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Semantic Scouting: Search, Analysis, and Presentation of Entities and Knowledge

Semantic Scouting is a framework that utilizes Linked Data, Semantic Web, Natural Language Processing, Information Retrieval, and Social Network Analysis technologies for searching, presenting, and analyzing entities and their associated knowledge. This application has been developed by the Semantic Technology Lab (STLab) at ISTC-CNR Rome and has been continuously evolving since 2006. It offers features such as semantic search, hybrid IR/SW identity management, and automatic document classification against DBpedia.

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Semantic Scouting: Search, Analysis, and Presentation of Entities and Knowledge

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  1. STLab • Semantic Technology Lab, ISTC-CNR, Rome • Aldo Gangemi, Alfio Gliozzo, Valentina Presutti, Alberto Salvati, Eva Blomqvist, Alessandro Adamou, Enrico Daga, Paolo Annesi, Concetto Bonafede, Claudio Baldassarre, Stefania Capotosti 1

  2. External funding 2008-9: ≈700K€ International publications of STLab people: >200 Combined h-index of STLab: 52

  3. Sample publications • Gangemi A., Presutti V. Ontology Design Patterns, in Staab S. et al. (eds.): Handbook of Ontologies (2nd edition), Springer, 2009 • Gangemi A., Euzenat J. (eds.) Knowledge Engineering: Practice and Patterns, Springer, 2008 • Blomqvist E., Gangemi A., Presutti V. Experiments in Pattern-based Ontology Design, Proceedings of KCAP09, Los Angeles, ACM Press, 2009Huang C.R., Calzolari N., • Presutti V., Gangemi A. Identity of Resources and Entities on the Web. International Journal on Semantic Web and Information Systems, 4(2), 2008 • Gangemi, A. Norms and plans as unification criteria for social collectives. Autonomous Agents and Multi-Agent Systems, 16(3), 2008 • Coppola B., Gangemi A., Gliozzo A., Picca D., Presutti V. Frame Detection over the Semantic Web. European Semantic Web Conference (ESWC2009), Springer, 2009 • Ciaramita M., Gangemi A., Ratsch E, Rojas I, Saric J. Unsupervised Learning of Semantic Relations between Concepts of a Molecular Biology Ontology, in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI2005), 2005 • Gliozzo A., Strapparava C. Semantic Domains in Computational Linguistics, Springer, 2009 • Gangemi A., Lenci A., Oltramari A., Prevot L. (eds.). Ontologies and the Lexicon. Cambridge University Press, 2009 7

  4. Quick recap on semantic technologies • Semantic technologies make it emerge a lot of information from the Web and local information systems (data silos) • Data integration, through reengineering (e.g. triplify), or querying (e.g. D2R) • Linking of heterogeneous data sources, either at schema or instance level • Extraction of new data (machine learning, NLP) from documents, and their semantic representation • Reasoning on those data, extracting more implicit information • Presentation of data on the simplest platform: the Web, and so enabling sharing, collaborative editing, customization, etc. (URI-based data integration and enrichment)

  5. Major breakthroughs • eXtreme ontology Design methods and tools (NeOn project) • design assistants, community portal, automatic ontology construction • Semantic search tools for organizational knowledge (Scouting, IKS projects) • semantic data reengineering, entity search, automatic document classification, linked data browsing, expert finding, identity management • Semantic wikis and CMS tools (NeOn, IKS projects) • Semantic e-Learning tools (Bony project) • advanced search, learning unit ontologies, social network analysis • The ontology-lexicon interface, frame semantics (NeOn project) • Social, legal, historical ontology frameworks (HKR project) • Formal model and practical methods for ontology evaluation (NeOn project) • Semantic configuration of software components and interaction (e.g. in Eclipse) (NeOn project) 9

  6. A sample application:Semantic Scouting • ISTC-CNR - STLab • Aldo Gangemi, Alberto Salvati, Enrico Daga, Claudio Baldassarre, Alfio Gliozzo, Paolo Annesi, Gianluca Troiani, Andrea Pompili, Angelo Olivieri • http://stlab.istc.cnr.it 1

  7. Semantic Scout • A framework for search, presentation, and analysis of entities and their associated knowledge • Employs technologies from Linked Data, Semantic Web, NLP, IR, Social Network Analysis • Scientific work goes back to 2006, first presented at ISWC2007 • An IKS evolving prototype for some requirements: semantic search, hybrid IR/SW identity management, automatic document classification (against DBpedia) • 2009 requirements from the technology transfer office of CNR for the NetwOrK initiative

  8. Main aspects

  9. Figures (current) • 28 modules • 120 classes • 300 relations • 1200 axioms • >200K entities • ≈3M facts (about 2M inferred or extracted) • 2372 datasets 4

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