The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Huanbo Luan - One of the best experts on this subject based on the ideXlab platform.

  • modeling Relation paths for representation learning of knowledge bases
    arXiv: Computation and Language, 2015
    Co-Authors: Huanbo Luan
    Abstract:

    Representation learning of knowledge bases (KBs) aims to embed both entities and Relations into a low-dimensional space. Most existing methods only consider direct Relations in representation learning. We argue that multiple-step Relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers Relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all Relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of Relation paths. (2) We represent Relation paths via semantic Composition of Relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and Relation extraction from text.

  • modeling Relation paths for representation learning of knowledge bases
    Empirical Methods in Natural Language Processing, 2015
    Co-Authors: Huanbo Luan
    Abstract:

    Representation learning of knowledge bases aims to embed both entities and Relations into a low-dimensional space. Most existing methods only consider direct Relations in representation learning. We argue that multiple-step Relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers Relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all Relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of Relation paths. (2) We represent Relation paths via semantic Composition of Relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and Relation extraction from text. The source code of this paper can be obtained from https://github.com/mrlyk423/ Relation_extraction.