Adaptive Named Entity Recognition for Social Network Analysis and Domain Ontology Maintenance
We present a system which unearths relationships between named entities from information in Web pages. We use an adaptive named entity recognition system, ESpotter, which recognizes entities of various types with high precision and recall from various domains on the Web, to generate entity data such as peoples' names. Given an entity, we apply a link analysis algorithm to the entity data for finding other entities which are closely related to it. We present our results to people whose names had been included for them to assess our findings. User feedback is analyzed by a statistical method. The results can be used to maintatin a domain ontology. Our experiments on the Knowledge Media Institute (KMi) domain show that our system can accurately find entities such as organizations, people, projects, and research areas which are closely related to people working in KMi, and the results conform with the existing knowledge in our ontology and suggest new knowledge which can be used to update the ontology.