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.
Keywords
- Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation
- Knowledge Representation, Reasoning, and Logic: Reasoning about Knowlege and Belief
- Machine Learning: Knowledge-based Learning
