TY - JOUR
T1 - End-to-end learning for answering structured queries directly over text
AU - Groth, Paul
AU - Scerri, Antony
AU - Daniel, Ron
AU - Allen, Bradley P.
N1 - Publisher Copyright:
© 2019 CEUR-WS. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067888558&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85067888558
SN - 1613-0073
VL - 2377
SP - 57
EP - 70
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2019 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2019
Y2 - 2 June 2019
ER -