ESSA-2011 poster - Netlogo wiki systems modeling Evgeny Patarakin
The paper discusses a wiki-based network communities multi-agent research model. The cases for the model application include a Russian educational wiki portal letopisi.ru and a wiki platform for drafting the Russian Law on education.
A generalized flow based method for analysis of implicit relationships on wik...ecway
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ESSA-2011 poster - Netlogo wiki systems modeling Evgeny Patarakin
The paper discusses a wiki-based network communities multi-agent research model. The cases for the model application include a Russian educational wiki portal letopisi.ru and a wiki platform for drafting the Russian Law on education.
A generalized flow based method for analysis of implicit relationships on wik...ecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
Android a generalized flow-based method for analysis of implicit relationshi...ecway
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Abstract— Relationships are there between objects in Wikipedia. Emphases on determining relationships are there between pairs of objects in Wikipedia whose pages can be regarded as separate objects. Two classes of relationships between two objects exist there in Wikipedia, an explicit relationship is illustrated by a single link between the two pages for the objects, and the other implicit relationship is illustrated by a link structure containing the two pages. Some of the before proposed techniques for determining relationships are cohesion-based techniques, and this technique which underestimate objects containing higher degree values and also such objects could be significant in constituting relationships in Wikipedia. The other techniques are inadequate for determining implicit relationships because they use only one or two of the following three important factor such as the distance, the connectivity and the cocitation.
Towards Ontology Development Based on Relational Databaseijbuiiir1
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
From Bibliometrics to Cybermetrics - a book chapter by Nicola de BellisXanat V. Meza
Disclaimer: All original texts and images belong to their rightful owners.
Chapter 8 of the book "Bibliometrics and Citation Analysis" by Nicola de Bellis.
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...ijaia
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
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.
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
An efficient approach for semantically enhanced document clustering by using ...ijaia
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
WHEELZ TRACKER is a provider of GPS based vehicle tracking system for transportation, Utility vehicles, Public transit, Logistics and delivery. Our GPS technology solution designed for vehicle owners and managers with a personalized digital dashboard with live-tracking, Fuel monitoring and alert features.
Android a generalized flow-based method for analysis of implicit relationshi...ecway
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Abstract— Relationships are there between objects in Wikipedia. Emphases on determining relationships are there between pairs of objects in Wikipedia whose pages can be regarded as separate objects. Two classes of relationships between two objects exist there in Wikipedia, an explicit relationship is illustrated by a single link between the two pages for the objects, and the other implicit relationship is illustrated by a link structure containing the two pages. Some of the before proposed techniques for determining relationships are cohesion-based techniques, and this technique which underestimate objects containing higher degree values and also such objects could be significant in constituting relationships in Wikipedia. The other techniques are inadequate for determining implicit relationships because they use only one or two of the following three important factor such as the distance, the connectivity and the cocitation.
Towards Ontology Development Based on Relational Databaseijbuiiir1
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
From Bibliometrics to Cybermetrics - a book chapter by Nicola de BellisXanat V. Meza
Disclaimer: All original texts and images belong to their rightful owners.
Chapter 8 of the book "Bibliometrics and Citation Analysis" by Nicola de Bellis.
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...ijaia
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
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.
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
An efficient approach for semantically enhanced document clustering by using ...ijaia
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
WHEELZ TRACKER is a provider of GPS based vehicle tracking system for transportation, Utility vehicles, Public transit, Logistics and delivery. Our GPS technology solution designed for vehicle owners and managers with a personalized digital dashboard with live-tracking, Fuel monitoring and alert features.
Bed side patients monitoring system with emergency alert
Android a generalized flow-based method for analysis of implicit relationships on wikipedia
1. ECWAY TECHNOLOGIES
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A GENERALIZED FLOW-BASED METHOD FOR ANALYSIS OF IMPLICIT
RELATIONSHIPS ON WIKIPEDIA
ABSTRACT:
We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be
regarded as individual objects. Two kinds of relationships between two objects exist: in
Wikipedia, an explicit relationship is represented by a single link between the two pages for the
objects, and an implicit relationship is represented by a link structure containing the two pages.
Some of the previously proposed methods for measuring relationships are cohesion-based
methods, which underestimate objects having high degrees, although such objects could be
important in constituting relationships in Wikipedia.
The other methods are inadequate for measuring implicit relationships because they use only one
or two of the following three important factors: distance, connectivity, and cocitation. We
propose a new method using a generalized maximum flow which reflects all the three factors and
does not underestimate objects having high degree. We confirm through experiments that our
method can measure the strength of a relationship more appropriately than these previously
proposed methods do. Another remarkable aspect of our method is mining elucidatory objects,
that is, objects constituting a relationship. We explain that mining elucidatory objects would
open a novel way to deeply understand a relationship.
