SINA: Semantic Interpretation of User Queries for Question Answering on Interlinked Data

Author(s)

Saeedeh Shekarpour, University of DaytonFollow
Edgard Marx, University of Bonn
Axel-Cyrille Ngonga Ngomo, University of Bonn
Sören Auer, Universitat Leipzig

Document Type

Article

Publication Date

1-2015

Publication Source

Journal of Web Semantics: Science, Services and Agents on the World Wide Web

Abstract

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets.

Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.

Inclusive pages

39-51

ISBN/ISSN

1570-8268

Comments

Permission documentation on file.

Copyright

Copyright © 2014, Elsevier

Publisher

Elsevier

Volume

30

Peer Reviewed

yes

eCommons Citation

Shekarpour, Saeedeh; Marx, Edgard; Ngonga Ngomo, Axel-Cyrille; and Auer, Sören, "SINA: Semantic Interpretation of User Queries for Question Answering on Interlinked Data" (2015). Computer Science Faculty Publications. 146.
https://ecommons.udayton.edu/cps_fac_pub/146