SenseNet: A Knowledge Representation Model for Computational Semantics

Ping Chen, Wei Ding, Chengmin Ding
2006 2006 5th IEEE International Conference on Cognitive Informatics  
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
more » ... 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. 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.
doi:10.1109/coginf.2006.365528 dblp:conf/IEEEicci/ChenDD06 fatcat:fasmhksuvna7nisk4bfytwzdye