Scaling Semantic Parsers with On-the-Fly Ontology Matching

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1545–1556, Seattle, Washington, USA, 18-21 October 2013. c⃝2013 Association for Computational Linguistics Scaling Semantic Parsers with On-the-fly Ontology Matching Tom Kwiatkowski Eunsol Choi Yoav Artzi Computer Science & EngineeringLuke Zettlemoyer University of Washington Seattle, WA 98195 {tomk,eunsol,yoav,lsz}@cs.washington.edu Abstract We consider the challenge of learning seman- tic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to rep- resent in a fixed target ontology. For ex- ample, even simple phrases such as ‘daugh- ter’ and ‘number of people living in’ can- not be directly represented in Freebase, whose ontology instead encodes facts about gen- der, parenthood, and population. In this pa- per, we introduce a new semantic parsing ap- proach that learns to resolve such ontologi- cal mismatches. The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logical- form meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology. Experiments demonstrate state-of-the-art per- formance on two benchmark semantic parsing datasets, including a nine point accuracy im- provement on a recent Freebase QA corpus. 1 Introduction Semantic parsers map sentences to formal represen- tations of their underlying meaning. Recently, al- gorithms have been developed to learn such parsers for many applications, including question answering (QA) (Kwiatkowski et al., 2011; Liang et al., 2011), relation extraction (Krishnamurthy and Mitchell, 2012), robot control (Matuszek et al., 2012; Kr- ishnamurthy and Kollar, 2013), interpreting instruc- tions (Chen and Mooney, 2011; Artzi and Zettle- moyer, 2013), and generating programs (Kushman and Barzilay, 2013). In each case, the parser uses a predefined set of logical constants, or an ontology, to construct meaning representations. In practice, the choice of ontology significantly impacts learning. For example, consider the following questions (Q) and candidate meaning representations (MR): Q1: What is the population of Seattle? Q2: How many people live in Seattle? MR1: λx.population(Seattle, x) MR2: count(λx.person(x) ∧ live(x, Seattle)) A semantic parser might aim to construct MR1 for Q1 and MR2 for Q2; these pairings align constants (count, person, etc.) directly to phrases (‘How many,’ ‘people,’ etc.). Unfortunately, few ontologies have sufficient coverage to support both meaning representations, for example many QA databases would only include the population relation required for MR1. Most existing approaches would, given this deficiency, simply aim to produce MR1 for Q2, thereby introducing significant lexical ambiguity that complicates learning. Such ontological mis- matches become increasingly common as domain and language complexity increases. In this paper, we introduce a semantic parsing ap- proach that supports scalable, open-domain ontolog- ical reasoning. The parser first constructs a linguis- tically motivated domain-independent meaning rep- resentation. For example, possibly producing MR1 for Q1 and MR2 for Q2 above. It then uses a learned ontology matching model to transform this represen- 1545 x : How many people visit the public library …

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