Web Searching with Entity Mining at Query Time

In this paper we present a method to enrich the classical web searching with entity mining that is performed at query time. The results of entity mining (entities grouped in categories) can complement the query answers with useful for the user information which can be further exploited in a faceted search-like interaction scheme. We show that the application of entity mining over the snippets of the top-hits of the answers, can be performed at real-time. However mining over the snippets returns less entities than mining over the full contents of the hits, and for this reason we report comparative results for these two scenarios. In addition, we show how Linked Data can be exploited for specifying the entities of interest and for providing further information about the identified entities, implementing a kind of entity-based integration of documents and (semantic) data. Finally, we discuss the applicability of this approach on professional search, specifically for the domains of fisheries/aquaculture and patents.

[1]  Kevin Chen-Chuan Chang,et al.  Supporting entity search: a large-scale prototype search engine , 2007, SIGMOD '07.

[2]  Stuart Macdonald,et al.  User Engagement in Research Data Curation , 2009, ECDL.

[3]  Roelof van Zwol,et al.  Faceted exploration of image search results , 2010, WWW '10.

[4]  Athman Bouguettaya,et al.  Web Information System Engineering - WISE 2011 - 12th International Conference, Sydney, Australia, October 13-14, 2011. Proceedings , 2011, WISE.

[5]  Fulvio Corno,et al.  Review of the state-of-the-art in patent information and forthcoming evolutions in intelligent patent informatics , 2010 .

[6]  Giovanni Maria Sacco,et al.  Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience , 2009, The Information Retrieval Series.

[7]  Kalina Bontcheva,et al.  Evolving GATE to meet new challenges in language engineering , 2004, Natural Language Engineering.

[8]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[9]  Roelof van Zwol,et al.  Machine learned ranking of entity facets , 2010, SIGIR '10.

[10]  Lora Aroyo,et al.  The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I , 2011, SEMWEB.

[11]  Felix Naumann,et al.  ECIR - A Lightweight Approach for Entity-Centric Information Retrieval , 2010, TREC.

[12]  Oren Etzioni,et al.  Web document clustering: a feasibility demonstration , 1998, SIGIR '98.

[13]  Sébastien Ferré,et al.  Semantic Search: Reconciling Expressive Querying and Exploratory Search , 2011, SEMWEB.

[14]  Kevin Chen-Chuan Chang,et al.  Entity Search Engine: Towards Agile Best-Effort Information Integration over the Web , 2007, CIDR.

[15]  Wim Vanderbauwhede,et al.  A survey of patent users: an analysis of tasks, behavior, search functionality and system requirements , 2010, IIiX.

[16]  Norbert Fuhr.  An infrastructure for supporting the evaluation of interactive information retrieval , 2011, DESIRE '11.

[17]  Yannis Tzitzikas,et al.  On exploiting static and dynamically mined metadata for exploratory web searching , 2011, Knowledge and Information Systems.

[18]  Kevin Chen-Chuan Chang,et al.  EntityRank: Searching Entities Directly and Holistically , 2007, VLDB.

[19]  Yannis Tzitzikas,et al.  Interactive Exploration of Fuzzy RDF Knowledge Bases , 2011, ESWC.

[20]  Barry Bishop,et al.  FactForge: A fast track to the Web of data , 2011, Semantic Web.

[21]  Yannis Tzitzikas,et al.  Scalable, flexible and generic instant overview search , 2012, WWW.

[22]  Yannis Tzitzikas,et al.  Exploratory Web Searching with Dynamic Taxonomies and Results Clustering , 2009, ECDL.

[23]  Yannis Tzitzikas,et al.  Exploiting Available Memory and Disk for Scalable Instant Overview Search , 2011, WISE.

[24]  François Bry,et al.  Professional Search: Requirements, Prototype and Preliminary Experience Report , 2008 .