Enriching mobile semantic search with web services

With the increasing number of mobile devices, there have been many researches on searching and managing a large volume of mobile data. Most of the mobile platforms today provide users with keyword-based full text search (FTS) in order to search for mobile data. Recently, voice search interfaces have been deployed. These search methods, however, query only the keywords given as an input to local databases in mobile devices. Therefore, it is quite difficult to figure out and to provide what a user really wants. To overcome this limitation, we propose a semantic search method for mobile platforms. The proposed method augments the results of semantic search on local databases with their related useful Web information according to the intention and context information of a user. Although there are various semantic search techniques, it is hard to apply the existing methods to mobile devices due to the characteristics of mobile devices such as isolated database structures and limited computing resources. To enable semantic search on mobile devices, we also propose a lightweight mobile ontology. The proposed mobile ontology is also aligned with related Web information to enrich search results. Experimental results from prototype implementation of the proposed method verify that our approach provides more accurate results than the conventional FTS does. In addition, the proposed method shows an acceptable amount of response time and battery consumption.

[1]  Ellen M. Voorhees,et al.  The TREC-8 Question Answering Track Report , 1999, TREC.

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

[3]  Sebastian Hellmann,et al.  Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[4]  Kyong-Ho Lee,et al.  Keyword Based Semantic Search for Mobile Data , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[5]  Susan T. Dumais,et al.  Enhancing Performance in Latent Semantic Indexing (LSI) Retrieval , 1990 .

[6]  Haofen Wang,et al.  Q2Semantic: A Lightweight Keyword Interface to Semantic Search , 2008, ESWC.

[7]  Haofen Wang,et al.  Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[8]  Kyong-Ho Lee,et al.  Semantically enhanced keyword search for smartphones , 2014, WWW '14 Companion.

[9]  Ihab F. Ilyas,et al.  Expressive and flexible access to web-extracted data: a keyword-based structured query language , 2010, SIGMOD Conference.

[10]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[11]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[12]  Wolfgang Nejdl,et al.  From keywords to semantic queries - Incremental query construction on the semantic web , 2009, J. Web Semant..

[13]  Julio Gonzalo,et al.  Indexing with WordNet synsets can improve text retrieval , 1998, WordNet@ACL/COLING.

[14]  Kemafor Anyanwu,et al.  Effectively Interpreting Keyword Queries on RDF Databases with a Rear View , 2011, International Semantic Web Conference.

[15]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[16]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[17]  Escuela Politécnica Superior,et al.  Semantically enhanced Information Retrieval: an ontology-based approach , 2009 .