SenseNet: A Knowledge Representation Model for Computational Semantics

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.

• Type: Conference
• Year: 2006
• Where: IEEEICCI
• Authors: Ping Chen, Wei Ding 0003, Chengmin Ding
• Keywords: IEEEICCI 2006, Informatics, Knowledge Representation, Knowledge Representation Model, Knowledge Representation Techniques