Yutaka Matsuo
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Publications by Yutaka Matsuo (bibliography)
Bollegala, Danushka T., Matsuo, Yutaka and Ishizuka, Mitsuru (2009): Measuring the similarity between implicit semantic relations from the web. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 651-660. Available online
“Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition). The person is interested in retrieving other such pairs with similar relations (e.g. Microsoft, Powerset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words. We propose a relational similarity measure, using a Web search engine, to compute the similarity between semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity between semantic relations using a metric learning approach. We evaluate the proposed method in two tasks: classifying semantic relations between named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time taken by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with an SAT score of 51%.”
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Matsuo, Yutaka and Yamamoto, Hikaru (2009): Community gravity: measuring bidirectional effects by trust and rating on online social networks. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 751-760. Available online
“Several attempts have been made to analyze customer behavior on online E-commerce sites. Some studies particularly emphasize the social networks of customers. Users' reviews and ratings of a product exert effects on other consumers' purchasing behavior. Whether a user refers to other users' ratings depends on the trust accorded by a user to the reviewer. On the other hand, the trust that is felt by a user for another user correlates with the similarity of two users' ratings. This bidirectional interaction that involves trust and rating is an important aspect of understanding consumer behavior in online communities because it suggests clustering of similar users and the evolution of strong communities. This paper presents a theoretical model along with analyses of an actual online E-commerce site. We analyzed a large community site in Japan: @cosme. The noteworthy characteristics of @cosme are that users can bookmark their trusted users; in addition, they can post their own ratings of products, which facilitates our analyses of the ratings' bidirectional effects on trust and ratings. We describe an overview of the data in @cosme, analyses of effects from trust to rating and vice versa, and our proposition of a measure of community gravity, which measures how strongly a user might be attracted to a community. Our study is based on the @cosme dataset in addition to the Epinions dataset. It elucidates important insights and proposes a potentially important measure for mining online social networks.”
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Yan, Yulan, Matsuo, Yutaka, Ishizuka, Mitsuru and Yokoi, Toshio (2008): Relation Classification for Semantic Structure Annotation of Text. In: 2008 IEEE / WIC / ACM International Conference on Web Intelligence WI 2008 9-12 December, 2008, Sydney, NSW, Australia. pp. 377-380. Available online
Oka, Mizuki and Matsuo, Yutaka (2008): Mining Scholarly Semantic Networks from the Web. In: IV 2008 - 12th International Conference on Information Visualisation 8-11 July, 2008, London, UK. pp. 349-355. Available online
Bollegala, Danushka, Honma, Taiki, Matsuo, Yutaka and Ishizuka, Mitsuru (2008): Mining for personal name aliases on the web. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1107-1108. Available online
“We propose a novel approach to find aliases of a given name from the web. We exploit a set of known names and their aliases as training data and extract lexical patterns that convey information related to aliases of names from text snippets returned by a web search engine. The patterns are then used to find candidate aliases of a given name. We use anchor texts and hyperlinks to design a word co-occurrence model and define numerous ranking scores to evaluate the association between a name and its candidate aliases. The proposed method outperforms numerous baselines and previous work on alias extraction on a dataset of personal names, achieving a statistically significant mean reciprocal rank of 0.6718. Moreover, the aliases extracted using the proposed method improve recall by 20% in a relation-detection task.”
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Bollegala, Danushka, Matsuo, Yutaka and Ishizuka, Mitsuru (2007): Measuring semantic similarity between words using web search engines. In: Proceedings of the 2007 International Conference on the World Wide Web 2007. pp. 757-766. Available online
“Semantic similarity measures play important roles in information retrieval and Natural Language Processing. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these applications, robustly measuring semantic similarity between two words (or entities) remains a challenging task. We propose a robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. The proposed method exploits page counts and text snippets returned by a Web search engine. We define various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets. These different similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similarity measures by a wide margin, achieving a correlation coefficient of 0:834. Moreover, the proposed semantic similarity measure significantly improves the accuracy (F-measure of 0:78) in a community mining task, and in an entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content.”
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Yang, Jie, Matsuo, Yutaka and Ishizuka, Mitsuru (2007): An Augmented Tagging Scheme with Triple Tagging and Collective Filtering. In: 2007 IEEE / WIC / ACM International Conference on Web Intelligence WI 2007 2-5 November, 2007, Silicon Valley, CA, USA. pp. 35-38. Available online
Hope, Tom, Hamasaki, Masahiro, Matsuo, Yutaka, Nakamura, Yoshiyuki, Fujimura, Noriyuki and Nishimura, Takuichi (2006): Doing Community: Co-construction of Meaning and Use with Interactive Information Kiosks. In: Dourish, Paul and Friday, Adrian (eds.) UbiComp 2006 Ubiquitous Computing - 8th International Conference September 17-21, 2006, Orange County, CA, USA. pp. 387-403. Available online
Matsuo, Yutaka, Mori, Junichiro, Hamasaki, Masahiro, Ishida, Keisuke, Nishimura, Takuichi, Takeda, Hideaki, Hasida, Kôiti and Ishizuka, Mitsuru (2006): POLYPHONET: an advanced social network extraction system from the web. In: Proceedings of the 2006 International Conference on the World Wide Web 2006. pp. 397-406. Available online
“Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents. Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.”
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Mori, Junichiro, Sugiyama, Tatsuhiko and Matsuo, Yutaka (2005): Real-world oriented information sharing using social networks. In: GROUP05: International Conference on Supporting Group Work November 6-9, 2005, Sanibel Island, Florida, USA. pp. 81-84. Available online
“While users disseminate various information in the open and widely distributed environment of the Semantic Web, determination of who shares access to particular information is at the center of looming privacy concerns. We propose a real-world-oriented information sharing system that uses social networks. The system automatically obtains users\' social relationships by mining various external sources. It also enables users to analyze their social networks to provide awareness of the information dissemination process. Users can determine who has access to particular information based on the social relationships and network analysis.”
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Matsuo, Yutaka, Tomobe, Hironori, Hasida, Kôiti and Ishizuka, Mitsuru (2003): Mining Social Network of Conference Participants from the Web. In: 2003 IEEE / WIC International Conference on Web Intelligence - WI 2003 13-17 October, 2003, Halifax, Canada. pp. 190-193. Available online
Matsuo, Yutaka (2003): Word Weighting Based on User's Browsing History. In: Brusilovsky, Peter, Corbett, Albert T. and Rosis, Fiorella De (eds.) User Modeling 2003 - 9th International Conference - UM 2003 June 22-26, 2003, Johnstown, PA, USA. pp. 35-44. Available online
Ohsawa, Yukio, Soma, Hirotaka, Matsuo, Yutaka, Matsumura, Naohiro and Usui, Masaki (2002): Featuring web communities based on word co-occurrence structure of communications. In: Proceedings of the 2002 International Conference on the World Wide Web 2002. pp. 736-742. Available online
“Textual communication in message boards is analyzed for classifying Web communities. We present a communication-content based generalization of an existing business-oriented classification of Web communities, using KeyGraph, a method for visualizing the co-occurrence relations between words and word clusters in text. Here, the text in a message board is analyzed with KeyGraph, and the structure obtained is shown to reflect the essence of the content-flow. The relation of this content-flow with participants' interests is then formalized. Three structure-features of relations between participants and words, determining the type of the community, are shown to be computed and visualized: (1) centralization (2) context coherence and (3) creative decisions. This helps in surveying the essence of a community, e.g. whether the community creates useful knowledge, how easy it is to join the community, and whether/why the community is good for making commercial advertisement.”
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Matsuo, Yutaka, Ohsawa, Yukio and Ishizuka, Mitsuru (2001): Average-Clicks: A New Measure of Distance on the World Wide Web. In: Zhong, Ning, Yao, Yiyu, Liu, Jiming and Ohsuga, Setsuo (eds.) Web Intelligence Research and Development - First Asia-Pacific Conference - WI 2001 October 23-26, 2001, Maebashi City, Japan. pp. 106-114. Available online
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14 Feb 2010: Enabled abstracts to be shown on Yutaka Matsuo's author page.09 Jul 2009: Author was edited 09 Jul 2009: Author was edited
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