Today's era of information explosion, information on a daily basis at an alarming rate. According to the statistics show that the world authority on global trading data information from the annual growth rate of 61%, and other information related to annual growth rate of more than 92%. Research into traditional relational database management system from the processing of data as structured data, to include paper documents, electronic documents, faxes, reports, tables, pictures, audio and video files, including information known as unstructured data or content. Through the survey found that in the vast amounts of information stored in corporate, structured data accounted for only 15% of the total data, and unstructured data accounts for 85% of the total data. Orderly storage, management and use of unstructured data mining is the value of all successful enterprises to improve global competitiveness and productivity of the primary means.
Leveraging Semantic Technologies for Enterprise Search Gianluca Demartini demartini@L3S.de L3S Research Center Leibniz Universität Hannover 1 – M.Sc. in Udine, Italy (Dec 05) – Ph.D. Student in Hannover, Germany (Mar 06) – Research Interests: • IR evaluation • Enterprise Search • Integration of SW and IR – My Goal: get a Ph.D. (before end 2009) Gianluca Demartini 2 – No previous work • see the 58 references in the paper – 1 slide per Research Question – Thoughts on my Big Picture 3 How can we query for different item types together: integrating document and people search – Extension of Vector Space Model to consider Docs and People – Place also People into the Space considering several evidences of expertise – Query properly in order to retrieve both Docs and People Gianluca Demartini 4 How can we query structured and unstructured data together? – DB search • Keyword search in DB – IR search • Structured search (author:john) – Goal: (un-)structured search on (un-)structured data Gianluca Demartini 5 How can we benefit from both Semantic Web and Information Retrieval techniques in enterprise search? – Semantic Search • Use metadata to improve content-based search – IR indexing • Use taxonomies instead of flat term-based indexing – Expert Search • Use ontologies as expertise taxonomies Gianluca Demartini 6 How can we enrich automatically the metadata annotation in a social infrastructure? – Scenario: desktops with metadata annotations – Search in your community for new metadata annotations – Ask to similar peers how they annotate similar resources Gianluca Demartini 7 Web, enterprise and desktop: how do they differ? – Link structure – Spam – Privacy • Sharing data • Activities Logging Gianluca Demartini 8 How can we systematically evaluate enterprise search? – Relevance Definition – Metrics – Standard evaluation approach: testbed – Privacy issues for building public collections Gianluca Demartini 9 How can we personalize the enterprise search user experience? – User Observation • Activity logging • Context detection – Tasks – User Role Gianluca Demartini 10 Privacy Information Retrieval Tags Personalization Web Search Evaluation Algorithms User Modelling Semantic Web Desktop Search SOA Metadata Recommendation Context Social Networks Expert Search – Integrate techniques from different fields – Innovate where the improvement is (economically) assessable Gianluca Demartini 11 Thanks Gianluca Demartini 12
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