Knowledgebase Representation for Royal Bengal Tiger In The Context of Bangladesh

Royal Bengal Tiger is one of the penetrating threaten animal in Bangladesh forest at Sundarbans. In this work we have had concentrate to establish a robust Knowledgebase for Royal Bengal Tiger. We improve our previous work to achieve efficiency on knowledgebase representation. We have categorized the tigers from others animal from collected data by using Support Vector Machines(SVM) .Manipulating our collected data in a structured way by XML parsing on JAVA platform. Our proposed system generates n-triple by considering parsed data. We proceed on an ontology is constructed by Protégé which containing information about names, places, awards. A straightforward approach of this work to make the knowledgebase representation of Royal Bengal Tiger more reliable on the web. Our experiments show the effectiveness of knowledgebase construction. Complete knowledgebase construction of Royal Bengal Tigers how the efficient out-put. The complete knowledgebase construction helps to integrate the raw data in a structured way. The outcome of our proposed system contains the complete knowledgebase. Our experimental results show the strength of our system by retrieving information from ontology in reliable way. IndexTerms : Ontology, Linked data, Web Semantics, XML parsing, N-triples, Royal Bengal Tiger.

[1]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[2]  Mark Fischetti,et al.  Weaving the web - the original design and ultimate destiny of the World Wide Web by its inventor , 1999 .

[3]  Jérôme Euzenat,et al.  Ten Challenges for Ontology Matching , 2008, OTM Conferences.

[4]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[5]  Masaki Aono,et al.  Anchor-Flood: Results for OAEI 2009 , 2009, OM.

[6]  Michel C. A. Klein,et al.  Structure-Based Partitioning of Large Concept Hierarchies , 2004, SEMWEB.

[7]  Asunción Gómez-Pérez,et al.  Six challenges for the Semantic Web , 2002, KR 2002.

[8]  Masaki Aono,et al.  An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size , 2009, J. Web Semant..

[9]  Klaus Meißner,et al.  Semantic Metadata Instantiation and Consolidation within an Ontology-based Multimedia Document Management System , 2008, SeMMA.

[10]  Roy Rada,et al.  Development and application of a metric on semantic nets , 1989, IEEE Trans. Syst. Man Cybern..

[11]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[12]  Bernardo Cuenca Grau.  Modularizing OWL Ontologies , 2005 .

[13]  Alan L. Rector,et al.  Web ontology segmentation: analysis, classification and use , 2006, WWW '06.

[15]  Stefanos D. Kollias,et al.  A String Metric for Ontology Alignment , 2005, SEMWEB.

[16]  Christiane Fellbaum,et al.  Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms , 1998 .

[17]  Masaki Aono,et al.  Alignment Results of Anchor-Flood Algorithm for OAEI-2008 , 2008, OM.

[18]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[19]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[20]  M. Ross Quillian,et al.  Retrieval time from semantic memory , 1969 .

[21]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[22]  Yuzhong Qu,et al.  Matching large ontologies: A divide-and-conquer approach , 2008, Data Knowl. Eng..

[23]  Yuzhong Qu,et al.  Partition-Based Block Matching of Large Class Hierarchies , 2006, ASWC.

[24]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[25]  Yuzhong Qu,et al.  The Results of Falcon-AO in the OAEI 2006 Campaign , 2006, Ontology Matching.

[26]  José L. V. Mejino,et al.  A reference ontology for biomedical informatics: the Foundational Model of Anatomy , 2003, J. Biomed. Informatics.

[27]  Troels Andreasen,et al.  Perspectives on ontology‐based querying , 2007, Int. J. Intell. Syst..

[28]  Heiner Stuckenschmidt,et al.  Results of the Ontology Alignment Evaluation Initiative 2007 , 2006, OM.

[29]  Tony Veale,et al.  An Intrinsic Information Content Metric for Semantic Similarity in WordNet , 2004, ECAI.

[30]  Marc Ehrig,et al.  Ontology Alignment: Bridging the Semantic Gap , 2006 .

[31]  G. Miller,et al.  Contextual correlates of semantic similarity , 1991 .

[32]  Heiner Stuckenschmidt,et al.  Results of the Ontology Alignment Evaluation Initiative , 2007 .