Katsumi Tanaka

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Katsumi Tanaka is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships Coordinate term
Unexpected information
Proceedings of the ACM Symposium on Applied Computing English 2013 Although many studies have addressed the problem of finding Web pages seeking relevant and popular information from a query, very few have focused on the discovery of unexpected information. This paper provides and evaluates methods for discovering unexpected information for a keyword query. For example, if the user inputs "Michael Jackson," our system first discovers the unexpected related term "karate" and then returns the unexpected information "Michael Jackson is good at karate." Discovering unexpected information is useful in many situations. For example, when a user is browsing a news article on the Web, unexpected information about a person associated with the article can pique the user's interest. If a user is sightseeing or driving, providing unexpected, additional information about a building or the region is also useful. Our approach collects terms related to a keyword query and evaluates the degree of unexpectedness of each related term for the query on the basis of (i) the relationships of coordinate terms of both the keyword query and related terms, and (ii) the degree of popularity of each related term. Experimental results show that considering these two factors are effective for discovering unexpected information. Copyright 2013 ACM. 0 0
Is Wikipedia too difficult? Comparative analysis of readability of Wikipedia, simple Wikipedia and Britannica Readability
Web content analysis
Web search
ACM International Conference Proceeding Series English 2012 Readability is one of key factors determining document quality and reader's satisfaction. In this paper we analyze readability of Wikipedia, which is a popular source of information for searchers about unknown topics. Although Wikipedia articles are frequently listed by search engines on top ranks, they are often too difficult for average readers searching information about difficult queries. We examine the average readability of content in Wikipedia and compare it to the one in Simple Wikipedia and Britannica. Next, we investigate readability of selected categories in Wikipedia. Apart from standard readability measures we use some new metrics based on words' popularity and their distributions across different document genres and topics. 0 0
Evaluating significance of historical entities based on tempo-spatial impacts analysis using Wikipedia link structure Historical entities
Historical entity importance
Wikipedia structure analysis
HT English 2011 0 0
Trial integration of agricultural field sensing data Field Server
Semantic MediaWiki
Web ontology
Proceedings of the SICE Annual Conference English 2011 MetBroker, virtually integrates meteorological data from different sources and access methods, was extended a web ontology (metbroker.owl) to provide flexible data retrieval at the primary stage, and then to utilize the OWL for data integration itself. As the first trial, we used MetBroker to integrate meteorological data part from the field sensing data by Field Servers, and found that we successfully integrate Field Server data with other meteorological data. We expected that we can integrate meteorological data from other field sensing data sources to MetBroker. But we found that there is no OWL for other data obtained from field sensing data and observation data. To solve this issue, we start the second trial integration, to identify the relationships between terms used in metadata, create an extended XML schema for data exchange based on existed standard. The details of our trials are described. 0 0
Adaptive ranking of search results by considering user's comprehension Adaptive ranking
User interaction
Web search
Data mining
Proceedings of the 4th International Conference on Ubiquitous Information Management and Communication ICUIMC 10 English 2010 Given a search query, conventional Web search engines provide users with the same ranking although users' comprehension levels can be different. It is often difficult especially for non-expert users to find comprehensible Web pages from the list of search results. In this paper, we propose the method of adaptively ranking search results by considering user's comprehension level. The main issues are (a) estimating the comprehensibility of Web pages and (b) estimating the user's comprehension level. In our method, the com-prehensibility of each search result is computed by using the readability index and technical terms extracted from Wikipedia. User's comprehension level is estimated by the users' feedback about the difficulty of search results that they have viewed. We implement a prototype system and evaluate the usefulness of our approach by user experiments. 0 0
Easiest-first search: Towards comprehension-based web search Comprehensibility
Web search
Data mining
International Conference on Information and Knowledge Management, Proceedings English 2009 Although Web search engines have become information gateways to the Internet, for queries containing technical terms, search results often contain pages that are difficult to be understood by non-expert users. Therefore, re-ranking search results in a descending order of their comprehensibility should be effective for non-expert users. In our approach, the comprehensibility of Web pages is estimated considering both the document readability and the difficulty of technical terms in the domain of search queries. To extract technical terms, we exploit the domain knowledge extracted from Wikipedia. Our proposed method can be applied to general Web search engines as Wikipedia includes nearly every field of human knowledge. We demonstrate the usefulness of our approach by user experiments. Copyright 2009 ACM. 0 0
Quality Evaluation of Search Results by Typicality and Speciality of Terms Extracted from Wikipedia Search results quality
Term extraction
Term speciality
Term typicality
Data mining
DASFAA English 2009 0 0
Extracting concept hierarchy knowledge from the Web based on Property Inheritance and Aggregation Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 English 2008 Concept hierarchy knowledge, such as hyponymy and meronymy, is very important for various natural language processing systems. While WordNet and Wikipedia are being manually constructed and maintained as lexical ontologies, many researchers have tackled how to extract concept hierarchies from very large corpora of text documents such as the Web not manually but automatically. However, their methods are mostly based on lexico-syntactic patterns as not necessary but sufficient conditions of hyponymy and meronymy, so they can achieve high precision but low recall when using stricter patterns or they can achieve high recall but low precision when using looser patterns. Therefore, we need necessary conditions of hyponymy and meronymy to achieve high recall and not low precision. In this paper, not only "Property Inheritance "from a target concept to its hyponyms but also "Property Aggregation" from its hyponyms to the target concept is assumed to be necessary and sufficient conditions of hyponymy, and we propose a method to extract concept hierarchy knowledge from the Web based on property inheritance and property aggregation. 0 0