Masatoshi Yoshikawa

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Masatoshi Yoshikawa 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
Assessing quality score of wikipedia articles using mutual evaluation of editors and texts Edit history
Peer review
Quality
Vandalism
Wikipedia
International Conference on Information and Knowledge Management, Proceedings English 2013 In this paper, we propose a method for assessing quality scores of Wikipedia articles by mutually evaluating editors and texts. Survival ratio based approach is a major approach to assessing article quality. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality, because poor quality texts have a high probability of being deleted by editors. However, many vandals, low quality editors, delete good quality texts frequently, which improperly decreases the survival ratios of good quality texts. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality score for calculating text quality score, and decrease the impact on text quality by vandals. Using this improvement, the accuracy of the text quality score should be improved. However, an inherent problem with this idea is that the editor quality scores are calculated by the text quality scores. To solve this problem, we mutually calculate the editor and text quality scores until they converge. In this paper, we prove that the text quality score converges. We did our experimental evaluation, and confirmed that our proposed method could accurately assess the text quality scores. Copyright is held by the owner/author(s). 0 0
Mutual Evaluation of Editors and Texts for Assessing Quality of Wikipedia Articles Wikipedia
Quality
Peer review
Edit history
Link analysis
WikiSym English August 2012 In this paper, we propose a method to identify good quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing article quality is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality. This approach assumes that poor quality texts are deleted by editors with high possibility. However, many vandals delete good quality texts frequently, then the survival ratios of good quality texts are improperly decreased by vandals. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality for calculating text quality, and decrease the impacts on text qualities by the vandals who has low quality. Using this improvement, the accuracy of the text quality should be improved. However, an inherent problem of this idea is that the editor qualities are calculated by the text qualities. To solve this problem, we mutually calculate the editor and text qualities until they converge. We did our experimental evaluation, and we confirmed that the proposed method could accurately assess the text qualities. 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
Mutual evaluation of editors and texts for assessing quality of Wikipedia articles Edit history
Link analysis
Peer review
Quality
Wikipedia
WikiSym 2012 English 2012 In this paper, we propose a method to identify good quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing article quality is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality. This approach assumes that poor quality texts are deleted by editors with high possibility. However, many vandals delete good quality texts frequently, then the survival ratios of good quality texts are improperly decreased by vandals. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality for calculating text quality, and decrease the impacts on text qualities by the vandals who has low quality. Using this improvement, the accuracy of the text quality should be improved. However, an inherent problem of this idea is that the editor qualities are calculated by the text qualities. To solve this problem, we mutually calculate the editor and text qualities until they converge. We did our experimental evaluation, and we confirmed that the proposed method could accurately assess the text qualities. 0 0
QualityRank: Assessing quality of wikipedia articles by mutually evaluating editors and texts Edit history
Link analysis
Peer review
Quality
HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media English 2012 In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing articles using edit history is a text survival ratio based approach. However, the problem is that many high-quality articles are identified as low quality, because many vandals delete high-quality texts, then the survival ratios of high-quality texts are decreased by vandals. Our approach's strongest point is its resistance to vandalism. Using our method, if we calculate text quality values using editor quality values, vandals do not affect any quality values of the other editors, then the accuracy of text quality values should improve. However, the problem is that editor quality values are calculated by text quality values, and text quality values are calculated by editor quality values. To solve this problem, we mutually calculate editor and text quality values until they converge. Using this method, we can calculate a quality value of a text that takes into consideration that of its editors. From experimental evaluation, we confirmed that the proposed method can improve the accuracy of quality values for articles. Copyright 2012 ACM. 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
A retrieval method for earth science data based on integrated use of Wikipedia and domain ontology Lecture Notes in Computer Science English 2010 Due to the recent advancement in observation technologies and progress in information technologies, the total amount of earth science data has increased at an explosive pace. However, it is not easy to search and discover earth science data because earth science requires high degree of expertness. In this paper, we propose a retrieval method for earth science data which can be used by non-experts such as scientists from other field, or students interested in earth science. In order to retrieve relevant data sets from a query, which may not include technical terminologies, supplementing terms are extracted by utilizing knowledge bases; Wikipedia and domain ontology. We evaluated our method using actual earth science data. The data, the queries, and the relevance assessments for our experiments were made by the researchers of earth science. The results of our experiments show that our method has achieved good recall and precision. 0 0
A retrieval method for earth science data based on integrated use of wikipedia and domain ontology DEXA English 2010 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
Classifying web pages by using knowledge bases for entity retrieval Lecture Notes in Computer Science English 2009 In this paper, we propose a novel method to classify Web pages by using knowledge bases for entity search, which is a kind of typical Web search for information related to a person, location or organization. First, we map a Web page to entities according to the similarities between the page and the entities. Various methods for computing such similarity are applied. For example, we can compute the similarity between a given page and a Wikipedia article describing a certain entity. The frequency of an entity appearing in the page is another factor used in computing the similarity. Second, we construct a directed acyclic graph, named PEC graph, based on the relations among Web pages, entities, and categories, by referring to YAGO, a knowledge base built on Wikipedia and WordNet. Finally, by analyzing the PEC graph, we classify Web pages into categories. The results of some preliminary experiments validate the methods proposed in this paper. 0 0