A context - based semantically enhanced information retrieval model

This paper approaches the use of both context and semantic information in the information retrieval process with the goal of developing context-based semantically enhanced information retrieval systems. To achieve our objective we have identified, defined and formalized three distinct types of context information relevant for an information retrieval system: knowledge context information, user context information and constraint context information. The context information is represented in an information system interpretable way by mapping it onto our RAP context model elements. The proposed information retrieval model is tested using the arhiNet system, our integrated information retrieval system for archive content, based on semantic enhancements.

[1]  Krzysztof Janowicz,et al.  Kinds of Contexts and their Impact on Semantic Similarity Measurement , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[2]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[3]  Ladan Tahvildari,et al.  Adaptive Action Selection in Autonomic Software Using Reinforcement Learning , 2008, Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08).

[4]  I. V. Ramakrishnan,et al.  On precision and recall of multi-attribute data extraction from semistructured sources , 2003, Third IEEE International Conference on Data Mining.

[5]  Ke Wang,et al.  Mining Generalized Associations of Semantic Relations from Textual Web Content , 2007, IEEE Transactions on Knowledge and Data Engineering.

[6]  Volker Haarslev,et al.  Racer: An OWL Reasoning Agent for the Semantic Web , 2003 .

[7]  Ioan Salomie,et al.  ArhiNet - A System for Generating and Processing Semantically-Enhanced Archival eContent , 2009, WEBIST.

[8]  Jerald Hughes,et al.  The Ability-Motivation-Opportunity Framework for Behavior Research in IS , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[9]  Marek Ciglan,et al.  Ontea : Semi-automatic Pattern based Text Annotation empowered with Information Retrieval Methods , 2007 .

[10]  Florence Amardeilh OntoPop or how to annotate documents and populate ontologies from texts , 2006 .

[11]  Raymond Cunningham,et al.  Self-Adapting Context Definition , 2007, First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007).

[12]  M. Dinsoreanu,et al.  RAP - a basic context awareness model , 2008, 2008 4th International Conference on Intelligent Computer Communication and Processing.

[13]  Paul Buitelaar,et al.  Ontology-based Information Extraction with SOBA , 2006, LREC.

[14]  James P. Callan,et al.  Combining document representations for known-item search , 2003, SIGIR.

[15]  Ulrich Schäfer,et al.  Integrating deep and shallow natural language processing components: representations and hybrid architectures , 2006 .

[16]  Abraham Bernstein,et al.  Querying the Semantic Web with Ginseng: A Guided Input Natural Language Search Engine , 2009 .

[17]  Jianyong Wang,et al.  Effective keyword search for valuable lcas over xml documents , 2007, CIKM '07.