· 2014
Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
- 191citations
- 2014year
Short summary
A novel tensor decomposition method for knowledge base embedding significantly speeds up learning (10x faster) and improves relation extraction performance by 10 points in weighted mean average precision when used with existing state-of-the-art systems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a tensor decomposition method for knowledge base embedding.
- Leverages entity type information for faster learning.
- Achieves 10x faster learning than previous approaches.
- Improves weighted mean average precision of a state-of-the-art relation extraction system by 10 points.
AI-generated from the title and abstract; the full text is not read.
Abstract
While relation extraction has traditionally been viewed as a task relying solely on textual data, recent work has shown that by taking as input existing facts in the form of entity-relation triples from both knowl-edge bases and textual data, the perfor-mance of relation extraction can be im-proved significantly. Following this new paradigm, we propose a tensor decompo-sition approach for knowledge base em-bedding that is highly scalable, and is es-pecially suitable for relation extraction. By leveraging relational domain knowl-edge about entity type information, our learning algorithm is significantly faster than previous approaches and is better able to discover new relations missing from the database. In addition, when ap-plied to a relation extraction task, our ap-proach alone is comparable to several ex-isting systems, and improves the weighted mean average precision of a state-of-the-art method by 10 points when used as a subcomponent. 1
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics