Predicting the Usefulness of Collection Enrichment for Enterprise Search

Enterprise search is changing. The explosion of information within organizations, technological advances and availability of free OpenSource machine learning libraries offer many possibilities. Eighteen informants from practice, academia, search technology vendors and large organizations (Oil and Gas, Governments, Pharmaceuticals, Aerospace and Retail) were interviewed to assess challenges and future directions. The findings confirmed the existence of the ‘Google Habitus’, technology propaganda and a need to transcend disciplines for a Systems thinking approach toward enterprise search. This encompasses information management, user search literacy, governance, learning feedback loops as well as technology. A novel four-level model for enterprise search use cases is presented, covering search as a utility, search as an answer machine, search task apps and a discovery engine. This could be used to reframe enterprise search perceptions, expanding possibilities and improving business outcomes.

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Semantic Search on Unstructured Data

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2010040102 ◽

2010 ◽

Vol 6 (2) ◽

pp. 17-35 ◽

Cited By ~ 2

Author(s):

Alex Kohn ◽

François Bry ◽

Alexander Manta

Keyword(s):

Retrieval System ◽

Pharmaceutical Research ◽

Information Retrieval System ◽

Semantic Search ◽

Unstructured Data ◽

Search Performance ◽

Adaptive Search ◽

Retrieval Performance ◽

Enterprise Search ◽

Existing Data

Studies agree that searchers are often not satisfied with the performance of current enterprise search engines. As a consequence, more scientists worldwide are actively investigating new avenues for searching to improve retrieval performance. This paper contributes to YASA (Your Adaptive Search Agent), a fully implemented and thoroughly evaluated ontology-based information retrieval system for the enterprise. A salient particularity of YASA is that large parts of the ontology are automatically filled with facts by recycling and transforming existing data. YASA offers context-based personalization, faceted navigation, as well as semantic search capabilities. YASA has been deployed and evaluated in the pharmaceutical research department of Roche, Penzberg, and results show that already semantically simple ontologies suffice to considerably improve search performance.

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