Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献_第10页

Recency ranking by diversification of result set 基于结果集多样化的近期排序

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063862

Andrey Styskin, Fedor Romanenko, F. Vorobyev, P. Serdyukov

引用次数: 20

Managing interoperability and complexity inhealth systems: MIXHS'11 workshop summary 管理卫生系统的互操作性和复杂性:MIXHS'11研讨会总结

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2064050

M. Bouamrane, C. Tao

引用次数: 3

Duplicate detection through structure optimization 通过结构优化进行重复检测

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063644

Luís Leitão, P. Calado

{"title":"Duplicate detection through structure optimization","authors":"Luís Leitão, P. Calado","doi":"10.1145/2063576.2063644","DOIUrl":"https://doi.org/10.1145/2063576.2063644","url":null,"abstract":"Detecting and eliminating duplicates in databases is a task of critical importance in many applications. Although solutions for traditional models, such as relational data, have been widely studied, recently there has been some focus on solutions for more complex hierarchical structures as, for instance, XML data. Such data presents many different challenges, among which is the issue of how to exploit the schema structure to determine if two objects are duplicates. In this paper, we argue that structure can indeed have a significant impact on the process of duplicate detection. We propose a novel method that automatically restructures database objects in order to take full advantage of the relations between its attributes. This new structure reflects the relative importance of the attributes in the database and avoids the need to perform a manual selection. To test our approach we applied it to an existing duplicate detection system. Experiments performed on several datasets show that, using the new learned structure, we consistently outperform both the results obtained with the original database structure and those obtained by letting a knowledgeable user manually choose the attributes to compare.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"12 1","pages":"443-452"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89826857","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: 19

A pairwise ranking based approach to learning with positive and unlabeled examples 一种基于两两排序的学习方法，使用正样例和未标记样例

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063675

Sundararajan Sellamanickam, Priyanka Garg, S. Keerthi

{"title":"A pairwise ranking based approach to learning with positive and unlabeled examples","authors":"Sundararajan Sellamanickam, Priyanka Garg, S. Keerthi","doi":"10.1145/2063576.2063675","DOIUrl":"https://doi.org/10.1145/2063576.2063675","url":null,"abstract":"A large fraction of binary classification problems arising in web applications are of the type where the positive class is well defined and compact while the negative class comprises everything else in the distribution for which the classifier is developed; it is hard to represent and sample from such a broad negative class. Classifiers based only on positive and unlabeled examples reduce human annotation effort significantly by removing the burden of choosing a representative set of negative examples. Various methods have been proposed in the literature for building such classifiers. Of these, the state of the art methods are Biased SVM and Elkan & Noto's methods. While these methods often work well in practice, they are computationally expensive since hyperparameter tuning is very important, particularly when the size of labeled positive examples set is small and class imbalance is high. In this paper we propose a pairwise ranking based approach to learn from positive and unlabeled examples (LPU) and we give a theoretical justification for it. We present a pairwise RankSVM (RSVM) based method for our approach. The method is simple, efficient, and its hyperparameters are easy to tune. A detailed experimental study using several benchmark datasets shows that the proposed method gives competitive classification performance compared to the mentioned state of the art methods, while training 3-10 times faster. We also propose an efficient AUC based feature selection technique in the LPU setting and demonstrate its usefulness on the datasets. To get an idea of the goodness of the LPU methods we compare them against supervised learning (SL) methods that also make use of negative examples in training. SL methods give a slightly better performance than LPU methods when there is a rich set of negative examples; however, they are inferior when the number of negative training examples is not large enough.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"7 1","pages":"663-672"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89862696","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: 28

Ranking-based processing of SQL queries 基于排名的SQL查询处理

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063614

H. Azzam, T. Roelleke, Sirvan Yahyaei

引用次数: 0

Content-driven detection of campaigns in social media 内容驱动的社交媒体活动检测

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063658

Kyumin Lee, James Caverlee, Zhiyuan Cheng, D. Sui

引用次数: 39

WikiLabel: an encyclopedic approach to labeling documents en masse 维基标签:一个百科全书式的标记文档的方法

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063961

Tadashi Nomoto

引用次数: 15

Latent feature encoding using dyadic and relational data 使用二进和关系数据的潜在特征编码

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063926

S. Ando

{"title":"Latent feature encoding using dyadic and relational data","authors":"S. Ando","doi":"10.1145/2063576.2063926","DOIUrl":"https://doi.org/10.1145/2063576.2063926","url":null,"abstract":"Learning from dyadic and relational data is a fundamental problem for IR and KDD applications in web and social media domain. Basic behaviors and characteristics of users and documents are typically described by a collection of dyads, i.e., pairs of entities. Discriminative features extracted from such data are essential in exploratory and discriminatory analyses. Relational properties of the entities reflect pair-wise similarities and their collective community structure which are also valuable for discriminative learning. A challenging aspect of learning from the relational data in many domains, is that the generative process of relational links appears noisy and is not well described by a stochastic model.\u0000 In this paper, we present a principled approach for learning discriminative features from heterogeneous sources of dyadic and relational data. We propose an information-theoretic framework called Latent Feature Encoding (LFE) which projects the entities and the links to a latent feature space in the analogy of -encoding. Projection is formalized as a maximization of the mutual information preserved in the latent features, regularized by the compression rate of encoding. The regularization is emphasized over more probable links to account for the noisiness of the observation. An empirical evaluation of the proposed method using text and social media datasets is presented. Performances in supervised and unsupervised learning tasks are compared with those of conventional latent feature extraction methods.","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":"2201-2204"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88134749","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

Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach twitter中的主题情感分析:一种基于图的标签情感分类方法

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063726

Xiaolong Wang, Furu Wei, Xiaohua Liu, M. Zhou, Ming Zhang

{"title":"Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach","authors":"Xiaolong Wang, Furu Wei, Xiaohua Liu, M. Zhou, Ming Zhang","doi":"10.1145/2063576.2063726","DOIUrl":"https://doi.org/10.1145/2063576.2063726","url":null,"abstract":"Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various interesting topics, such as brands, products or celebrities, etc. Naturally, people may anticipate an approach to receiving the common sentiment tendency towards these topics directly rather than through reading the huge amount of tweets about them. On the other side, Hashtags, starting with a symbol \"#\" ahead of keywords or phrases, are widely used in tweets as coarse-grained topics. In this paper, instead of presenting the sentiment polarity of each tweet relevant to the topic, we focus our study on hashtag-level sentiment classification. This task aims to automatically generate the overall sentiment polarity for a given hashtag in a certain time period, which markedly differs from the conventional sentence-level and document-level sentiment analysis. Our investigation illustrates that three types of information is useful to address the task, including (1) sentiment polarity of tweets containing the hashtag; (2) hashtags co-occurrence relationship and (3) the literal meaning of hashtags. Consequently, in order to incorporate the first two types of information into a classification framework where hashtags can be classified collectively, we propose a novel graph model and investigate three approximate collective classification algorithms for inference. Going one step further, we show that the performance can be remarkably improved using an enhanced boosting classification setting in which we employ the literal meaning of hashtags as semi-supervised information. Experimental results on a real-life data set consisting of 29,195 tweets and 2,181 hashtags show the effectiveness of the proposed model and algorithms.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"32 1","pages":"1031-1040"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85939031","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: 470

Effective stratification for low selectivity queries on deep web data sources 深层网络数据源低选择性查询的有效分层

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063786

Fan Wang, G. Agrawal

{"title":"Effective stratification for low selectivity queries on deep web data sources","authors":"Fan Wang, G. Agrawal","doi":"10.1145/2063576.2063786","DOIUrl":"https://doi.org/10.1145/2063576.2063786","url":null,"abstract":"We study the problem of estimating the result of an aggregation query with low selectivity when a data source only supports limited data accesses. Existing stratified sampling techniques cannot be applied to such a problem since either it is very hard, if not impossible, to gather certain critical statistics from such a data source, or more importantly, the selective attribute of the query may not be queriable on the data source. In such cases, we need an effective mechanism to stratify the data and form homogeneous strata with respect to the selective attribute of the query, despite not being able to query the data source with the selective attribute.\u0000 This paper presents and evaluates a stratification method for this problem utilizing a queriable auxiliary attribute. The breaking points for the stratification are computed based on a novel Bayesian Adaptive Harmony Search algorithm. This method derives from the existing Harmony search method, but includes novel objective function, and introduces a technique for dynamically adapting key parameters of this method. Our experiments show that the estimation accuracy achieved using our method is consistently higher than 95% even for 0.01% selectivity query, even when there is only a low correlation between the auxiliary attribute and the selective attribute. Furthermore, our method achieves at least a five fold reduction in estimation error over three other methods, for the same sampling cost.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"223 1","pages":"1455-1464"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86801169","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