{"title":"LG-ERM: An Entity-Level Ranking Mechanism for Deep Web Query","authors":"Yue Kou, Derong Shen, Ge Yu, Tiezheng Nie","doi":"10.1109/WAIM.2008.92","DOIUrl":"https://doi.org/10.1109/WAIM.2008.92","url":null,"abstract":"With the rapid growth of Web databases, it's necessary to extract and integrate large-scale data available in deep Web automatically. But current Web search engines conduct page-level ranking, which are becoming inadequate for entity-oriented vertical search. In this paper, we present an entity-level ranking mechanism called LG-ERM for deep Web query based on local scoring and global aggregation. Unlike traditional approaches, LG-ERM considers more rank influencing factors including the uncertainty of entity extraction, the style information of entities and the importance of Web sources, as well as the entity relationship. By combining local scoring and global aggregation in ranking, the query result can be more accurate and effective to meet users' needs. The experiments demonstrate the feasibility and effectiveness of the key techniques of LG-ERM.","PeriodicalId":217119,"journal":{"name":"2008 The Ninth International Conference on Web-Age Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SIMP: Efficient XML Structural Index for Multiple Query Processing","authors":"Bo Zhang, Zhihua Geng, Aoying Zhou","doi":"10.1109/WAIM.2008.25","DOIUrl":"https://doi.org/10.1109/WAIM.2008.25","url":null,"abstract":"XML indexing is an important method for accelerating query processing. Existing structural indexes suffer from the problems of redundant traversal and lack of scalability for answering multiple queries simultaneously. In this paper, we present a novel index called as SIMP, which is an efficient XML structural index for multiple query processing. We first introduce an index to cluster the concerned parts in XML document together and increase the distinction for avoiding redundant traversal. Then we come up with a method for clustering multiple queries efficiently. Based on the indexing methods for both XML document and a set of queries, we propose a novel query processing method, which employs hash operation to answer a set of queries simultaneously and efficiently. For further optimization, we use a suffix tree to explore both the prefix and suffix parts among queries. Experimental results demonstrate that SIMP brings a substantial performance improvement on query performance compared with the existing techniques.","PeriodicalId":217119,"journal":{"name":"2008 The Ninth International Conference on Web-Age Information Management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114666629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Approach to the Formal Representation of OWL-S Ontology Maintenance Requirements","authors":"Aihua Bao, L. Yao, Wei Ming Zhang, Jinping Yuan","doi":"10.1109/WAIM.2008.16","DOIUrl":"https://doi.org/10.1109/WAIM.2008.16","url":null,"abstract":"OWL-S Ontology Maintenance is an important issue in the research of semantic Web services and knowledge management. This paper analysed the differences between the maintenance of OWL-S and domain ontology evolution, and proposed an OWL-S oriented ontology maintenance framework (PROEM), in which, the formal representation method of maintenance requirements is introduced in detail. The 23 elementary changes of OWL-S ontology are enumerated, and the composite change that can combine elementary changes in and-or way is also introduced in this paper. Then, the relation matrix is proposed to analyse the relations between elementary changes, and an optimize algorithm of elementary changes list is also provided, so that, the redundant changes in the list can be deleted, and the efficiency of OWL-S ontology maintenance will be improved.","PeriodicalId":217119,"journal":{"name":"2008 The Ninth International Conference on Web-Age Information Management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117312035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Outlier Detection for Transaction Databases Using Association Rules","authors":"K. Narita, H. Kitagawa","doi":"10.1109/WAIM.2008.58","DOIUrl":"https://doi.org/10.1109/WAIM.2008.58","url":null,"abstract":"Outlier detection, a data mining technique to detect rare events, deviant objects, and exceptions from data, has drawn increasing attention in recent years. Much existing research targets record data constructed with numerical attributes or a set of points having numeric values. However, very few studies have attempted to detect outliers from data having items. We focus on transaction data and propose a framework for detecting outlier transactions that behave abnormally compared to others. As an outlier, we are interested in a transaction t in which more items are not observed even though they should normally have a strong dependency on item sets in t. We use information of association rules with high confidence for the outlier degree calculation. In this paper, we first discuss what outliers of transactions are, and provide an outlier degree for systematically detecting outlier transactions. We also propose algorithms for efficiently detecting outlier transactions from transaction databases. We present two devices for faster detection that (i) remove redundant association rules and (ii) prune candidates of outlier transactions utilizing maximal frequent itemsets. In experiments using synthetic and real world data sets, we show that our proposal can derive enough detection accuracies and detect outlier transactions faster than a brute force algorithm.","PeriodicalId":217119,"journal":{"name":"2008 The Ninth International Conference on Web-Age Information Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130179347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}