Sciweavers

Share
IEEEICCI
2006
IEEE

SenseNet: A Knowledge Representation Model for Computational Semantics

8 years 9 months ago
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.
Ping Chen, Wei Ding 0003, Chengmin Ding
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IEEEICCI
Authors Ping Chen, Wei Ding 0003, Chengmin Ding
Comments (0)
books