Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Retrieval and feedback models for blog feed search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Knowledge base population: successful approaches and challenges
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Multi-step classification approaches to cumulative citation recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Knowledge bases such as Wikipedia have been shown to be effective to improve the performance in many information tasks. Clearly, the effectiveness is based upon the quality of these knowledge bases. A high-quality knowledge base should have up-to-date complete information. However, constructing a high-quality knowledge base is not an easy task because it would require significant manual efforts to collect relevant documents, extract valuable information and update the knowledge bases accordingly. In this paper, we aim to automate this labor-intensive process. Specifically, we focus on how to collect relevant documents with regard to an entity from sheer volume of Web data automatically. To solve the problem, we propose to construct the profile of the entity by leveraging a set of its related entities and then discuss how to use the training data to weight the related entities. Experiments over the TREC 2012 KBA collection shows that the proposed method can outperform state-of-the-art methods.