Open AccessProceedings Article
Large-scale Semantic Parsing via Schema Matching and Lexicon Extension
Qingqing Cai,Alexander Yates +1 more
- pp 423-433
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TLDR
A semantic parser for Freebase is developed based on a reduction to standard supervised training algorithms, schema matching, and pattern learning that is capable of parsing questions with an F1 that improves by 0.42 over a purely-supervised learning algorithm.Abstract:
Supervised training procedures for semantic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they must see labeled training data. We present a technique for developing semantic parsers for large databases based on a reduction to standard supervised training algorithms, schema matching, and pattern learning. Leveraging techniques from each of these areas, we develop a semantic parser for Freebase that is capable of parsing questions with an F1 that improves by 0.42 over a purely-supervised learning algorithm.read more
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References
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Proceedings ArticleDOI
Freebase: a collaboratively created graph database for structuring human knowledge
TL;DR: MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
Journal ArticleDOI
A survey of approaches to automatic schema matching
Erhard Rahm,Philip A. Bernstein +1 more
TL;DR: A taxonomy is presented that distinguishes between schema-level and instance-level, element- level and structure- level, and language-based and constraint-based matchers and is intended to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.
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Andrew Carlson,Justin Betteridge,Bryan Kisiel,Burr Settles,Estevam R. Hruschka,Tom M. Mitchell +5 more
TL;DR: This work proposes an approach and a set of design principles for an intelligent computer agent that runs forever and describes a partial implementation of such a system that has already learned to extract a knowledge base containing over 242,000 beliefs.
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Open information extraction from the web
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