Abstract
More and more RDF datasets are available on the web. These datasets can be queried using the SPARQL language; to do so, one must be familiar with the query language itself, but also with the content of the dataset in terms of resources and properties in order to formulate the queries. Keyword search is an alternative way to query RDF data. In this paper, we present a keyword search approach which uses online lexical databases to bridge the terminological gap between the keywords and the dataset when searching for matching elements in the dataset. We formulate the problem of aggregating the matching elements as a Steiner tree problem and we adapt Kruskal’s algorithm to provide a solution. We also propose a ranking approach if several answers are found for a given query. We have performed some experiments on the DBpedia and the AIFB datasets to illustrate the effectiveness of our approach.
This work was funded by the National Council for Scientific Research of Lebanon (CNRS-L) and the French National Research Agency through the CAIR ANR-14-CE23-0006 project.
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Rihany, M., Kedad, Z., Lopes, S. (2019). A Keyword Search Approach for Semantic Web Data. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_11
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