OpenRaiser/Intern-Atlas huggingface dataset:
Designing an ensemble classifier over subspace classifiers using iterative convergence routine
Authors: Bhanukiran Vinzamuri, Kamalakar Karlapalem
Venue: CIKM
Year: 2011

There can be multiple classifiers for a given data set. One way to generate multiple classifiers is to use subspaces of the attribute sets. In this paper, we generate subspace classifiers by an iterative convergence routine to build an ensemble classifier. Experimental evaluation covers the cases of both labelled and unlabelled (blind) data separately. We evaluate our approach on many benchmark UC Irvine datasets to assess the robustness of our approach with varying induced noise levels. We explicitly compare and present the utility of the clusterings generated for classification using several diverse clustering dissimilarity metrics. Results show that our ensemble classifier is a more robust classifier in comparison to different multi-class classification approaches.
RFID data analysis using tensor calculus for supply chain management
Authors: Roberto De Virgilio, Franco Milicchio
Venue: CIKM
Year: 2011

In current trends of consumer products market, there is a growing significance of the role of retailers in the governance of supply chains. RFID is a promising infrastructure-less technology, allowing to connect an object with its virtual counterpart, i.e., its representation within information systems. However, the amount of RFID data in supply chain management is vast, posing significant challenges for attaining acceptable performance on their analysis. Current approaches provide hard-coded solutions, with high consumption of resources; moreover, these exhibit very limited flexibility dealing with multidimensional queries, at various levels of granularity and complexity. In this paper we propose a general model for supply chain management based on the first principles of linear algebra, in particular on tensorial calculus. Leveraging our abstract algebraic framework, our technique allows both quick decentralized on-line processing, and centralized off-line massive business logic analysis, according to needs and requirements of supply chain actors. Experimental results show that our approach, utilizing recent linear algebra techniques can process analysis efficiently, when compared to recent approaches. In particular, we are able to carry out the required computations even in high memory constrained environments, such as on mobile devices. Moreover, when dealing with massive amounts of data, we are capable of exploiting recent parallel and distributed technologies, subdividing our tensor objects into sub-blocks, and processing them independently.
Semantic data markets: a flexible environment for knowledge management
Authors: Roberto De Virgilio, Giorgio Orsi, Letizia Tanca, Riccardo Torlone
Venue: CIKM
Year: 2011

We present Nyaya, a system for the management of Semantic-Web data which couples a general-purpose and extensible storage mechanism with efficient ontology reasoning and querying capabilities. Nyaya processes large Semantic-Web datasets, expressed in multiple formalisms, by transforming them into a collection of Semantic Data Kiosks. Nyaya uniformly exposes the native meta-data of each kiosk using the datalog+- language, a powerful rule-based modelling language for ontological databases. The kiosks form a Semantic Data Market where the data in each kiosk can be uniformly accessed using conjunctive queries and where users can specify user-defined constraints over the data. Nyaya is easily extensible and robust to updates of both data and meta-data in the kiosk and can readily adapt to different logical organization of the persistent storage. The approach has been experimented using well-known benchmarks, and compared to state-of-the-art research prototypes and commercial systems.
A peer's-eye view: network term clouds in a peer-to-peer system
Authors: Raynor Vliegendhart, Martha A. Larson, Christoph Kofler, Johan A. Pouwelse
Venue: CIKM
Year: 2011

We investigate term clouds that represent the content available in a peer-to-peer (P2P) network. Such network term clouds are non-trivial to generate in distributed settings. Our term cloud generator was implemented and released in Tribler--a widely-used, server-free P2P system--to support users in understanding the sorts of content available. Our evaluation and analysis focuses on three aspects of the clouds: coverage, usefulness and accumulation speed. A live experiment demonstrates that individual peers accumulate substantial network-level information, indicating good coverage of the overall content of the system. The results of a user study carried out on a crowdsourcing platform confirm the usefulness of clouds, showing that they succeed in conveying to users information on the type of content available in the network. An analysis of five example peers reveals that accumulation speeds of terms at new peers can support the development of a semantically diverse term set quickly after a cold start. This work represents the first investigation of term clouds in a live, 100% server-free P2P setting.
What to do when one size does not fit all?
Authors: Arjen P. de Vries
Venue: CIKM
Year: 2011

This talk addresses the theme how semantic annotations could improve information access. In this context, "semantic annotation" may refer to any (perhaps typed) clue about documents in a collection that can be useful for retrieval purposes: the call for papers of the ESAIR workshop mentions examples varying from automatically assigned linguistic annotations like named entities to manually assigned social media tags. While any clue could in principle be useful for retrieval, it can be expected to prove extremely hard (if not impossible) to ever provide a single canned solution, the one size fits all "semantically enhanced retrieval model", that would not just match information need representations with document representations, but also automatically determine how to best interpret the individual relative contribution of all those types of clues. In this talk, I explore an alternative route - can we empower the user to express which semantic annotations should be exploited, such that we do not need to define an a priori fixed retrieval model, but instead may provide the user the building blocks with which they can create their own retrieval model, specific to the information need at hand. Is this at all feasible? What challenges are there to be addressed?
Constructing efficient information extraction pipelines
Authors: Henning Wachsmuth, Benno Stein, Gregor Engels
Venue: CIKM
Year: 2011

Information Extraction (IE) pipelines analyze text through several stages. The pipeline's algorithms determine both its effectiveness and its run-time efficiency. In real-world tasks, however, IE pipelines often fail acceptable run-times because they analyze too much task-irrelevant text. This raises two interesting questions: 1) How much "efficiency potential" depends on the scheduling of a pipeline's algorithms? 2) Is it possible to devise a reliable method to construct efficient IE pipelines? Both questions are addressed in this paper. In particular, we show how to optimize the run-time efficiency of IE pipelines under a given set of algorithms. We evaluate pipelines for three algorithm sets on an industrially relevant task: the extraction of market forecasts from news articles. Using a system-independent measure, we demonstrate that efficiency gains of up to one order of magnitude are possible without compromising a pipeline's original effectiveness.
More influence means less work: fast latent dirichlet allocation by influence scheduling
Authors: Mirwaes Wahabzada, Kristian Kersting, Anja Pilz, Christian Bauckhage
Venue: CIKM
Year: 2011

There have recently been considerable advances in fast inference for (online) latent Dirichlet allocation (LDA). While it is widely recognized that the scheduling of documents in stochastic optimization and in turn in LDA may have significant consequences, this issue remains largely unexplored. Instead, practitioners schedule documents essentially uniformly at random, due perhaps to ease of implementation, and to the lack of clear guidelines on scheduling the documents.
Effective stratification for low selectivity queries on deep web data sources
Authors: Fan Wang, Gagan Agrawal
Venue: CIKM
Year: 2011

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.
Filtering and clustering relations for unsupervised information extraction in open domain
Authors: Wei Wang, Romaric Besançon, Olivier Ferret, Brigitte Grau
Venue: CIKM
Year: 2011

Information Extraction has recently been extended to new areas by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, we focus on the task of extracting and characterizing a priori unknown relations between a given set of entity types. One of the challenges of this task is to deal with the large amount of candidate relations when extracting them from a large corpus. We propose in this paper an approach for the filtering of such candidate relations based on heuristics and machine learning models. More precisely, we show that the best model for achieving this task is a Conditional Random Field model according to evaluations performed on a manually annotated corpus of about one thousand relations. We also tackle the problem of identifying semantically similar relations by clustering large sets of them. Such clustering is achieved by combining a classical clustering algorithm and a method for the efficient identification of highly similar relation pairs. Finally, we evaluate the impact of our filtering of relations on this semantic clustering with both internal measures and external measures. Results show that the filtering procedure doubles the recall of the clustering while keeping the same precision.
Statistical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond
Authors: Jun Wang, Kevyn Collins-Thompson
Venue: CIKM
Year: 2011

Statistical modelling of Information Retrieval (IR) systems is a key driving force in the development of the IR field. The goal of this tutorial is to provide a comprehensive and up-to-date introduction to statistical IR modelling. We take a fresh and systematic perspective from the viewpoint of portfolio theory of IR and risk management. A unified treatment and new insights will be given to reflect the recent developments of considering the ranked retrieval results as a whole. Recent research progress in diversification, risk management, and portfolio theory will be covered, in addition to classic methods such as Maron and Kuhns' Probabilistic Indexing, Robertson-Sparck Jones model (and the resulting BM25 formula) and language modelling approaches. The tutorial also reviews the resulting practical algorithms of risk-aware query expansion, diverse ranking, IR metric optimization as well as their performance evaluations. Practical IR applications such as web search, multimedia retrieval, and collaborative filtering are also introduced, as well as discussion of new opportunities for future research and applications that intersect among information retrieval, knowledge management, and databases.
Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization
Authors: Hua Wang, Heng Huang, Chris H. Q. Ding
Venue: CIKM
Year: 2011

The rapid growth of Internet and modern technologies has brought data involving objects of multiple types that are related to each other, called as multi-type relational data. Traditional clustering methods for single-type data rarely work well on them, which calls for more advanced clustering techniques to deal with multiple types of data simultaneously to utilize their interrelatedness. A major challenge in developing simultaneous clustering methods is how to effectively use all available information contained in a multi-type relational data set including inter-type and intra-type relationships. In this paper, we propose a Symmetric Nonnegative Matrix Tri-Factorization (S-NMTF) framework to cluster multi-type relational data at the same time. The proposed S-NMTF approach employs NMTF to simultaneously cluster different types of data using their inter-type relationships, and incorporate the intra-type information through manifold regularization. In order to deal with the symmetric usage of the factor matrix in S-NMTF, we present a new generic matrix inequality to derive the solution algorithm, which involves a fourth-order matrix polynomial, in a principled way. Promising experimental results have validated the proposed approach.
Efficient lp-norm multiple feature metric learning for image categorization
Authors: Shuhui Wang, Qingming Huang, Shuqiang Jiang, Qi Tian
Venue: CIKM
Year: 2011

Previous metric learning approaches are only able to learn the metric based on single concatenated multivariate feature representation. However, for many real world problems with multiple feature representation such as image categorization, the model trained by previous approaches will degrade because of sparsity brought by significant dimension growth and uncontrolled influence from each feature channel. In this paper, we propose an efficient distance metric learning model which adapts Distance Metric Learning on multiple feature representations. The aim is to learn the Mahalanobis matrices for each independent feature and their non-sparse lp-norm weight coefficients simultaneously by maximizing the margin of the overall learned distance metric among the pairs from the same class and the distance of pairs from different classes. We further extend this method to nonlinear kernel learning and category specific metric learning, which demonstrate the applicability of using many existing kernels for image data and exploring the hierarchical semantic structures for large scale image datasets. Experiments on various datasets demonstrate the promising power of our method.
Learning to recommend questions based on public interest
Authors: Jun Wang, Xia Hu, Zhoujun Li, Wen-Han Chao, Biyun Hu
Venue: CIKM
Year: 2011

This paper is concerned with the problem of question recommendation in the setting of Community Question Answering (CQA). Given a question as query, our goal is to rank all of the retrieved questions according to their likelihood of being good recommendations for the query. In this paper, we propose a notion of public interest, and show how public interest can boost the performance of question recommendation. In particular, to model public interest in question recommendation, we build a language model to combine relevance score to the query and popularity score regarding question popularity. Experimental results on Yahoo!Answers dataset demonstrate the performance of question recommendation can be greatly improved with considering the public interest.
CoRankBayes: bayesian learning to rank under the co-training framework and its application in keyphrase extraction
Authors: Chen Wang, Sujian Li
Venue: CIKM
Year: 2011

Recently, learning to rank algorithms have become a popular and effective tool for ordering objects (e.g. terms) according to their degrees of importance. The contribution of this paper is that we propose a simple and fast learning to rank model RankBayes and embed it in the co-training framework. The detailed proof is given that Naïve Bayes algorithm can be used to implement a learning to rank model. To solve the problem of two-model inconsistency, an ingenious approach is put forward to rank all the phrases by making use of the labeled results of two RankBayes models. Experimental results show that the proposed approach is promising in solving ranking problems.
Connecting users with similar interests via tag network inference
Authors: Xufei Wang, Huan Liu, Wei Fan
Venue: CIKM
Year: 2011

The popularity of social networking greatly increases interaction among people. However, one major challenge remains --- how to connect people who share similar interests. In a social network, the majority of people who share similar interests with given a user are in the long tail that accounts for 80% of total population. Searching for similar users by following links in social network has two limitations: it is inefficient and incomplete. Thus, it is desirable to design new methods to find like-minded people. In this paper, we propose to use collective wisdom from the crowd or tag networks to solve the problem. In a tag network, each node represents a tag as described by some words, and the weight of an undirected edge represents the co-occurrence of two tags. As such, the tag network describes the semantic relationships among tags. In order to connect to other users of similar interests via a tag network, we use diffusion kernels on the tag network to measure the similarity between pairs of tags. The similarity of people's interests are measured on the basis of similar tags they share. To recommend people who are alike, we retrieve top k people sharing the most similar tags. Compared to two baseline methods triadic closure and LSI, the proposed tag network approach achieves 108% and 27% relative improvements on the BlogCatalog dataset, respectively.
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Authors: Xiaolong Wang, Furu Wei, Xiaohua Liu, Ming Zhou, Ming Zhang
Venue: CIKM
Year: 2011

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.
Relevance feedback exploiting query-specific document manifolds
Authors: Chang Wang, Emine Yilmaz, Martin Szummer
Venue: CIKM
Year: 2011

We incorporate relevance feedback into a learning to rank framework by exploiting query-specific document similarities. Given a few judged feedback documents and many retrieved but unjudged documents for a query, we learn a function that adjusts the initial ranking score of each document. Scores are fit so that documents with similar term content get similar scores, and scores of judged documents are close to their labels. By such smoothing along the manifold of retrieved documents, we avoid overfitting, and can therefore learn a detailed query-specific scoring function with several dozen term weights.
A language model approach to capture commercial intent and information relevance for sponsored search
Authors: Lei Wang, Mingjiang Ye, Yu Zou
Venue: CIKM
Year: 2011

A fundamental task of sponsored search is how to find the best match between web search queries and textual advertisements. To address this problem, we explicitly characterize the criteria for an advertisement to be a 'good match' to a query from two aspects (it should be relevant with the query from information perspective, and it should be able to capture and satisfy the commercial intent in the query). Correspondingly, we introduce in this paper a mixture language model of two parts: a commercial model which characterizes language bias of commercial intent leveraging on users' clicks on advertisements, and an informational model which is a traditional language model with consideration of the entropy of each word to capture informational relevance. We then introduce a regularized expectation-maximization (EM) algorithm model for parameters estimation, and integrate query commercial intent into the scoring function to boost overall click efficiency. Empirical evaluation shows that our model achieves better performance as compared to a well tuned classical language model and deliberated TFIDF-pLSI model (6% and 5% precision improvement at our operating point in production environment of 30% recall, and 5.3% and 6.3% AUC improvement), and performs superior to the KL Divergence language model for tail queries (0.5% nDCG improvement). Live traffic test shows over 2% CTR lift and 2.5% RPS lift as well.
Using random walks for multi-label classification
Authors: Chaokun Wang, Wei Zheng, Zhang Liu, Yiyuan Bai, Jianmin Wang
Venue: CIKM
Year: 2011

The Multi-Label Classification (MLC) problem has aroused wide concern in these years since the multi-labeled data appears in many applications, such as page categorization, tag recommendation, mining of semantic web data, social network analysis, and so forth. In this paper, we propose a novel MLC solution based on the random walk model, called MLRW. MLRW maps the multi-labeled instances to graphs, on which the random walk is applied. When an unlabeled data is fed, MLRW transforms the original multi-label problem to some single-label subproblems. Experimental results on several real-world data sets demonstrate that MLRW is a better solution to the MLC problems than many other existing multi-label classification methods.