2017
IJCAI
IJCAI 2017
A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations
Abstract
This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.
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Interdisciplinary Bridge
— Knowledge & Reasoning and Machine Learning
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Keyword Pioneer
— relation representation
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Hot Topic Early Bird
— knowledge graph completion
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning