Exploring Term Networks for Semantic Search over Large RDF Knowledge Graphs

Tracking #: 1717-2929

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Edgard Marx
Saeedeh Shekarpour
Konrad Höffner
Axel-Cyrille Ngonga Ngomo
Jens Lehmann
Sören Auer

Responsible editor: 
Thomas Lukasiewicz

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Full Paper
Information retrieval approaches are currently regarded as a key technology to empower lay users to access the Web of Data. To assist such need, a large number of approaches such as Question Answering and Semantic Search have been developed. While Question Answering promises accurate results by returning a specific answer, Semantic Search engines are designed to retrieve the top-K resources on a given scoring function. In this work, we focus on the latter paradigm. We aim to address one of the major drawbacks of current implementations, i.e., the accuracy. We propose *P, a Semantic Search approach that explores term networks to answer keyword queries on large RDF knowledge graphs. The proposed method is based on a novel graph disambiguation model. The adequacy of the approach is demonstrated on the QALD benchmark data set against state-of-the-art Question Answering and Semantic Search systems as well as in the Triple Scoring Challenge at the International Conference on Web Search and Data Mining (WSDM) 2017. The results suggest that *P is more accurate than the currently best performing Semantic Search scoring function while achieving a performance comparable to an average Question Answering system.
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