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Capturing Knowledge in Semantically-typed Relational Patterns to Enhance Relation Linking

 
: Singh, Kuldeep; Mulang, Isaiah Onando; Lytra, Ioanna; Jaradeh, Mohamad Yaser; Sakor, Ahmad; Vidal, Maria-Esther; Lange, Christoph; Auer, Sören

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Association for Computing Machinery -ACM-:
K-CAP 2017. Proceedings of the Knowledge Capture Conference : Austin, TX, USA, December 04 - 06, 2017
New York: ACM, 2017
ISBN: 978-1-4503-5553-7
Article 31
International Conference on Knowledge Capture (K-CAP) <9, 2017, Austin/Tex.>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.

: http://publica.fraunhofer.de/dokumente/N-481357.html