Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献

{"title":"LSDS-IR'11: the 9th workshop on large-scale and distributed systems for information retrieval","authors":"C. Lucchese, B. B. Cambazoglu","doi":"10.1145/2063576.2064054","DOIUrl":"https://doi.org/10.1145/2063576.2064054","url":null,"abstract":"The growth of the Web and user bases lead to important performance problems for large-scale Web search engines. The LSDS- IR '11 workshop focuses on research contributions related to the scalability and efficiency of distributed information retrieval (IR) systems. The workshop also encourages contributions that propose different ways of leveraging diversity and multiplicity of resources available in distributed systems. More specifically, we are interested in novel applications, models, and architectures that deal with efficiency and scalability of distributed IR systems.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"24 1","pages":"2643-2644"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72796219","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":"e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning","authors":"Xiangjun Dong, Z. Zheng, Longbing Cao, Yanchang Zhao, Chengqi Zhang, Jinjiu Li, Wei Wei, Yuming Ou","doi":"10.1145/2063576.2063695","DOIUrl":"https://doi.org/10.1145/2063576.2063695","url":null,"abstract":"Mining Negative Sequential Patterns (NSP) is much more challenging than mining Positive Sequential Patterns (PSP) due to the high computational complexity and huge search space required in calculating Negative Sequential Candidates (NSC). Very few approaches are available for mining NSP, which mainly rely on re-scanning databases after identifying PSP. As a result, they are very inefficient. In this paper, we propose an efficient algorithm for mining NSP, called e-NSP, which mines for NSP by only involving the identified PSP, without re-scanning databases. First, negative containment is defined to determine whether or not a data sequence contains a negative sequence. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. The supports of NSC are then calculated based only on the corresponding PSP. Finally, a simple but efficient approach is proposed to generate NSC. With e-NSP, mining NSP does not require additional database scans, and the existing PSP mining algorithms can be integrated into e-NSP to mine for NSP efficiently. e-NSP is compared with two currently available NSP mining algorithms on 14 synthetic and real-life datasets. Intensive experiments show that e-NSP takes as little as 3% of the runtime of the baseline approaches and is applicable for efficient mining of NSP in large datasets.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"18 1","pages":"825-830"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72666038","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":"Legal document clustering with built-in topic segmentation","authors":"Qiang Lu, Jack G. Conrad, Khalid Al-Kofahi, William Keenan","doi":"10.1145/2063576.2063636","DOIUrl":"https://doi.org/10.1145/2063576.2063636","url":null,"abstract":"Clustering is a useful tool for helping users navigate, summarize, and organize large quantities of textual documents available on the Internet, in news sources, and in digital libraries. A variety of clustering methods have also been applied to the legal domain, with various degrees of success. Some unique characteristics of legal content as well as the nature of the legal domain present a number of challenges. For example, legal documents are often multi-topical, contain carefully crafted, professional, domain-specific language, and possess a broad and unevenly distributed coverage of legal issues. Moreover, unlike widely accessible documents on the Internet, where search and categorization services are generally free, the legal profession is still largely a fee-for-service field that makes the quality (e.g., in terms of both recall and precision) a key differentiator of provided services. This paper introduces a classification-based recursive soft clustering algorithm with built-in topic segmentation. The algorithm leverages existing legal document metadata such as topical classifications, document citations, and click stream data from user behavior databases, into a comprehensive clustering framework. Techniques associated with the algorithm have been applied successfully to very large databases of legal documents, which include judicial opinions, statutes, regulations, administrative materials and analytical documents. Extensive evaluations were conducted to determine the efficiency and effectiveness of the proposed algorithm. Subsequent evaluations conducted by legal domain experts have demonstrated that the quality of the resulting clusters based upon this algorithm is similar to those created by domain experts.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"584 2","pages":"383-392"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/2063576.2063636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72418761","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":"Inferring query aspects from reformulations using clustering","authors":"Van Dang, Xiaobing Xue, W. Bruce Croft","doi":"10.1145/2063576.2063904","DOIUrl":"https://doi.org/10.1145/2063576.2063904","url":null,"abstract":"When the information need is not clear from the user query, a good strategy would be to return documents that cover as many aspects of the query as possible. To do this, the possible aspects of the query need to be automatically identified. In this paper, we propose to do this by clustering reformulated queries generated from publicly available resources and using each cluster to represent an aspect of the query. Our results show that the automatically generated reformulations for the TREC Web Track queries match up quite well with actual sub-topics of these queries identified by TREC experts. Moreover, agglomerative clustering using query-to-query similarity based on co-occurrence in text passages can provide clusters of high quality that potentially can be used to identify aspects.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"101 1","pages":"2117-2120"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79385534","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":"PDFMeat: managing publications on the semantic desktop","authors":"D. Aumüller, E. Rahm","doi":"10.1145/2063576.2064020","DOIUrl":"https://doi.org/10.1145/2063576.2064020","url":null,"abstract":"Researchers maintain bibliographies and extensive sets of PDF files of scholarly publications on their desktop. The lack of proper metadata of downloaded PDFs makes this task a tedious one. With PDFMeat we present a solution to automatically determine publication metadata for scholarly papers within the user's desktop environment and link the metadata to the files. PDFMeat effectively matches local full texts to an online repository. In an evaluation for more than 2.000 diverse PDF files it worked highly reliable and showed excellent accuracy of up to 98 percent. We demonstrate PDFMeat for different sets of papers, highlighting the semantic integration and use of the retrieved metadata within the file browser of the desktop environment.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"10 1","pages":"2565-2568"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79426372","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":"DIGRank: using global degree to facilitate ranking in an incomplete graph","authors":"Xiang Niu, Lusong Li, Ke Xu","doi":"10.1145/2063576.2063950","DOIUrl":"https://doi.org/10.1145/2063576.2063950","url":null,"abstract":"PageRank has been broadly applied to get credible rank sequences of nodes in many networks such as the web, citation networks, or online social networks. However, in the real world, it is usually hard to ascertain a complete structure of a network, particularly a large-scale one. Some researchers have begun to explore how to get a relatively accurate rank more efficiently. They have proposed some local approximation methods, which are especially designed for quickly estimating the PageRank value of a new node, after it is just added to the network. Yet, these local approximation methods rely on the link server too much, and it is difficult to use them to estimate rank sequences of nodes in a group. So we propose a new method called DIGRank, which uses global Degree to facilitate Ranking in an Incomplete Graph and which takes into account the frequent need for applications to rank users in a community, retrieve pages in a particular area, or mine nodes in a fractional or limited network. Based on experiments in small-world and scale-free networks generated by models, the DIGRank method performs better than other local estimation methods on ranking nodes in a given subgraph. In the models, it tends to perform best in graphs that have low average shortest path length, high average degree, or weak community structure. Besides, compared with an local PageRank and an advanced local approximation method, it significantly reduces the computational cost and error rate.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"21 1","pages":"2297-2300"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79632102","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":"I/O-efficient algorithms for answering pattern-based aggregate queries in a sequence OLAP system","authors":"C. Chui, B. Kao, Eric Lo, Reynold Cheng","doi":"10.1145/2063576.2063812","DOIUrl":"https://doi.org/10.1145/2063576.2063812","url":null,"abstract":"Many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. In recent years, the concept of Sequence OLAP (S-OLAP) has been proposed. The biggest distinguishing feature of SOLAP from traditional OLAP is that data sequences managed by an S-OLAP system are characterized by the subsequence/substring patterns they possess. An S-OLAP system thus supports pattern-based grouping and aggregation. Conceptually, an S-OLAP system maintains a sequence data cube which is composed of sequence cuboids. Each sequence cuboid presents the answer of a pattern-based aggregate (PBA) query. This paper focuses on the I/O aspects of evaluating PBA queries. We study the problems of joining plan selection and execution planning, which are the core issues in the design of I/O-efficient cuboid materialization algorithms. Through an empirical study, we show that our algorithms lead to a very I/O-efficient strategy for sequence cuboid materialization.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"6 1","pages":"1619-1628"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84890236","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":"Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs","authors":"Kazufumi Watanabe, Masanao Ochi, Makoto Okabe, R. Onai","doi":"10.1145/2063576.2064014","DOIUrl":"https://doi.org/10.1145/2063576.2064014","url":null,"abstract":"We propose a system for detecting local events in the real-world using geolocation information from microblog documents. A local event happens when people with a common purpose gather at the same time and place. To detect such an event, we identify a group of Twitter documents describing the same theme that were generated within a short time and a small geographic area. Timestamps and geotags are useful for finding such documents, but only 0.7% of documents are geotagged and not sufficient for this purpose. Therefore, we propose an automatic geotagging method that identifies the location of non-geotagged documents. Our geotagging method successfully increased the number of geographic groups by about 115 times. For each group of documents, we extract co-occurring terms to identify its theme and determine whether it is about an event. We subjectively evaluated the precision of our detected local events and found that it had 25.5% accuracy. These results demonstrate that our system can detect local events that are difficult to identify using existing event detection methods. A user can interactively specify the size of a desired event by manipulating the parameters of date, area size, and the minimum number of Twitter users associated with the location. Our system allows users to enjoy the novel experience of finding a local event happening near their current location in real time.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"44 1","pages":"2541-2544"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80265141","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":"Object ranking","authors":"R. V. Zwol, Srinivas Vadrevu","doi":"10.1145/2063576.2064038","DOIUrl":"https://doi.org/10.1145/2063576.2064038","url":null,"abstract":"Object ranking is an emerging discipline within information retrieval that is concerned with the ranking of objects, e.g. named entities and their attributes, in context of given a user query, or application. In this tutorial we will address the different aspects involved when building an object ranking system. We will present the state-of-the-art research in object ranking, as well as going into detail about our hands-on experiences when designing and developing the system for object ranking as it is in production at Yahoo! today. This allows for a unique mixture of research and development that will give the participants in-depth insights into the problem of object ranking.\u0000 The focus of current Web search engines is to retrieve relevant documents on the Web, and more precisely documents that match with the query intent of the user. Some users are looking for specific information, while other just want to access rich media content (images, videos, etc.) or explore a topic. In the latter scenario, users do not have a fixed or pre-determined information need, but are using the search engine to discover information related to a particular object of interest. In this scenario one can say that the user is in a exploratory mode.\u0000 To support users in their exploratory search the search engines are offering semantic search suggestions. In this tutorial, we will present a generic framework for ranking related objects. This framework ranks related entities according to two dimensions: a lateral dimension and a faceted dimension. In the lateral dimension, related entities are of the same nature as the entity queried (e.g. Barcelona and Madrid, or Angelina Jolie and Jessica Alba). In the faceted dimension, related entities are usually not of the same type as the queried entity, and refer to a specific aspect of the queried entity (e.g. Jennifer Aniston and the tvshow Friends).\u0000 In this tutorial we will describe the process of building a Web-scale object ranking system. In particular we will address the construction of a knowledge base that forms the basis for the object ranking, and the generation of ranking features using external sources such as search engine query logs, photo annotations in Flickr, and tweets on Twitter. Next, we will discuss machine learned ranking models using an ensemble of pair-wise preference models, and address various aspects of object ranking, including multi-media extensions, vertical solutions, attribute-aware ranking, and the importance of freshness. Last but not least, we will address the evaluation methodologies involved to tune the performance of Web-scale object ranking strategies.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"4 1","pages":"2613-2614"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80520048","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":"The where in the tweet","authors":"Wen Li, P. Serdyukov, A. D. Vries, Carsten Eickhoff, M. Larson","doi":"10.1145/2063576.2063995","DOIUrl":"https://doi.org/10.1145/2063576.2063995","url":null,"abstract":"Twitter is a widely-used social networking service which enables its users to post text-based messages, so-called tweets. POI tags on tweets can show more human-readable high-level information about a place rather than just a pair of coordinates. In this paper, we attempt to predict the POI tag of a tweet based on its textual content and time of posting. Potential applications include accurate positioning when GPS devices fail and disambiguating places located near each other. We consider this task as a ranking problem, i.e., we try to rank a set of candidate POIs according to a tweet by using language and time models. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. From our experiments, we find that users indeed leak some information about their accurate locations in their tweets.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"64 1","pages":"2473-2476"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80342061","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}