Keyword-Driven Resource Disambiguation over RDF Knowledge Bases
Document Type
Conference Paper
Publication Date
2012
Publication Source
Joint International Semantic Technology Conference: JIST 2012: Semantic Technology
Abstract
Keyword search is the most popular way to access information. In this paper we introduce a novel approach for determining the correct resources for user-supplied queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. We leverage the semantic relationships between resources for computing the parameter estimations. In this approach, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segments is obtained leveraging the underlying knowledge base; then, the final correct set of segments is determined after the most likely resource mapping was computed. While linguistic analysis (e.g. named entity, multi-word unit recognition and POS-tagging) fail in the case of keyword-based queries, we will show that our statistical approach is robust with regard to query expression variance. Our experimental results reveal very promising results.
Inclusive pages
159-174
ISBN/ISSN
9783642379956
Copyright
Copyright © 2013, Springer
Publisher
Springer
eCommons Citation
Shekarpour, Saeedeh; Ngonga Ngomo, Axel-Cyrille; and Auer, Sören, "Keyword-Driven Resource Disambiguation over RDF Knowledge Bases" (2012). Computer Science Faculty Publications. 149.
https://ecommons.udayton.edu/cps_fac_pub/149
COinS
Comments
The Joint International Semantic Technology Conference: JIST 2012: Semantic Technology is part of the Lecture Notes in Computer Science book series.
Permission documentation on file.