Concept Extraction based on Association Linked Network
Text keywords at different semantic levels have different semantic representation abilities. Although words have been organized by semantic dictionaries (e.g. WordNet) with exact semantics, the dictionaries can not be constructed automatically by machine and there are still many words which are not included in the dictionaries. This paper proposes a novel method to automatically extract keywords of higher semantic level which named concept. According to the Association Linked Network (ALN) of webpages, the ALN of keywords (kALN) is constructed first which holds the keywords of a domain and the relations among these keywords. By analyzing graph characteristics of kALN, keywords are grouped into communities. Then drawing on Entropy and Mutual Information, concepts are extracted from each kALN community. Experimental results show that the proposed method of concept extraction is acceptable in accuracy and complexity.
Xiao Wei Xiangfeng Luo
School of Computing Engineering and Science, High Performance Computing Center,Shanghai University. School of Computing Engineering and Science, High Performance Computing Center,Shanghai University.
国际会议
Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)
宁波
英文
42-49
2010-11-01（万方平台首次上网日期，不代表论文的发表时间）