Tool for Ontology Paraphrasing, Querying and Visualization on the Semantic Web Project By Senthil Kumar K III MCA (SS)‏

Guides Details

Project Context Military Intelligence Reports XML Documents Semantic Web (Ontology)‏ Summary of Intelligence Reports Information Extraction Ontology Generation Paraphrasing and Querying with Visualization

Ontology Ontology is defined as an explicit specification of conceptualization. It is a way of representing things and the relationship between things in such a way that a machine can understand it. It is a mechanized way of Knowledge Representation. Ontology is used as the Knowledge Representation format at CAIR.

Paraphrasing Context Summarizer Module

Paraphrasing Steps Document Planning Content Selection : Specify maximum allowed distance in graph. Text Planning : Ordering of facts at various distances. Micro Planning Micro plans are templates with numerous slots and fillers for these slots. Lexicalization : Specify single sentence for each fact. Aggregation : Multiple sentences aggregated to improve readability. Surface Realization Grammar used to fill up missing information.

Paraphrasing Example XYZ_FN Lahore Pakistan located belongs Karachi moved belongs Input Ontology After Lexicalization 1)XYZ_FN is moved to Karachi. 2)Karachi belongs to Pakistan. 3)XYZ_FN is located at Lahore. 4)Lahore belongs to Pakistan.

Paraphrasing Example (Contd.)‏ After Aggregation 1)XYZ_FN is located at Lahore which belongs to Pakistan. 2)XYZ_FN is moved to Karachi which belongs to Pakistan. Final Summary XYZ_FN is located at Lahore which belongs to Pakistan and it is moved to Karachi which belongs to Pakistan.

SPARQL: – SPARQL is an RDF query language, that stands for Simple Protocol and RDF Query Language. – SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns. Example: PREFIX abc: SELECT ?capital ?country WHERE { ?x abc:cityname ?capital ; abc:isCapitalOf ?y. ?y abc:countryname ?country ; abc:isInContinent abc:Africa. } SPARQL query returns all country capitals in Africa

Information Visualization Information Visualization concentrates on the use of computer-supported tools to explore large amount of abstract data. Compact graphical presentation for visualizing large numbers of items possibly extracted from far larger datasets. Enables users to make discoveries, decisions or explanations about patterns (trend, cluster, gap, outlier...), groups of items, or individual items.

Knowledge Visualization Knowledge Visualization focuses on transferring insights and creating new knowledge. Beyond the mere transfer of facts, knowledge visualization aims to further transfer insights, experiences, perspectives and predictions by using various complementary visualizations.

Existing Work and Issues No tool available for paraphrasing from the ontology and rendering a visual representation of the same. Query engine support with Visual representation of the output is required. Browser based tool required to perform the above.

Proposed Work To develop a web application for paraphrasing and querying from an ontology with visualization support for AINN group of CAIR (DRDO) which is fully browser based.

Architecture Diagram OWL Files Java Library JUNG,JENA Library Ontology Querying and Visualization Applets Browsers Java Applet Class File Web Server Client

Block Diagram Ontology Graph Summary of Intelligence Reports Query Result Visual Graph Ontology OWL Graph Construction Paraphrase Query Processor Visualizer

Software Requirements JDK (Java Development Kit) 1.5 JRE 1.5 or above Supported Web Browser JUNG (Java’s Universal Graph/Network Library) JENA (Java framework for building Semantic Web applications) 2.5.7

What JENA can / can’t do? Can: – It provides a programmatic environment for RDF, RDFS and OWL. – It provides SPARQL processing support on ontology. – It provides Rule based Inference Engine. Can’t: – It cant take Ontology as an input. – It doesn’t provide support for Graph based Traversals and Querying. – It doesn’t provide support for paraphrasing from the ontology.

What JUNG can / can’t do? Can: – It provides a very basic library for creating simple graphs and visualizing it. – It provides basic layouts support for the graph rendering. – It can only understand vertices, edges and basic graph primitives Can’t: – It cant model the Ontology as a graph. – It cant enforce the user specific constraints followed in owl specification on the graph. – It cant Rearrange the graph based on the changes made to the ontology

Major Modules Paraphraser Query Processor Visualizer

Level – 0 DFD Visual Graph Query Result Paraphrased Summary Ontology Paraphrasing, Querying and Visualizing Tool OWL

Level – 1 DFD OWL Graph Natural Language Summary Textual Result OWL Query 1 OWL Graph Constructor User 3 Query Processor 2 Paraphraser OWL Graph 4 Visualizer Visual Graph OWL Graph

OWL Graph Constructor(1)‏ 1.1 Read the OWL File and Load the Ontology 1.2 Map the ontology resources to Nodes and Edges OWL Nodes and Edges OWL Ontology Model 1.3 Build the graph Using the nodes and edges OWL Graph

Paraphraser(2)‏ 2.1 Select Contents of Interest 2.2 Lexicalize Sentences and Aggregate them. Formed Sentences OWL Graph Selected Facts 2.3 Apply grammar to the formed sentences. Natural Language Summary OWL Sub Graph

Query Processor(3)‏ 3.1 Validate the Query and Plan the Execution 3.2 Execute the Query by Traversing the Graph OWL Sub Graph OWL Graph OWL Graph and Query Textual Result Query

Visualizer(4)‏ 4.1 Create Renderer and layout for the Graph 4.2 Set Visual attributes for the OWL Nodes and Edges Renderer OWL Graph 4.3 Render OWL Graph Visual Graph Renderer

References 1) OWL Web Ontology Language Guide: 2) SPARQL: 3) “Generating Natural Language Descriptions from OWL Ontologies” - Ion Androutsopoulos1,2 and Dimitrios Galanis1 4) “Speech and Language Processing”, ISBN Daniel Jurafsky and James H.Martin 5) “Systemic Functional Linguistics” - 6) “Systemic Functional Grammar” /Publications/sfg_firststep/SFG%20intro%20New.html 7) “Paraphrasing from ontology”