TY - CHAP

T1 - EARL

T2 - 17th International Semantic Web Conference - ISWC 2018

AU - Dubey, Mohnish

AU - Banerjee, Debayan

AU - Chaudhuri, Debanjan

AU - Lehmann, Jens

N1 - Conference code: 17

PY - 2018/9/18

Y1 - 2018/9/18

N2 - Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.

AB - Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.

KW - Entity linking

KW - GTSP

KW - Question answering

KW - Relation linking

KW - Informatics

UR - http://www.scopus.com/inward/record.url?scp=85054809353&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-00671-6_7

DO - 10.1007/978-3-030-00671-6_7

M3 - Article in conference proceedings

AN - SCOPUS:85054809353

SN - 978-3-030-00670-9

VL - 1

T3 - Lecture Notes in Computer Science

SP - 108

EP - 126

BT - The Semantic Web – ISWC 2018

A2 - Vrandecic, Denny

A2 - Bontcheva, Kalina

A2 - Suárez-Figueroa, Mari Carmen

A2 - Presutti, Valentina

A2 - Celino, Irene

A2 - Sabou, Marta

A2 - Kaffee, Lucie-Aimee

A2 - Simperl, Elena

PB - Springer Schweiz

CY - Cham

Y2 - 8 October 2018 through 12 October 2018

ER -