Yasuhito Asano

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Yasuhito Asano is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Mining knowledge on relationships between objects from the web Content-based image retrieval
Knowledge retrieval
Relationships between objects
Data mining
IEICE Transactions on Information and Systems English 2014 How do global warming and agriculture influence each other? It is possible to answer the question by searching knowledge about the relationship between global warming and agriculture. As exemplified by this question, strong demands exist for searching relationships between objects. Mining knowledge about relationships on Wikipedia has been studied. However, it is desired to search more diverse knowledge about relationships on theWeb. By utilizing the objects constituting relationships mined from Wikipedia, we propose a new method to search images with surrounding text that include knowledge about relationships on the Web. Experimental results show that our method is effective and applicable in searching knowledge about relationships. We also construct a relationship search system named "Enishi" based on the proposed new method. Enishi supplies a wealth of diverse knowledge including images with surrounding text to help users to understand relationships deeply, by complementarily utilizing knowledge from Wikipedia and the Web. Copyright 0 0
A generalized flow-based method for analysis of implicit relationships on wikipedia Generalized flow
Link analysis
Relationship
Data mining
IEEE Transactions on Knowledge and Data Engineering English 2013 We focus on measuring relationships between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relationships between two objects exist: in Wikipedia, an explicit relationship is represented by a single link between the two pages for the objects, and an implicit relationship is represented by a link structure containing the two pages. Some of the previously proposed methods for measuring relationships are cohesion-based methods, which underestimate objects having high degrees, although such objects could be important in constituting relationships in Wikipedia. The other methods are inadequate for measuring implicit relationships because they use only one or two of the following three important factors: distance, connectivity, and cocitation. We propose a new method using a generalized maximum flow which reflects all the three factors and does not underestimate objects having high degree. We confirm through experiments that our method can measure the strength of a relationship more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relationship. We explain that mining elucidatory objects would open a novel way to deeply understand a relationship. 0 0
How the web can help Wikipedia: a study on information complementation of Wikipedia by the web Complementary information retrieval
Information aggregation
Topic modeling
ICUIMC English 2012 0 0
How the web can help wikipedia: A study on information complementation of wikipedia by the web Complementary information retrieval
Information aggregation
Topic modeling
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12 English 2012 With the huge amount of data on the Web, looking for desired information can be a time consuming task. Wikipedia is a very helpful tool as it is the largest most popular general reference site on the internet. Most search engines rank Wikipedia pages among the top listed results. However, because many articles on Wikipedia are manually updated by users, there are several articles that lack information and need to be upgraded. Those lacking information can sometimes be found on the web. Uprooting this information from the web will involve a time consuming process of reading, analyzing and summarizing the information for the user. In order to support the user search process and help Wikipedia contributors in the updating process of articles, we propose a method of finding valuable complementary information on the web. Experiments showed that our method was quite effective in retrieving important complementary information from the web pages. 0 0
Towards improving wikipedia as an image-rich encyclopaedia through analyzing appropriateness of images for an article Multimedia information
Relation
Wikipedia
APWeb English 2011 0 0
Analysis of implicit relations on wikipedia: Measuring strength through mining elucidatory objects Generalized flow
Link analysis
Relation
Data mining
Lecture Notes in Computer Science English 2010 We focus on measuring relations between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relations between two objects exist: in Wikipedia, an explicit relation is represented by a single link between the two pages for the objects, and an implicit relation is represented by a link structure containing the two pages. Previously proposed methods are inadequate for measuring implicit relations because they use only one or two of the following three important factors: distance, connectivity, and co-citation. We propose a new method reflecting all the three factors by using a generalized maximum flow. We confirm that our method can measure the strength of a relation more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relation. We explain that mining elucidatory objects opens a novel way to deeply understand a relation. 0 0
Analysis of implicit relations on wikipedia: measuring strength through mining elucidatory objects Generalized flow
Link analysis
Relation
Data mining
DASFAA English 2010 0 0
Enishi: Searching knowledge about relations by complementarily utilizing wikipedia and the web Knowledge retrieval
Relation
Data mining
Lecture Notes in Computer Science English 2010 How global warming and agriculture mutually influence each other? It is possible to answer the question by searching knowledge about the relation between global warming and agriculture. As exemplified by this question, strong demands exist for searching relations between objects. However, methods or systems for searching relations are not well studied. In this paper, we propose a relation search system named "Enishi." Enishi supplies a wealth of diverse multimedia information for deep understanding of relations between two objects by complementarily utilizing knowledge from Wikipedia and the Web. Enishi first mines elucidatory objects constituting relations between two objects from Wikipedia. We then propose new approaches for Enishi to search more multimedia information about relations on the Web using elucidatory objects. Finally, we confirm through experiments that our new methods can search useful information from the Web for deep understanding of relations. 0 0
Enishi: searching knowledge about relations by complementarily utilizing wikipedia and the web Knowledge retrieval
Relation
Data mining
WISE English 2010 0 0
Mining and explaining relationships in Wikipedia Generalized max-flow
Link analysis
Relationship
Data mining
Lecture Notes in Computer Science English 2010 Mining and explaining relationships between objects are challenging tasks in the field of knowledge search. We propose a new approach for the tasks using disjoint paths formed by links in Wikipedia. To realizing this approach, we propose a naive and a generalized flow based method, and a technique of avoiding flow confluences for forcing a generalized flow to be disjoint as possible. We also apply the approach to classification of relationships. Our experiments reveal that the generalized flow based method can mine many disjoint paths important for a relationship, and the classification is effective for explaining relationships. 0 0
Mining and explaining relationships in wikipedia Generalized max-flow
Link analysis
Relationship
Data mining
DEXA English 2010 0 0