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Proceedings of the 18th ACM conference on Information and knowledge management最新文献

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OSSOBOOK: database and knowledgemanagement techniques for archaeozoology OSSOBOOK:数据库和知识管理技术的考古学
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646318
H. Kriegel, Peer Kröger, Henriette Obermaier, J. Peters, M. Renz, C. V. D. Meijden
{"title":"OSSOBOOK: database and knowledgemanagement techniques for archaeozoology","authors":"H. Kriegel, Peer Kröger, Henriette Obermaier, J. Peters, M. Renz, C. V. D. Meijden","doi":"10.1145/1645953.1646318","DOIUrl":"https://doi.org/10.1145/1645953.1646318","url":null,"abstract":"This demo describes the OSSOBOOK database system developed for archaeozoology applications providing data storage, data retrieval, and data mining facilities. It shows a case study of integrating state-of-the-art database concepts like intermittently synchronized database system as well as concepts of information retrieval and knowledge representation like similarity search and data mining in order to provide a comprehensive system for an interesting application domain.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116335335","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}
引用次数: 3
Web search result summarization: title selection algorithms and user satisfaction 网络搜索结果摘要:标题选择算法和用户满意度
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646177
T. Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawrence Wai
{"title":"Web search result summarization: title selection algorithms and user satisfaction","authors":"T. Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawrence Wai","doi":"10.1145/1645953.1646177","DOIUrl":"https://doi.org/10.1145/1645953.1646177","url":null,"abstract":"Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user's search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compare two modeling approaches: i) using absolute editorial judgments and ii) using pairwise preference judgments. We find that the pairwise modeling approach gives better results in terms of three offline metrics. We present results of our models in four regions. We also describe a hybrid user satisfaction evaluation process -- search success -- that combines page relevance and user click behavior, and show that our machine-learned algorithm improves in search success.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127606250","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}
引用次数: 8
Session details: KM advance mining techniques 会议详情:KM先进的挖掘技术
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/3261224
A. Fu
{"title":"Session details: KM advance mining techniques","authors":"A. Fu","doi":"10.1145/3261224","DOIUrl":"https://doi.org/10.1145/3261224","url":null,"abstract":"","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126226805","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}
引用次数: 0
Learning to rank graphs for online similar graph search 学习为在线相似图搜索排序图
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646252
Bingjun Sun, P. Mitra, C. Lee Giles
{"title":"Learning to rank graphs for online similar graph search","authors":"Bingjun Sun, P. Mitra, C. Lee Giles","doi":"10.1145/1645953.1646252","DOIUrl":"https://doi.org/10.1145/1645953.1646252","url":null,"abstract":"Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chemoinformatics, have used the maximum common edge subgraph (MCEG) to compute the graph similarity. This approach is prohibitively slow for real-time queries. In this work, we propose an algorithm that extracts and indexes subgraph features from a graph dataset. It computes the similarity of graphs using a linear graph kernel based on feature weights learned offline from a training set generated using MCEG. We show empirically that our proposed algorithm of learning to rank graphs can achieve higher normalized discounted cumulative gain compared with existing optimal methods based on MCEG. The running time of our algorithm is orders of magnitude faster than these existing methods.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405513","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}
引用次数: 3
Automatic web data extraction using tree alignment 使用树对齐的自动web数据提取
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646194
Yingju Xia, Hao Yu, Shu Zhang
{"title":"Automatic web data extraction using tree alignment","authors":"Yingju Xia, Hao Yu, Shu Zhang","doi":"10.1145/1645953.1646194","DOIUrl":"https://doi.org/10.1145/1645953.1646194","url":null,"abstract":"This paper investigates the automatic extraction of data from forums, blogs and news web sites. Web pages are increasingly dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment to automatically extract data from these types of web pages. A new tree alignment algorithm is presented for determining the optimal matching structure of the input web pages. Based on the alignment, the trees are merged into one union tree whose nodes record statistical information obtained from multiple web pages. A heuristic method is employed for determining the most probable content block and the alignment algorithm detects repeating patterns on the union tree. A wrapper built on the most probable content block and the repeating patterns extracts data from web pages. Experimental results show that the method achieves high extraction accuracy and has steady performance.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128172102","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}
引用次数: 5
Session details: KM information filtering and recommender systems 会议细节:KM信息过滤和推荐系统
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/3261231
B. Kao
{"title":"Session details: KM information filtering and recommender systems","authors":"B. Kao","doi":"10.1145/3261231","DOIUrl":"https://doi.org/10.1145/3261231","url":null,"abstract":"","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122004864","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}
引用次数: 0
Efficient algorithms for approximate member extraction using signature-based inverted lists 基于签名的倒排列表的高效成员提取算法
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1645995
Jiaheng Lu, Jialong Han, Xiaofeng Meng
{"title":"Efficient algorithms for approximate member extraction using signature-based inverted lists","authors":"Jiaheng Lu, Jialong Han, Xiaofeng Meng","doi":"10.1145/1645953.1645995","DOIUrl":"https://doi.org/10.1145/1645953.1645995","url":null,"abstract":"We study the problem of approximate membership extraction (AME), i.e., how to efficiently extract substrings in a text document that approximately match some strings in a given dictionary. This problem is important in a variety of applications such as named entity recognition and data cleaning. We solve this problem in two steps. In the first step, for each substring in the text, we filter away the strings in the dictionary that are very different from the substring. In the second step, each candidate string is verified to decide whether the substring should be extracted. We develop an incremental algorithm using signature-based inverted lists to minimize the duplicate list-scan operations of overlapping windows in the text. Our experimental study of the proposed algorithms on real and synthetic datasets showed that our solutions significantly outperform existing methods in the literature.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980253","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}
引用次数: 15
(Not) yet another matcher (不是)另一个匹配器
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646165
F. Duchateau, Rémi Coletta, Zohra Bellahsene, Renée J. Miller
{"title":"(Not) yet another matcher","authors":"F. Duchateau, Rémi Coletta, Zohra Bellahsene, Renée J. Miller","doi":"10.1145/1645953.1646165","DOIUrl":"https://doi.org/10.1145/1645953.1646165","url":null,"abstract":"Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a schema matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach is able to generate the best matcher for a given scenario.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131366212","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}
引用次数: 36
Matching person names through name transformation 通过名称转换匹配人名
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646253
Jun Gong, Lidan Wang, Douglas W. Oard
{"title":"Matching person names through name transformation","authors":"Jun Gong, Lidan Wang, Douglas W. Oard","doi":"10.1145/1645953.1646253","DOIUrl":"https://doi.org/10.1145/1645953.1646253","url":null,"abstract":"Matching person names plays an important role in many applications, including bibliographic databases and indexing systems. Name variations and spelling errors make exact string matching problematic; therefore, it is useful to develop methodologies that can handle variant forms for the same named entity. In this paper, a novel person name matching model is presented. Common name variations in the English speaking world are formalized, and the concept of name transformation paths is introduced; name similarity is measured after the best transformation path has been selected. Supervised techniques are used to learn a similarity function and a decision rule. Experiments with three datasets show the method to be effective.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134265914","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}
引用次数: 9
Frequent subgraph pattern mining on uncertain graph data 不确定图数据的频繁子图模式挖掘
Proceedings of the 18th ACM conference on Information and knowledge management Pub Date : 2009-11-02 DOI: 10.1145/1645953.1646028
Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang
{"title":"Frequent subgraph pattern mining on uncertain graph data","authors":"Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang","doi":"10.1145/1645953.1646028","DOIUrl":"https://doi.org/10.1145/1645953.1646028","url":null,"abstract":"Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. The frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. An approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns. The algorithm uses an efficient approximation algorithm to determine whether a subgraph pattern can be output or not. The analytical and experimental results show that the algorithm is very efficient, accurate and scalable for large uncertain graph databases.","PeriodicalId":286251,"journal":{"name":"Proceedings of the 18th ACM conference on Information and knowledge management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134594786","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}
引用次数: 63
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