End-to-end learning for answering structured queries directly over text

Paul Groth, Antony Scerri, Ron Daniel, Bradley P. Allen

Research output: Contribution to journalConference articlepeer-review


Structured queries expressed in languages (such as SQL, SPARQL, or XQuery) offer a convenient and explicit way for users to express their information needs for a number of tasks. In this work, we present an approach to answer these directly over text data without storing results in a database. We specifically look at the case of knowledge bases where queries are over entities and the relations between them. Our approach combines distributed query answering (e.g. Triple Pattern Fragments) with models built for extractive question answering. Importantly, by applying distributed querying answering we are able to simplify the model learning problem. We train models for a large portion (572) of the relations within Wikidata and achieve an average 0.70 F1 measure across all models. We describe both a method to construct the necessary training data for this task from knowledge graphs as well as a prototype implementation.

Original languageEnglish
Pages (from-to)57-70
Number of pages14
JournalCEUR Workshop Proceedings
StatePublished - Jan 1 2019
Event2019 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2019 - Portoroz, Slovenia
Duration: Jun 2 2019 → …


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