Bibliometric cartography of information retrieval research by using co-word analysis
Information Processing and Management: an International Journal
The Size of the Giant Component of a Random Graph with a Given Degree Sequence
Combinatorics, Probability and Computing
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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Domain knowledge map construction as an important method can describe the significant characters of a selected domain. In this research, we will address three problems for knowledge graph generation. Firstly, this paper will construct domain (core journals and conference proceedings) knowledge and domain context (domain citation) knowledge graphs, and propose a novel method to integrate those graphs. Secondly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Last but not least, the paper will propose an innovative method to evaluate the accuracy and coverage of knowledge graphs based on training keyword oriented Labeled-LDA model and validate different domain or domain context graphs.