the images. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper deals with using discrete wavelet transform derived features used for digital image texture analysis to denoise an image even in the presence of very high ratio of noise. Image Denoising is devised as a regression problem between the noise and signals, therefore, Wavelets appear to be a suitable tool for this task, because they allow analysis of images at various levels of resolution.
A Study on migrated Students and their Well - being using Amartya Sens Functi...ijbuiiir1
This paper deals with the multidimensional analysis of well-being from the theoretical point of views suggested by Dr. Amartya Sen. Sens Functioning Multidimensional Approach is broadly recognized as one of the most satisfying approaches to well-being. The Capability approach and the Functioning approach of Sen have found relatively many pragmatic applications mainly for its strong informational and methodological requirements. An attempt has been made to realize a multidimensional assessment of Sens concept of wellbeing with the use of the Fuzzy Set theory. The methodology is applied to the evaluative space of Functionings, with an experimental application to migrated students studying in Chennai, Tamil Nadu.
Methodologies on user Behavior Analysis and Future Request Prediction in Web ...ijbuiiir1
Web Usage Mining is a kind of web mining which provides knowledge about user navigation behavior and gets the interesting patterns from web. Web usage mining refers to the mechanical invention and scrutiny of patterns in click stream and linked data treated as a consequence of user interactions with web resources on one or more web sites. Identify the need and interest of the user and its useful for upgrade web Sources. Web site developers they can update their web site according to their attention. In this paper discuss about the different types of Methodologies which has been carried out in previous research work for Discovering User Behavior and Predicting the Future Request.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Enhancing Effective Interoperability Between Mobile Apps Using LCIM Modelijbuiiir1
Levels of conceptual interoperability model is used to develop the method and model towards enhancing interoperability among mobile apps. The LCIM is used as descriptive and prescriptive form and it also make available of both metric of the degree of conceptual representation that exists between interoperating systems. In descriptive form LCIM is used to decrease the discrepancies in rating mobile apps based on content by suggesting a rating system that is completely based on interoperability. In the prescriptive form it receives information for app development, which allows producing apps with prominent level of interoperability. The Levels of Conceptual Interoperability has the abstract backbone for developing and implementing an interoperability framework that supports to exchange of XML based languages used by M&S systems across the web
Deployment of Intelligent Transport Systems Based on User Mobility to be Endo...ijbuiiir1
The emerging increase in vehicles and very high traffic, demands the need for improved Intelligent Transport Systems (ITS). The available ITSs do not meet all the requirements of the present day situation in providing safetravels and avoidance of congestionin spite of its limitations on road. Intelligent Transport Systemsrequiremore research and implementation of better solutions on the traffic network with increased mobility and more rapid acquisition of data by sense network technology. In this paper a review is made on the present ITS where research is required so that improvement in the course of implementing reality mining can enhance the behavior of ITS. This will breed a forward leap in the improvement of safety and convenience of personal and commercial travel and in turn guarantee an ultimate drop in fatality in the society
Stock Prediction Using Artificial Neural Networksijbuiiir1
Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. In this paper ANN modeling of stock prices of selected stocks under BSE is attempted to predict closing prices. The network developed consists of an input layer, one hidden layer and an output layer and the inputs being opening price, high, low, closing price and volume. Mean Absolute Percentage Error, Mean Absolute Deviation and Root Mean Square Error are used as indicators of performance of the networks. This paper is organized as follows. In the first section, the adaptability of ANN in stock prediction is discussed. In section two, we justify the using of ANNs in forecasting stock prices. Section three gives the literature review on the applications of ANNs in predicting the stock prices. Section four gives an overview of artificial neural networks. Section five presents the methodology adopted. Section six gives the simulation and performance analysis. Last section concludes with future direction of the study
Indian Language Text Representation and Categorization Using Supervised Learn...ijbuiiir1
India is the home of different languages, due to its cultural and geographical diversity. The official and regional languages of India play an important role in communication among the people living in the country. In the Constitution of India, a provision is made for each of the Indian states to choose their own official language for communicating at the state level for official purpose. In the eighth schedule as of May 2008, there are 22 official languages in India.The availability of constantly increasing amount of textual data of various Indian regional languages in electronic form has accelerated. So the Classification of text documents based on languages is essential. The objective of the work is the representation and categorization of Indian language text documents using text mining techniques. South Indian language corpus such as Kannada, Tamil and Telugu language corpus, has been created. Several text mining techniques such as naive Bayes classifier, k-Nearest-Neighbor classifier and decision tree for text categorization have been used.There is not much work done in text categorization in Indian languages. Text categorization in Indian languages is challenging as Indian languages are very rich in morphology. In this paper an attempt has been made to categories Indian language text using text mining algorithms
Highly Secured Online Voting System (OVS) Over Networkijbuiiir1
Internet voting systems have gained popularity and have been used for government elections and referendums in the United Kingdom, Estonia and Switzerland as well as municipal elections in Canada and party primary elections in the United States. Voting system can involve transmission of ballots and votes via private computer networks or the Internet. Electronic voting technology can speed the counting of ballots and can provide improved accessibility for disabled voters. The aim of this paper is to people who have citizenship of India and whose age is above 18 years and of any sex can give their vote through online without going to any physical polling station. Election Commission Officer (Election Commission Officer who will verify whether registered user and candidates are authentic or not) to participate in online voting. This online voting system is highly secured, and its design is very simple, ease of use and also reliable. The proposed software is developed and tested to work on Ethernet and allows online voting. It also creates and manages voting and an election detail as all the users must login by user name and password and click on his favorable candidates to register vote. This will increase the voting percentage in India. By applying high security it will reduce false votes.
Software Developers Performance relationship with Cognitive Load Using Statis...ijbuiiir1
The success of the software development highly appreciated with the intellectual capital instead of physical assets of the concern. Human resource is the challenging resource in the software development industry to meet the customer requirement and deliver the project on time to the client. The software industry requires multi-skill and dynamic performers to meet the challenges. The skills domain knowledge and the developer�s performance are considered as the potential key factors for the success of the delivery of projects. The developer�s performance is influenced with cognitive factors and its measures. This study aimed to relate developer�s performance in the software industry with his/her cognitive workload. The various statistical measures like correlation, regression, variance and standard deviations are to be calculated for the developer�s performance with the cognitive load. The real-time development sector observations made of around 250 employees, 15 projects work and its corresponding cognitive load such as physical ability, mental ability, temporal ability, effort, frustration and performance in Web application, Database application and Multimedia. This paper provides the measurable analysis of the development process of the developers with their assigned task using statistical approach
Wireless Health Monitoring System Using ZigBeeijbuiiir1
Recent developments in off-the-shelf wireless embedded computing boards and the increasing need for efficient health monitoring systems, fueled by the increasing number of patients, has prompted R&D professionals to explore better health monitoring systems that are both mobile and cheap. This work investigates the feasibility of using the ZigBee embedded technology in health-related monitoring applications. Selected vital signs of patients are acquired using sensor nodes and readings are transmitted wirelessly using devices that utilize the ZigBee communications protocols. A prototype system has been developed and tested with encouraging results
Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transformijbuiiir1
This research paper presents a proposed method for the compression of medical images using hybrid compression technique (DWT, DCT and Huffman coding). The objective of this hybrid scheme is to achieve higher compression rates by first applying DWT and DCT on individual components RGB. After applying this image is quantized to calculate probability index for each unique quantity so as to find out the unique binary code for each unique symbol for their encoding. Finally the Huffman compression is applied. Results show that the coding performance can be significantly improved by the hybrid DWT, DCT and Huffman coding algorithm
Agile development methodologies are very promising in the software industry. Agile development techniques are very realistic n understanding the fact that requirement in a business environment changes constantly. Agile development processes optimize the opportunity provided by cloud computing by doing software release iteratively and getting user feedback more frequently. The research work, a study on Agile Methods and cloud computing. This paper analyzes the Agile Management and development methods and its benefits with cloud computing. Combining agile development methodology with cloud computing brings the best of both worlds. A business strategy, the outcomes of which optimize profitability revenue and customer satisfaction by organizing around customer segments, fostering customer-satisfying behaviors, and implementing customer-centric processes
SOA is becoming important for Business Process Management and Enterprises. Now SOA is widely used by Enterprises as it provides seamless environment, flexibility, interoperability, but at the same time security should also consider because the basic SOA framework doesn�t possess any security. It depends upon the respective proprietor for security [1]. In recent times many research work had done for SOA security. Researchers have also proposed various frameworks and models such as FIX [2], SAVT [3] which tries a lot, but cannot achieve any landmark as they are based on XML schema.This proposed novel work contains an inbuilt security module which was based on PKI. At the same time this model will intact the flexibility and interoperability as the security module is embedded by analyzing the nature of WSDL, UDDI, SOAP and XML. These protocols are also compatible with PKI. Proposed Model was implemented in the asp.net environment then experimental results are compared with other security methods such as data mining based web security and automata based web security
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Towards Ontology Development Based on Relational Database
1. Integrated Intelligent Research (IIR) International Journal of Web Technology
Volume: 01 Issue: 02 December 2012 Page No.57-60
ISSN: 2278-2389
57
Towards Ontology Development Based on
Relational Database
L. Ravi, N .Sivaranjini
Department of Computer Science, Sacred Heart College (Autonomous), Tirupattur.
Email:raviatshc@yahoo.com, ssk.siva4@gmail.com
Abstract- Ontology is defined as the formal explicit specification
of a shared conceptualization. It has been widely used in almost
all fields especially artificial intelligence, data mining, and
semantic web etc. It is constructed using various set of resources.
Now it has become a very important task to improve the
efficiency of ontology construction. In order to improve the
efficiency, need an automated method of building ontology from
database resource. Since manual construction is found to be
erroneous and not up to the expectation, automatic construction
of ontology from database is innovated. Then the construction
rules for ontology building from relational data sources are put
forward. Finally, ontology for “automated building of ontology
from relational data sources” has been implemented.
Keywords: Schema, Generation, Ontology, Database
I. INTRODUCTION
Ontology provides a shared and reusable piece of knowledge
about a specific domain, and has been applied in many fields,
such as Semantic Web, e-commerce and information retrieval,
etc. However, building ontology by hand is a very hard and
error-prone task. Loading ontology from existing resources is a
good solution. Because relational database is widely used for
storing data and OWL is the latest standard recommended by
W3C. This paper proposes an approach of loading OWL
ontology from data in relational database. Compared with
existing methods, the approach can acquire ontology from
relational database automatically by using a group of learning
rules instead of using a middle model. In addition, it can obtain
OWL ontology, including the classes, properties, properties
characteristics, cardinality and instances, while none of existing
methods can acquires all of them. Thus an automatic generation
of ontology from database is designed in this paper.The main aim
of this paper is to build ontology automatically using the
relational database resources to improve the efficiency is
proposed to achieve efficient interoperability of information
systems. This paper includes the related work of mappings
between relational databases and building OWL ontology based
on the relational database. The relational schema is mapped to
ontology through the analysis of the relations among primary
key, attributes. Then, the relational data is mapped to ontology
instances. Since the semantic in relational model is very limited,
these methods can be used only to build lightweight ontology. In
this work, the mapping rules between relational database
elements and ontology are proposed based on existing ontology
construction method. Manually correcting the logical
inconsistencies in the first version of the OWL ontology; making
use of foundational ontologies. The conversion of these
databases into RDF/OWL format is an important step towards
realizing the benefits of Semantic Web research. Based on this
manually created basic ontology, the data from the databases
were then automatically converted to OWL using programs
written in Java and Python. The automated export scripts
extended the manually created basic ontology through the
creation of subclasses, OWL property restrictions and
individuals. The resulting ontologies show no clearly
distinguishable division between a schema and data. This
document describes representation of the databases in Resource
Description Framework (RDF) and Web Ontology Language
(OWL), and discusses the advantages and disadvantages of these
representations.
II. AUTOMATIC GENERATION OF ONTOLOGY FROM
DATABASE SCHEMAS
Initially a mapping between attributes and database is made. That
mapping process starts by detecting particular cases for
conceptual elements in the database and accordingly converts
database components to the corresponding ontology components.
A. Ontology for Database Design
The method used to develop the ontology is outlined and the
components and representation issues discussed. The research is
intended to use existing libraries of ontology’s as they are
developed. As mentioned above, there has been much call for the
development of ontology’s for different application domains that
can be shared amongst different applications and interest groups.
Although the actual development of ontology in are not the focus
of this research, the following outlines the procedure followed
for the manual, but systematic, development of the ontologes
used in this research.
B. Components and Development
The main components of ontologies are terms, relationships, and
axioms. The relationships that typically appear in an ontology are
subclass-of (is_a), instance-of, synonym, and related-to. This
research extends these main concepts to include domain
relationships that capture additional constraints needed for
database design and which represent, to some extent, semantic
integrity constraints. There additional constraints capture various
aspects of a business application.
C. Ontology Components and Representation
In ontology development, concepts or terms are often organized
into taxonomies with binary relationships. Most ontology’s focus
heavily on terms, often organized as a class and subclasses. To a
much lesser extent, instances of terms are included. Representing
and reasoning with these basic components is not enough to
create heavyweight ontology capable of supporting complex
reasoning. Therefore, before implementing ontology, one should
2. Integrated Intelligent Research (IIR) International Journal of Web Technology
Volume: 01 Issue: 02 December 2012 Page No.57-60
ISSN: 2278-2389
58
decide on the application needs in terms of expressiveness and
inference.This research extends the relationship component of
ontology. For conceptual modelling, the basic relationships
(synonym, is_a, and related_to) employed in an ontology are
limited because of their inability to capture domain specific
knowledge (business rules). We model other types of
relationships, which we call domain relationships, the purpose of
which is to define and enforce business rules.
The following four types of domain relationships:
Pre-requisite
Mutually-inclusive
Mutually-exclusive
Temporal
Figure 1. Partial Auction Domain Ontology
D. Ontology-based approach for Database design
The two primary ways in which an ontology could be used to
improve a design are:
1. Conceptual model generation
2. Conceptual model validation
The role of an ontology is to provide a comprehensive set of
terms, definitions, and relationships for the domain for which a
conceptual model is to be created. Therefore, the first task is to
generate a design “from scratch,” using the terms and
relationships in the ontology as representative of the domain. The
second task is to use the ontology to check for missing terms or
inconsistencies in an existing, or partial, design.
III. IMPLEMENTATION
Most conceptual data models of information systems are created
from scratch, wasting time and resources. Ontology represents
the real-world domain knowledge. So ontology can be reused in
conceptual model building. However ontology engineering is not
mature enough. In this paper the new approach is proposed to
develop Automatic generation of Ontology from Relational
Database (AGOFRD). The ontology can be evaluated, extended
and reused as domain knowledge for other conceptual data
models.
A. AGOFRD Implementation
Initially the structure of AGOFRD can be divided in to three
parts based on the above rules and the construction process of
ontology.
1. Database metadata reading
2. Ontology meta-model construction and
3. Goal ontology generation
B. Database Metadata Reading
A metadata is nothing but the data about a data. So metadata
reading is defined as extraction of data or information from
relational databases to establish a relational database model. In
order to link database with ontology, a tool called “Data master is
used”. This links up with the backend databases and retrieves
information stored in the tables.
C. Ontology Meta Model Construction
By analyzing the relational database metadata model, the
relational database metadata model is converted in to ontology
meta-model. Conversion is done based on the above proposed
rules and definitions. The main content of ontology meta-model
construction is to construct an ontology meta-model Graph
Model, which consists of the set of nodes (defining a node
contains the concept name and property) and the set of edges
(defining an edge as the connection of two concepts).
D. Goal Ontology Generation
Extracting ontology information from above ontology meta-
model, it maps the database data into ontology instances
according to the data conversion steps from relational database to
OWL ontology. Finally, the completed OWL document is
generated. Then, the ontology generated is opened and verified it
in ontology tool Protégé.
E. Method of Mapping Protege to Database
The Ontology is mapped with Relational databases using the tool
called Data Master. Initially a database is created in Microsoft
Access with extension of (*.accdb). Then an ODBC data source
for that particular Database is created.
F. ODBC Data Source Creation
It’s a simple process, in which a data source name and the path
where the database is stored is given as input. Then the path of
data source newly created is given as an input to the Data Master
of protégé 3.3.1. A ODBC Data Source is linked with Data
Master as shown in below figure2.
Figure 2. ODBC Mapping with Ontology
After giving all necessary fields, connect button is pressed. So
this will link with the particular database present in data source
path given and retrieve all the fields present in the database. This
will get mapped automatically based on the above mentioned
3. Integrated Intelligent Research (IIR) International Journal of Web Technology
Volume: 01 Issue: 02 December 2012 Page No.57-60
ISSN: 2278-2389
59
rules. It has two options basically for ODBC and JDBC. It is left
to user’s choice to use any one of them.
G. Datamaster Plug-in for Importing Schemas from Relational
Databases
There are a variety of ways of describing the schema of a
database in ontology, depending on the requirements of an
application. For example, some applications will only require an
import of the database content without needing a “live”
connection to the database. In other cases, the mapping between
the database structure and the ontology elements is more
important, so that data reside in the database but are accessible to
querying or reasoning through an ontology layer.
IV. SCHEMA STRUCTURE ONTOLOGY
A. Schema Structure Ontology for Protégé-Frames
DataMaster may be used to import a relational database structure
and the table data into Protégé-Frames ontology. The ontology
for describing the database structure (Figure 5.4.) is the same as
the one used by the DataGenie plug-in. All imported database
tables are defined as Protégé classes that are instances of the
Table Metaclass meta-class. Each column of the database table is
represented by a template slot added to the newly created table
classes. The column slots will have data types corresponding to
the SQL types associated to the database columns.
Figure 3. Database schema ontology for Protégé
Frames
If there are foreign keys defined between the database tables, for
each foreign key an instance of the Foreign Key class will be
created that will be used to link the corresponding ontology
classes. It is also possible to import the data from the tables in the
database. For each row in the table an instance of the table class
will be created and the values of the own slots at these instances
will be set with values contained in the table row corresponding
to the table columns. An extra slot of type instance will be
created for each foreign key defined in the table that will point to
the instances corresponding to the referred rows.
B. Schema Structure Ontology for Database tables as OWL
Classes
One of our goals when designing the schema structure ontology
in OWL was to be able to use DL reasoning on it. This means
that certain constructs used in the Frames approach had to be
changed. However, certain combination of import options will
result in OWL Full schema ontology. The Protégé OWL schema
ontology for importing tables as classes is the OWL version of
the previously described ontology for Protégé Frames. There are
only a few differences: The imported classes will not be instance
of a common meta-class, because it would make the ontology
OWL Full. Instead, all the template slots attached to Table
Metaclass in the Frames ontology have been defined as
annotation properties on the imported table classes.The property
and class names from the Frames ontology are using the camel
notation and the space character in the class and property names
have been avoided. E.g., hasForeignKeys instead of Foreign
Keys, Foreign Key instead of Foreign Key, etc.For easier
navigation and performance reasons, we have introduced four
additional object properties attached to the Foreign Key class to
refer the local and referenced table classes as well as the
referring and referred column properties.
C. Schema Structure ontology for database Tables as OWL
Instances using Relational OWL
Another way of representing the structure of a database in OWL
ontology is the approach taken by Relational.OWL. The ontology
used by Relational.OWL is shown in Figure 5.5. It defines four
classes Database, Table, Column and PrimaryKey. The instances
of these classes and their relationships can represent the schema
structure of any relational database. One of the available
configurations of DataMaster is to import the database structure
as instances of the Relational.OWL ontology. However, the
Relational.OWL ontology is OWL Full, because
Relational.OWL defines the representation of the database
column types by using the rdfs:range property on the Column
class, which makes the Column class be an owl:Class and an
rdf:Property at the same time.
IV. RESULTS AND DIRECTIONS
Open the main interface, configure the relevant information
which is needed for database connection, select the save path for
ontology. Then, input the database configuration information,
connect to the database. The OWL ontology is generated using
DataMaster plugin automatically. Database to ontology mapping
correspondences between database components (table, column,
constraint) and ontological components (concept, property).
Database to ontology mapping complex direct data migration
query driven massive dump mapping database to already existing
ontology creating ontology from database mapping definition
mapping definition
Figure 5. Example ontology from relational database
4. Integrated Intelligent Research (IIR) International Journal of Web Technology
Volume: 01 Issue: 02 December 2012 Page No.57-60
ISSN: 2278-2389
60
V. CONCLUSION
Aiming to find a method for automatic generate ontology from
relational database resources to improve the efficiency of
ontology construction. An approach of loading OWL ontology
from data in relational database Construction rules of ontology
elements based on relational database, which are used to generate
ontology concepts, properties, axioms, instances are put
forward.An ontology automatic generation system based on
relational database is designed and implemented according to the
construction rules. Mapping automatically based on the above
mentioned rules based on two options basically for ODBC and
JDBC. This research is an initial step to illustrate how domain
ontology’s, which capture knowledge about specific application
domains, can be used for the creation and validation of entity-
relationship models for conceptual modeling. This research
extends the main concepts of synonym and relations to include
domain relationships that capture additional constraints needed
for database design and which represent, to some extent,
semantic integrity constraints. Ontology generated based on the
construction rules of ontology concepts, instances, properties,
axioms in future ontology is generated based on other attribute
relations.
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