DocumentCode
463386
Title
SenseNet: A Knowledge Representation Model for Computational Semantics
Author
Chen, Ping ; Ding, Wei ; Ding, Chengmin
Author_Institution
Dept. of Comput. & Math. Sci., Univ. of Houston-Downtown, Houston, TX
Volume
1
fYear
2006
fDate
17-19 July 2006
Firstpage
434
Lastpage
439
Abstract
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
Keywords
inference mechanisms; knowledge representation; natural language processing; text analysis; SenseNet; commonsense reasoning; computational semantics; hidden Markov model; intelligent information processing; knowledge representation model; natural language processing; semantics modeling; text analysis; Analytical models; Computational intelligence; Computational modeling; Hidden Markov models; Inference algorithms; Information processing; Knowledge representation; Large-scale systems; Natural language processing; Text analysis; Computational Semantics; Hidden Markov Model; Information Retrieval; Knowledge Representation; Natural Language Processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
1-4244-0475-4
Type
conf
DOI
10.1109/COGINF.2006.365528
Filename
4216445
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