OpenRaiser/Intern-Atlas huggingface dataset:
Structured data classification by means of matrix factorization
Authors: Paolo Garza
Venue: CIKM
Year: 2011

Singular Value Decomposition (SVD) has been extensively used in the classification context as a preprocessing step aiming to reduce the number of features of the input space. Traditional classification algorithms are then applied on the new space to generate accurate models. In this paper, we propose a different use of SVD. In our approach SVD is the building block of a new classification algorithm, called CMF, and not that of a feature reduction algorithm. In particular, we propose a new classification algorithm where the classification model corresponds to the k largest right singular vectors of the factorization of the training dataset obtained by applying SVD. The selected singular vectors allows representing the main "characteristics" of the training data and can be used to provide accurate predictions. The experiments performed on 15 structured UCI datasets show that CMF is efficient and, despite its simplicity, it is more accurate than many state of the art classification algorithms.
TOPSIG: topology preserving document signatures
Authors: Shlomo Geva, Christopher M. De Vries
Venue: CIKM
Year: 2011

Comparisons between file signatures and inverted files for text retrieval have shown the shortcomings of traditional file signatures. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures that extends recent advances in semantic hashing and dimensionality reduction. These were not so far linked to general purpose, signature file based, search engines. We demonstrate significant improvements in the performance of signature file based indexing and retrieval. Performance is comparable to the state of the art inverted file based systems, including language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and positions the file signatures model in the class of Vector Space retrieval models.
H-DB: a hybrid quantitative-structural sql optimizer
Authors: Lucantonio Ghionna, Gianluigi Greco, Francesco Scarcello
Venue: CIKM
Year: 2011

Structural decomposition methods are query optimization methods specifically conceived in the database theory community to efficiently answer (near-)acyclic queries. We propose to demonstrate H-DB, an SQL query optimizer that combines classical quantitative optimization techniques with such structural decomposition methods, which so far have been just analyzed from the theoretical viewpoint. The system provides support to optimizing SQL queries with arbitrary output variables, aggregate operators, ORDER BY statements, and nested queries. H-DB can be put on top of any existing database management system supporting JDBC technology, by transparently interacting/replacing its standard query optimization module. However, to push at maximum its optimization capabilities, H-DB should be coupled with an ad-hoc physical semi-join operator, which (as a relevant example) we implemented and integrated within the PostgreSQL database management system.
Learning to rank user intent
Authors: Giorgos Giannopoulos, Ulf Brefeld, Theodore Dalamagas, Timos K. Sellis
Venue: CIKM
Year: 2011

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.
3rd international workshop on collaborative information retrieval (CIR2011)
Authors: Gene Golovchinsky, Juan M. Fernández-Luna, Juan F. Huete, Meredith Ringel Morris, Jeremy Pickens, Julio C. Rodríguez Cano
Venue: CIKM
Year: 2011

Synchronous, explicit search has some interesting characteristics that distinguish it from other types of interaction: there is much more emphasis on interaction, as the system has to not only communicate search results to the user, but also mediate some forms of communication and data sharing among its users. There are new algorithms that need to be invented that use inputs from multiple people to produce search results, and new evaluation metrics need to be invented that reflect the collaborative and interactive nature of the task. Finally, we need to integrate the expertise of library and information science researchers and practitioners by revisiting real-world information seeking situations with an eye for explicit, synchronous collaborative search.
Categorising logical differences between OWL ontologies
Authors: Rafael S. Gonçalves, Bijan Parsia, Ulrike Sattler
Venue: CIKM
Year: 2011

The analysis of changes between OWL ontologies (in the form of a diff ) is an important service for ontology engineering. A purely syntactic analysis of changes is insufficient to distinguish between changes that have logical impact and those that do not. The current state of the art in semantic diffing ignores logically ineffectual changes and lacks any further characterisation of even significant changes. We present a diff method based on an exhaustive categorisation of effectual and ineffectual changes between ontologies. In order to verify the applicability of our approach we apply it to 88 versions of the National Cancer Institute (NCI) Thesaurus (NCIt), and demonstrate that all categories are realized throughout the corpus. Based on the outcome of the NCIt study we argue that the devised categorisation of changes is helpful for ontology engineers and their understanding of changes carried out between ontologies.
Insights into explicit semantic analysis
Authors: Thomas Gottron, Maik Anderka, Benno Stein
Venue: CIKM
Year: 2011

Since its debut the Explicit Semantic Analysis (ESA) has received much attention in the IR community. ESA has been proven to perform surprisingly well in several tasks and in different contexts. However, given the conceptual motivation for ESA, recent work has observed unexpected behavior. In this paper we look at the foundations of ESA from a theoretical point of view and employ a general probabilistic model for term weights which reveals how ESA actually works. Based on this model we explain some of the phenomena that have been observed in previous work and support our findings with new experiments. Moreover, we provide a theoretical grounding on how the size and the composition of the index collection affect the ESA-based computation of similarity values for texts.
Annotating software documentation in semantic wikis
Authors: Klaas Andries de Graaf
Venue: CIKM
Year: 2011

The use of software documentation in traditional file-based format has various issues. Storing and annotating the content of software documentation in semantic wiki pages addresses many of these issues and supports knowledge retrieval. This paper reports on the application of semantic annotation and knowledge retrieval using the ArchiMind semantic wiki system. Knowledge in documentation is annotated by indexing text with a lightweight software engineering ontology. The process and the use-cases of this semantic annotation of software documents are described.
The quality of the XML web
Authors: Steven Grijzenhout, Maarten Marx
Venue: CIKM
Year: 2011

We collect evidence to answer the following question: Is the quality of the XML documents found on the web sufficient to apply XML technology like XQuery, XPath and XSLT? XML collections from the web have been previously studied statistically, but no detailed information about the quality of the XML documents on the web is available to date. We address this shortcoming in this study. We gathered 180K XML documents from the web. Their quality is surprisingly good; 85.4% is well-formed and 99.5% of all specified encodings is correct. Validity needs serious attention. Only 25% of all files contain a reference to a DTD or XSD, of which just one third is actually valid. Errors are studied in detail. Automatic error repair seems promising. Our study is well documented and easily repeatable. This paves the way for a periodic quality assessment of the XML web.
Towards feature selection in network
Authors: Quanquan Gu, Jiawei Han
Venue: CIKM
Year: 2011

Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). However, in real world, there are tremendous amount of data which are distributing in a network. Existing features selection methods are not suited for networked data because the i.i.d. assumption no longer holds. This motivates us to study feature selection in a network. In this paper, we present a supervised feature selection method based on Laplacian Regularized Least Squares (LapRLS) for networked data. In detail, we use linear regression to utilize the content information, and adopt graph regularization to consider the link information. The proposed feature selection method aims at selecting a subset of features such that the empirical error of LapRLS is minimized. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a $L_{2,1}$-norm constrained LapRLS problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark networked data sets show that the proposed feature selection method outperforms traditional feature selection method and the state of the art learning in network approaches.
Correlated multi-label feature selection
Authors: Quanquan Gu, Zhenhui Li, Jiawei Han
Venue: CIKM
Year: 2011

Multi-label learning studies the problem where each instance is associated with a set of labels. There are two challenges in multi-label learning: (1) the labels are interdependent and correlated, and (2) the data are of high dimensionality. In this paper, we aim to tackle these challenges in one shot. In particular, we propose to learn the label correlation and do feature selection simultaneously. We introduce a matrix-variate Normal prior distribution on the weight vectors of the classifier to model the label correlation. Our goal is to find a subset of features, based on which the label correlation regularized loss of label ranking is minimized. The resulting multi-label feature selection problem is a mixed integer programming, which is reformulated as quadratically constrained linear programming (QCLP). It can be solved by cutting plane algorithm, in each iteration of which a minimax optimization problem is solved by dual coordinate descent and projected sub-gradient descent alternatively. Experiments on benchmark data sets illustrate that the proposed methods outperform single-label feature selection method and many other state-of-the-art multi-label learning methods.
Extract knowledge from semi-structured websites for search task simplification
Authors: Yingqin Gu, Jun Yan, Hongyan Liu, Jun He, Lei Ji, Ning Liu, Zheng Chen
Venue: CIKM
Year: 2011

Simplifying the key tasks of search engine users by directly retrieving to them structured knowledge according to their queries is attracting much attention from both industry and academia. A bottleneck of this challenging problem is how to extract the structured knowledge from the noisy and complex Web scale websites automatically. In this paper, we propose an unsupervised automatic wrapper induction algorithm, named as Scalable Knowledge Extractor from webSites (SKES). SKES induces the wrapper in a divide and conquer mode, i.e., it divides the general wrapper into several sub-wrappers to learn from the data independently. Moreover, through employing techniques such as tag path representation of Web pages, SKES is verified to be efficient and noise-tolerant by the experimental results. Furthermore, based on our automatically extracted knowledge, we also built a prototype to serve structured knowledge to end users for simplifying their key search tasks. Very positive feedbacks were received on the prototype.
Optimized processing of multiple aggregate continuous queries
Authors: Shenoda Guirguis, Mohamed A. Sharaf, Panos K. Chrysanthis, Alexandros Labrinidis
Venue: CIKM
Year: 2011

Data Streams Management Systems are designed to support monitoring applications, which require the processing of hundreds of Aggregate Continuous Queries (ACQs). These ACQs typically have different time granularities, with possibly different selection predicates and group-by attributes. In order to achieve scalability in the presence of heavy workloads, in this paper, we introduce the concept of 'Weaveability' as an indicator of the potential gains of sharing the processing of ACQs. We then propose Weave Share, a cost-based optimizer that exploits weaveability to optimize the shared processing of ACQs. Our experimental analysis shows that Weave Share outperforms the alternative sharing schemes generating up to four orders of magnitude better quality plans. Finally, we describe a practical implementation of the Weave Share optimizer.
Domain customization for aspect-oriented opinion analysis with multi-level latent sentiment clues
Authors: Honglei Guo, Huijia Zhu, Zhili Guo, Zhong Su
Venue: CIKM
Year: 2011

Aspect-oriented opinion mining detects the reviewers' sentiment orientation (e.g. positive, negative or neutral) towards different product-features. Domain customization is a big challenge for opinion mining due to the accuracy loss across domains. In this paper, we show our experiences and lessons learned in the domain customization for the aspect-oriented opinion analysis system OpinionIt. We present a customization method for sentiment classification with multi-level latent sentiment clues. We first construct Latent Semantic Association model to capture latent association among product-features from the unlabeled corpus. Meanwhile, we present an unsupervised method to effectively extract various domain-specific sentiment clues from the unlabeled corpus. In the customization, we tune the sentiment classifier on the labeled source domain data by incorporating the multi-level latent sentiment clues (e.g. latent association among product-features, domain-specific and generic sentiment clues). Experimental results show that the proposed method significantly reduces the accuracy loss of sentiment classification without any labeled target domain data.
Mining frequent patterns across multiple data streams
Authors: Jing Guo, Peng Zhang, Jianlong Tan, Li Guo
Venue: CIKM
Year: 2011

Mining frequent patterns from data streams has drawn increasing attention in recent years. However, previous mining algorithms were all focused on a single data stream. In many emerging applications, it is of critical importance to combine multiple data streams for analysis. For example, in real-time news topic analysis, it is necessary to combine multiple news report streams from dierent media sources to discover collaborative frequent patterns which are reported frequently in all media, and comparative frequent patterns which are reported more frequently in a media than others. To address this problem, we propose a novel frequent pattern mining algorithm Hybrid-Streaming, H-Stream for short. H-Stream builds a new Hybrid-Frequent tree to maintain historical frequent and potential frequent itemsets from all data streams, and incrementally updates these itemsets for efficient collaborative and comparative pattern mining. Theoretical and empirical studies demonstrate the utility of the proposed method.
Index tuning for query-log based on-line index maintenance
Authors: Sairam Gurajada, P. Sreenivasa Kumar
Venue: CIKM
Year: 2011

The existing query-log based on-line index maintenance approaches rely on frequency distribution of terms in the static query-log. Though these approaches are proved to be efficient, but in real world, the frequency distribution of the terms changes over a period of time. This negatively affects the efficiency of the static query-log based approaches. To overcome this problem, we propose an index tuning strategy for reorganizing the indexes according to the latest frequency distribution of the terms captured from query-logs.Experimental results show that the proposed tuning strategy improves the performance of static query-log based approaches.
Examining the "leftness" property of Wikipedia categories
Authors: Karl Gyllstrom, Marie-Francine Moens
Venue: CIKM
Year: 2011

Wikipedia’s rich category structure has helped make it one of the largest semantic taxonomies in existence, a property that has been central to much recent research. However, Wikipedia’s category representation is simplistic: an article contains a single list of categories, with no data about their relative importance. We investigate the ordering of category lists to determine how a category’s position in the list correlates with its relevance to the article and overall significance. We identify a number of interesting connections between a category’s position and its persistence within the article, age, popularity, size, and descriptiveness.
Query session detection as a cascade
Authors: Matthias Hagen, Benno Stein, Tino Rüb
Venue: CIKM
Year: 2011

We propose a cascading method for query session detection, the problem of identifying series of consecutive queries a user submits with the same information need. While the existing session detection research mostly deals with effectiveness, our focus also is on efficiency, and we investigate questions related to the analysis trade-off: How expensive (in terms of runtime) is a certain improvement in F-Measure? In this regard, we distinguish two major scenarios where query session knowledge is important: (1) In an online setting, the search engine tries to incorporate knowledge of the preceding queries for an improved retrieval performance. Obviously, the efficiency of the session detection method is a crucial issue as the overall retrieval time should not be influenced too much. (2) In an offline post-retrieval setting, search engine logs are divided into sessions in order to examine what causes users to fail or to identify typical reformulation patterns etc. Here, efficiency might not be as important as in the online scenario but the accuracy of the detected sessions is essential.
A parallel algorithm for computing borders
Authors: Nicolas Hanusse, Sofian Maabout
Venue: CIKM
Year: 2011

The border concept has been introduced by Mannila and Toivonen in their seminal paper [20]. This concept finds many applications, e.g maximal frequent itemsets, minimal functional dependencies, emerging patterns between consecutive database instances and materialized view selection. For large transactions and relational databases defined on n items or attributes, the running time of any border computations are mainly dominated by the time T (for standard sequential algorithms) required to test the interestingness, in general the frequencies, of sets of candidates.
Bayesian latent variable models for collaborative item rating prediction
Authors: Morgan Harvey, Mark James Carman, Ian Ruthven, Fabio Crestani
Venue: CIKM
Year: 2011

Collaborative filtering systems based on ratings make it easier for users to find content of interest on the Web and as such they constitute an area of much research. In this paper we first present a Bayesian latent variable model for rating prediction that models ratings over each user's latent interests and also each item's latent topics. We describe a Gibbs sampling procedure that can be used to estimate its parameters and show by experiment that it is competitive with the gradient descent SVD methods commonly used in state-of-the-art systems. We then proceed to make an important and novel extension to this model, enhancing it with user-dependent and item-dependant biases to significantly improve rating estimation. We show by experiment on a large set of real ratings data that these models are able to outperform 3 common baselines, including a very competitive and modern SVD-based model. Furthermore we illustrate other advantages of our approach beyond simply its ability to provide more accurate ratings and show that it is able to perform better on the common and important case where the user profile is short.
A task level metric for measuring web search satisfaction and its application on improving relevance estimation
Authors: Ahmed Hassan Awadallah, Yang Song, Li-wei He
Venue: CIKM
Year: 2011

Understanding the behavior of satisfied and unsatisfied Web search users is very important for improving users search experience. Collecting labeled data that characterizes search behavior is a very challenging problem. Most of the previous work used a limited amount of data collected in lab studies or annotated by judges lacking information about the actual intent. In this work, we performed a large scale user study where we collected explicit judgments of user satisfaction with the entire search task. Results were analyzed using sequence models that incorporate user behavior to predict whether the user ended up being satisfied with a search or not. We test our metric on millions of queries collected from real Web search traffic and show empirically that user behavior models trained using explicit judgments of user satisfaction outperform several other search quality metrics. The proposed model can also be used to optimize different search engine components. We propose a method that uses task level success prediction to provide a better interpretation of clickthrough data. Clickthough data has been widely used to improve relevance estimation. We use our user satisfaction model to distinguish between clicks that lead to satisfaction and clicks that do not. We show that adding new features derived from this metric allowed us to improve the estimation of document relevance.
Generating links to background knowledge: a case study using narrative radiology reports
Authors: Jiyin He, Maarten de Rijke, Merlijn Sevenster, Rob C. van Ommering, Yuechen Qian
Venue: CIKM
Year: 2011

Automatically annotating texts with background information has recently received much attention. We conduct a case study in automatically generating links from narrative radiology reports to Wikipedia. Such links help users understand the medical terminology and thereby increase the value of the reports. Direct applications of existing automatic link generation systems trained on Wikipedia to our radiology data do not yield satisfactory results. Our analysis reveals that medical phrases are often syntactically regular but semantically complicated, e.g., containing multiple concepts or concepts with multiple modifiers. The latter property is the main reason for the failure of existing systems. Based on this observation, we propose an automatic link generation approach that takes into account these properties. We use a sequential labeling approach with syntactic features for anchor text identification in order to exploit syntactic regularities in medical terminology. We combine this with a sub-anchor based approach to target finding, which is aimed at coping with the complex semantic structure of medical phrases. Empirical results show that the proposed system effectively improves the performance over existing systems.