Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Statistical mechanics of complex networks
Statistical mechanics of complex networks
Sentence co-occurrences as small-world graphs: a solution to automatic lexical disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Modeling the Activity of a Multiagent System with Evolving Metadata
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Graph decomposition approaches for terminology graphs
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Information Sciences: an International Journal
From information networks to bisociative information networks
Bisociative Knowledge Discovery
Bridging concept identification for constructing information networks from text documents
Bisociative Knowledge Discovery
Hi-index | 0.01 |
During the past several years, social network analysis methods have been used to model many complex real-world phenomena, including social networks, transportation networks, and the Internet. Graph theoretic methods, based on an elegant representation of entities and relationships, have been used in computational biology to study biological networks; however they have not yet been adopted widely by the greater informatics community. The graphs produced are generally large, sparse, and complex, and share common global topological properties. In this review of research (1998-2005) on large-scale semantic networks, we used a tailored search strategy to identify articles involving both a graph theoretic perspective and semantic information. Thirty-one relevant articles were retrieved. The majority (28, 90.3%) involved an investigation of a real-world network. These included corpora, thesauri, dictionaries, large computer programs, biological neuronal networks, word association networks, and files on the Internet. Twenty-two of the 28 (78.6%) involved a graph comprised of words or phrases. Fifteen of the 28 (53.6%) mentioned evidence of small-world characteristics in the network investigated. Eleven (39.3%) reported a scale-free topology, which tends to have a similar appearance when examined at varying scales. The results of this review indicate that networks generated from natural language have topological properties common to other natural phenomena. It has not yet been determined whether artificial human-curated terminology systems in biomedicine share these properties. Large network analysis methods have potential application in a variety of areas of informatics, such as in development of controlled vocabularies and for characterizing a given domain.