A system for visualizing and analyzing the evolution of the web with a time series of graphs
Proceedings of the sixteenth ACM conference on Hypertext and hypermedia
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
Retrieval in text collections with historic spelling using linguistic and spelling variants
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Evaluation of using human relationships on the web as information navigation paths
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Retrieval technique with the modern mongolian query on traditional mongolian text
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
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The use of text mining for obtaining new knowledge from historical documents has gained wide attention in humanities research. We propose a method of revealing and visualizing relationships between historical persons using locational information expressed in historical documents. For each person, a vector of co-occurring place names is constructed. We then clustered persons using this vector in the K-means algorithm. We conducted experiments using Hyohanki, a diary written by an aristocrat in the classical era. The result showed that the obtained clusters match well with historically meaningful groups, indicating the effectiveness of our proposed method.