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Abstract
Knowledge graphs have become vital resources for semantic search and provide users with precise answers to their information needs. Knowledge graphs often consist of billions of facts, typically encoded in the form of RDF triples. In most cases, these facts are extracted automatically and can thus be susceptible to errors. For many applications, it can therefore be very useful to complement knowledge graph facts with textual evidence. For instance, it can help users make informed decisions about the validity of the facts that are returned as part of an answer to a query. In this paper, we therefore propose, an approach that given a knowledge graph and a text corpus, retrieves the top-k most relevant textual passages for a given set of facts. Since our goal is to retrieve short passages, we develop a set of IR models combining exact matching through the Okapi BM25 model with semantic matching using word embeddings. To evaluate our approach, we built an extensive benchmark consisting of facts extracted from YAGO and text passages retrieved from Wikipedia. Our experimental results demonstrate the effectiveness of our approach in retrieving textual evidence for knowledge graph facts.
Original language | English |
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Title of host publication | The Semantic Web |
Editors | Krzysztof Janowicz, Armin Haller, Amrapali Zaveri, Alasdair J.G. Gray, Pascal Hitzler, Karl Hammar, Vanessa Lopez, Miriam Fernández |
Number of pages | 16 |
Place of Publication | Portorož, Slovenia |
Publisher | Springer |
Publication date | 1 Jan 2019 |
Pages | 52-67 |
ISBN (Print) | 978-3-030-21347-3 |
ISBN (Electronic) | 978-3-030-21348-3 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | 16th International Semantic Web Conference, ESWC 2019 - Portorož, Slovenia Duration: 2 Jun 2019 → 6 Jun 2019 |
Conference
Conference | 16th International Semantic Web Conference, ESWC 2019 |
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Country/Territory | Slovenia |
City | Portorož |
Period | 02/06/2019 → 06/06/2019 |
Sponsor | Elsevier, et al., QualiChain, Semantic Web Company, Siemens, STI International |
Series | Lecture Notes in Computer Science |
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Volume | 11503 |
ISSN | 0302-9743 |
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Poul Due Jensen Professorate in Big Data and Artificial Intelligence
Hose, K., Jendal, T. E. & Hansen, E. R.
01/11/2019 → 31/10/2024
Project: Research
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