An Entity Relationship Extraction Model Based on Chameleon Clustering Algorithm

Current relationship extraction models are human centered. It doesn’t consider the impact of time attribute and only focuses on extracting whole relationship network of a group. In accordance with these problems, this paper proposes an entity relationship extraction model based on Chameleon Clustering Algorithm. By collecting and analyzing interactions between entities, the new model can find out sub-clusters of a group and extract relationship between these sub-clusters. It fully considers the impact of time attribute. With a real data set, the experiments demonstrate that sub-clusters and relationship between them can be found by the model we proposed efficiently. It lays a solid foundation for further study of entity relationship networks.