An Integrated Probabilistic and Logic Approach to Encyclopedia Relation Extraction with Multiple Features

We propose a new integrated approach based on Markov logic networks (MLNs), an effective combination of probabilistic graphical models and firstorder logic for statistical relational learning, to extracting relations between entities in encyclopedic articles from Wikipedia. The MLNs model entity relations in a unified undirected graph collectively using multiple features, including contextual, morphological, syntactic, semantic as well as Wikipedia characteristic features which can capture the essential characteristics of relation extraction task. This model makes simultaneous statistical judgments about the relations for a set of related entities. More importantly, implicit relations can also be identified easily. Our experimental results showed that, this integrated probabilistic and logic model significantly outperforms the current stateof-the-art probabilistic model, Conditional Random Fields (CRFs), for relation extraction from encyclopedic articles.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Xiaofeng Yu, Wai Lam
• Keywords: COLING 2008, Computational Linguistics, Encyclopedic Articles, Model Entity Relations, Relation Extraction