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Title: Semantic Information Retrieval from research papers Based on Ontology and SPARQL Query: Research paper optimizer
Authors: Abeyrathne, L.
Lankage, M.
Darshanamali, C.
Wickramarachchi, D.
koggalahewa, D.
Keywords: Information retrieval
Semantic Web Ontology
Multi agent system
Natural Language Processing
Natural Language Generation
Knowledge Representation, Knowledge Extraction
Issue Date: 2016
Publisher: SLIIT
Abstract: Research paper optimizer simulates the approach of centralizing knowledge that have been extracted from set of research papers and represent relevant information that user requests from the centralized knowledge.User can upload any number of research papers to the system then it extract information from PDF files using Natural language processing techniques and then system creates a XML file as the output of First step. This XML file contains extracted information and it will be the input for the second step. Second step is constructing an ontology that will also support protege using JENA framework. Extracted Information is automatically mapped to owl elements such as classes, sub classes, individuals and relationships. The output of this step is OWL file and it will be the input for the next step. Final step is retrieve relevant information from the constructed ontology using JENA libraries and with the support of SPARQL query language and using multi-agent system (M.A.S.) composed of multiple interacting intelligent agents within an environment
Appears in Collections:SLIIT Student Research -2016

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