SenseNet: A Knowledge Representation Model for Computational Semantics

摘要：

Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This paper describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model An inference algorithm is proposed to simulate human-like text analysis procedure. A. new measurement, confidence, is introduced to facilitate the text analysis. We present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings.

关键词： Knowledge Representation Computational Semantics Hidden Markov Model Natural Language Processing Information Retrieval

作者: Ping Chen Wei Ding Chengmin Ding

作者单位: Dept. of Computer and Mathematical Sciences Univ. of Houston-Downtown One Main St. Houston, TX 77002 Dept. of Computer Science Univ. of Houston-Clear Lake 2700 Bay Area Blvd. Houston, TX 77058 IBM Business Consulting 12902 Federal Systems Fairfax, VA 22033

会议类型: 国际会议

会议名称: Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)

会议地点: 北京

会议语种:英文

页码: 434-439

在线出版日期: 2006-07-17（万方平台首次上网日期，不代表论文的发表时间）