Grounding proposition stores for question answering over linked data

Bernardo Cabaleiro, Anselmo Peñas, Suresh Kumar Manandhar

Research output: Contribution to journalArticlepeer-review


Grounding natural language utterances into semantic representations is crucial for tasks such as question answering and knowledge base population. However, the importance of the lexicons that are central to this mapping remains unmeasured because question answering systems are evaluated as end-to-end systems.

This article proposes a methodology to enable a standalone evaluation of grounding natural language propositions into semantic relations by fixing all the components of a question answering system other than the lexicon itself. Thus, we can explore different configurations trying to conclude which are the ones that contribute better to improve overall system performance.

Our experiments show that grounding accounts with close to 80% of the system performance without training, whereas training supposes a relative improvement of 7.6%. Finally we show how lexical expansion using external linguistic resources can consistently improve the results from 0.8% up to 2.5%.
Original languageEnglish
Pages (from-to)34-42
Number of pages9
JournalKnowledge Based Systems
Early online date26 Apr 2017
Publication statusPublished - 15 Jul 2017

Bibliographical note

© 2017 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.


  • Question answering, Semantic parsing, Linked data, Grounding

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