OpenRaiser/Intern-Atlas huggingface dataset:
Exploiting context to detect sensitive information in call center conversations
Authors: Tanveer A. Faruquie, Sumit Negi, Anup Chalamalla, L. Venkata Subramaniam
Venue: CIKM
Year: 2008

Protecting sensitive information while preserving the share-ability and usability of data is becoming increasingly important. In call-centers a lot of customer related sensitive information is stored in audio recordings. In this work, we address the problem of protecting sensitive information in audio recordings and speech transcripts. We present a semi-supervised method to model sensitive information as a directed graph. Effectiveness of this approach is demonstrated by applying it to the problem of detecting and locating credit card transaction in real life conversations between agents and customers in a call center.
Semantic web for net-enabled decision making
Authors: Yousri M. El Fattah
Venue: CIKM
Year: 2008

The paper introduces a Semantic Web approach for net-enabled decision making. The Semantic Web ontology language OWL-DL is used to specify the vocabulary and relationships linking situational and decision making semantics for the decision domain. The knowledge is stored as an ontology library of templates where each template is a parameterized causal model fragment that can be instantiated to situation-specific Bayesian networks for course of action reasoning. Our semantic web approach allows semantic interoperability in joint distributed decision making with multiple information sources. The approach shortens the decision cycle and reduces the cognitive burden on the decision-maker. The paper illustrates the approach on a prototype application for course of action in military effects based operations.
The role of syntactic features in protein interaction extraction
Authors: Timur Fayruzov, Martine De Cock, Chris Cornelis, Véronique Hoste
Venue: CIKM
Year: 2008

Most approaches for protein interaction mining from biomedical texts use both lexical and syntactic features. However, the individual impact of these two kinds of features on the effectiveness of the mining process has not yet been thoroughly studied. In this paper, we perform such a study on a recently published state of the art support vector machine approach that uses both lexical and syntactic features. To this end, we strip this approach down to an algorithm that uses only a subset of the initial syntactic features. Next, we compare the original and the stripped-down method by evaluating them on 5 benchmark datasets as well as by performing 5 additional cross-dataset experiments. Although the original method exploits a very rich feature set including words, parts-of-speech and grammatical relations, it is not significantly better than the stripped-down version; in fact, the former does not even consistently outperform the latter.
Structure feature selection for graph classification
Authors: Hongliang Fei, Jun Huan
Venue: CIKM
Year: 2008

With the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. Towards building highly accurate classification models for graph data, here we present an efficient graph feature selection method. In our method, we use frequent subgraphs as features for graph classification. Different from existing methods, we consider the spatial distribution of the subgraph features in the graph data and select those ones that have consistent spatial location.
Managing collaborative feedback information for distributed retrieval
Authors: Pascal Felber, Toan Luu, Martin Rajman, Etienne Rivière
Venue: CIKM
Year: 2008

Despite the many research efforts invested recently in peer-to-peer search engines, none of the proposed system has reached the level of quality and efficiency of their centralized counterpart. One of the main reasons for this inferior performance is the difficulty to attract a critical mass of users that would make the peer-to-peer system truly competitive. We argue that decentralized search mechanisms should not aim at replacing existing engines, but instead complement them by adding novel functionalities that would be difficult to provide in a centralized manner. This paper introduces an example of such a complementary search mechanism and presents the design of a distributed collaborative system for leveraging user feedback and document/user profiling information.
Mining conceptual graphs for knowledge acquisition
Authors: Milagros Fernández Gavilanes, Éric Villemonte de la Clergerie, Manuel Vilares Ferro
Venue: CIKM
Year: 2008

This work addresses the use of computational linguistic analysis techniques for conceptual graphs learning from unstructured texts. A technique including both content mining and interpretation, as well as clustering and data cleaning, is introduced. Our proposal exploits sentence structure in order to generate concept hypothese, rank them according to plausibility and select the most credible ones. It enables the knowledge acquisition task to be performed without supervision, minimizing the possibility of failing to retrieve information contained in the document, in order to extract non-taxonomic relations.
BNS feature scaling: an improved representation over tf-idf for svm text classification
Authors: George Forman
Venue: CIKM
Year: 2008

In the realm of machine learning for text classification, TF-IDF is the most widely used representation for real-valued feature vectors. However, IDF is oblivious to the training class labels and naturally scales some features inappropriately. We replace IDF with Bi-Normal Separation (BNS), which has been previously found to be excellent at ranking words for feature selection filtering. Empirical evaluation on a benchmark of 237 binary text classification tasks shows substantially better accuracy and F-measure for a Support Vector Machine (SVM) by using BNS scaling. A wide variety of other feature representations were later tested and found inferior, as well as binary features with no scaling. Moreover, BNS scaling yielded better performance without feature selection, obviating the need for feature selection.
Linear time membership in a class of regular expressions with interleaving and counting
Authors: Giorgio Ghelli, Dario Colazzo, Carlo Sartiani
Venue: CIKM
Year: 2008

The extension of Regular Expressions (REs) with an interleaving (shuffle) operator has been proposed in many occasions, since it would be crucial to deal with unordered data. However, interleaving badly affects the complexity of basic operations, and, expecially, makes membership NP-hard [13], which is unacceptable for most uses of REs.
An extended cooperative transaction model for xml
Authors: Francis Gropengießer, Kai-Uwe Sattler
Venue: CIKM
Year: 2008

In many application areas, for example in design or media production processes, several authors have to work cooperatively on the same project. Thereby, a frequently used data format is XML. In this paper, we address the special requirements of cooperative working on shared XML graph structures, such as early visibility of updates, multi-directional information flow, and parallel working. Since most existing transaction models are hardly applicable, we present a novel transaction model based on multi-level transactions and dynamic actions that meets these requirements. Additional advantages of this model are appropriate concepts for transaction synchronization and resolution of conflicts.
Pruning nested XQuery queries
Authors: Bilel Gueni, Talel Abdessalem, Bogdan Cautis, Emmanuel Waller
Venue: CIKM
Year: 2008

We present in this paper an approach for XQuery optimization that exploits minimization opportunities raised in composition-style nesting of queries. More precisely, we consider the simplification of XQuery queries in which the intermediate result constructed by a subexpression is queried by another subexpression. Based on a large subset of XQuery, we describe a rule-based algorithm that recursively prunes query expressions, eliminating useless intermediate results. Our algorithm takes as input an XQuery expression that may have navigation within its subexpressions and outputs a simplified, equivalent XQuery expression, and is thus readily usable as an optimization module in any existing XQuery processor. We demonstrate by experiments the impact of our rewriting approach on query evaluation costs and we prove formally its correctness.
An interactive ontology learning workbench for non-experts
Authors: Jon Atle Gulla, Vijayan Sugumaran
Venue: CIKM
Year: 2008

Ontologies are an integral part of Knowledge and Information Management systems and there is increased interest in using ontologies for organizational memory. Ontology learning workbenches are used for semi-automatic learning of ontologies from representative text collections. This paper presents a new interactive workbench that gives the users more freedom in their ontology engineering process and frees them from knowing any ontology language syntax. The workbench is implemented as part of a search project, in which ontologies are used to search for movie information on the web. New techniques are steadily being added to the workbench, though early testing has already confirmed the validity of the ontology learning approach.
Tapping on the potential of q&a community by recommending answer providers
Authors: Jinwen Guo, Shengliang Xu, Shenghua Bao, Yong Yu
Venue: CIKM
Year: 2008

The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information.
Topic models and a revisit of text-related applications
Authors: Viet Ha-Thuc, Padmini Srinivasan
Venue: CIKM
Year: 2008

Topic models such as aspect model or LDA have been shown as a promising approach for text modeling. Unlike many previous models that restrict each document to a single topic, topic models support the important idea that each document could be relevant to multiple topics. This makes topic models significantly more expressive in modeling text documents. However, we observe two limitations in topic models. One is that of scalability as it is extremely expensive to run the models on large corpora. The other limitation is the inability to model the key concept of relevance. This prevents the models from being directly applied to goals such as text classification and relevance feedback for query modification; in these goals, items relevant to topics (classes and queries) are provided upfront. The first aim of this paper is to sketch solutions for these limitations. To alleviate the scalability problem, we introduce a one-scan topic model requiring only a single pass over a corpus for inference. To overcome the latter, we propose relevance-based topic models that have the advantages of previous models while taking the concept of relevance into account. The second aim, based on the proposed models, is to revisit a wide range of well-known but still open text-related tasks, and outline our vision on how the approaches for the tasks could be improved by topic models.
Concurrency control and recovery for multiversion database structures
Authors: Tuukka K. Haapasalo, Ibrahim Jaluta, Seppo Sippu, Eljas Soisalon-Soininen
Venue: CIKM
Year: 2008

In modern database applications access to historical versions of the dataset is becoming increasingly important. Several multiversion structures with corresponding concurrency-control and recovery algorithms exist, but none of these have optimal logarithmic execution times for all actions in all situations. The time-split B+tree by Lomet et al. (TSBT) is used in the Immortal DB database prototype, but it does not consolidate pages. The multiversion B+tree by Becker et al. (MVBT) is an asymptotically optimal multiversion structure that guarantees logarithmic execution times for all actions, but it lacks concurrency-control algorithms.
LearnLexTo: a machine-learning based word segmentation for indexing Thai texts
Authors: Choochart Haruechaiyasak, Sarawoot Kongyoung, Chaianun Damrongrat
Venue: CIKM
Year: 2008

Thai language is considered as an unsegmented language in which words are written continuously without the use of word delimiters. To index Thai texts via the inverted index, a word segmentation algorithm is usually required to tokenize a text into a series of terms. Recent works on word segmentation reported Conditional Random Fields (CRFs) as the best machine learning algorithm, outperforming the dictionary-based approach and other machine learning algorithms. Our main contribution is to propose a new hybrid approach, LearnLexTo, which further improves the CRF model by integrating the dictionary-based approach. The key idea is to solve the ambiguity problem in the CRF model by using the dictionary-based approach which relies on a valid word set. Experimental results showed that the proposed hybrid approach yields the highest F1 value of 88.46%, compared to 82.07% by using the dictionary-based approach and 85.71% by using the CRF model.
Improved query difficulty prediction for the web
Authors: Claudia Hauff, Vanessa Murdock, Ricardo Baeza-Yates
Venue: CIKM
Year: 2008

Query performance prediction aims to predict whether a query will have a high average precision given retrieval from a particular collection, or low average precision. An accurate estimator of the quality of search engine results can allow the search engine to decide to which queries to apply query expansion, for which queries to suggest alternative search terms, to adjust the sponsored results, or to return results from specialized collections. In this paper we present an evaluation of state of the art query prediction algorithms, both post-retrieval and pre-retrieval and we analyze their sensitivity towards the retrieval algorithm. We evaluate query difficulty predictors over three widely different collections and query sets and present an analysis of why prediction algorithms perform significantly worse on Web data. Finally we introduce Improved Clarity, and demonstrate that it outperforms state-of-the-art predictors on three standard collections, including two large Web collections.
PROQID: partial restarts of queries in distributed databases
Authors: Jon Olav Hauglid, Kjetil Nørvåg
Venue: CIKM
Year: 2008

In a number of application areas, distributed database systems can be used to provide persistent storage of data while providing efficient access for both local and remote data. With an increasing number of sites (computers) involved in a query, the probability of failure at query time increases. Recovery has previously only focused on database updates while query failures have been handled by complete restart of the query. This technique is not always applicable in the context of large queries and queries with deadlines. In this paper we present an approach for partial restart of queries that incurs minimal extra network traffic during query recovery. Based on results from experiments on an implementation of the partial restart technique in a distributed database system, we demonstrate its applicability and significant reduction of query cost in the presence of failures.
Translation enhancement: a new relevance feedback method for cross-language information retrieval
Authors: Daqing He, Dan Wu
Venue: CIKM
Year: 2008

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and cross-language information retrieval (CLIR) settings. The studies of RF in CLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in CLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolve some out-of-vocabulary terms. In this paper, we propose a novel RF method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra translation alternatives if available so that the translated queries are more tuned to the current search. We studied TE using pseudo relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve CLIR with both types of RF methods, and that the improvement is comparable to that of QE. More importantly, the effects of TE and QE are complementary. Their integration can produce further improvement, and makes CLIR more robust for a variety of queries.
What should blog search look like?
Authors: Marti A. Hearst, Matthew Hurst, Susan T. Dumais
Venue: CIKM
Year: 2008

Blog search has not yet reached its full potential. In this position paper, we suggest that more could be done to accommodate the task of finding good blogs to read, especially with respect to matching a desired taste or style of writing. We propose a faceted navigation interface as a good starting point for blog and author search, and that search oriented around people and their writings will lend itself well to advanced interfaces. We also argue that blog search is probably best integrated with search on other forms of timely social media for the task of determining what is currently being thought about a particular topic.
Tree, funny, to_read, google: what are tags supposed to achieve? a comparative analysis of user keywords for different digital resource types
Authors: Markus Heckner, Tanja Neubauer, Christian Wolff
Venue: CIKM
Year: 2008

Social tagging systems have become increasingly popular over the past years. Users' tagging practices have been little studied and understood so far. However, understanding tagging behaviour can contribute towards a thorough understanding of the tagging phenomenon from multiple perspectives. In the present paper, results of a comparative analysis of tag characteristics on the tagging platforms connotea.org (scientific articles), del.icio.us (bookmarks), flickr.com (photos), and youtube.com (videos) are presented. Results show that differences in tagging behaviour can be observed for different digital resource types. Finally, a short discussion of the possible implications of the results for the design of future tagging systems is presented.
Scaling up duplicate detection in graph data
Authors: Melanie Herschel, Felix Naumann
Venue: CIKM
Year: 2008

Duplicate detection determines different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. We scale up duplicate detection in graph data (DDG) to large amounts of data using the support of a relational database system. We first generalize the process of DDG and then present how to scale DDG in space (amount of data processed with limited main memory) and in time. Finally, we explore how complex similarity computation can be performed efficiently. Experiments on data an order of magnitude larger than data considered so far in DDG clearly show that our methods scale to large amounts of data.
Answering questions with authority
Authors: Andrew Hickl
Venue: CIKM
Year: 2008

This paper presents a novel approach to textual question answering (QA) which identifies answers to natural language questions by leveraging large collections of question-answer pairs extracted from web sources or generated automatically from text.
Learning a two-stage SVM/CRF sequence classifier
Authors: Guilherme Hoefel, Charles Elkan
Venue: CIKM
Year: 2008

Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "cat", based on three images of handwritten letters and on general knowledge of English letter combinations, is a sequence classification task. This paper describes a new two-stage approach to learning a sequence classifier that is (i) highly accurate, (ii) scalable, and (iii) easy to use in data mining applications. The two-stage approach combines support vector machines (SVMs) and conditional random fields (CRFs). It is (i) highly accurate because it benefits from the maximum-margin nature of SVMs and also from the ability of CRFs to model correlations between neighboring output tags. It is (ii) scalable because the input to each SVM is a small training set, and the input to the CRF has a small number of features, namely the SVM outputs. It is (iii) easy to use because it combines existing published software in a straightforward way. In detailed experiments on the task of recognizing handwritten words, we show that the two-stage approach is more accurate, or faster and more scalable, or both, than leading other methods for learning sequence classifiers, including max-margin Markov networks (M3Ns) and standard CRFs.
Re-considering neighborhood-based collaborative filtering parameters in the context of new data
Authors: Adele E. Howe, Ryan D. Forbes
Venue: CIKM
Year: 2008

The Movielens dataset and the Herlocker et al. study of 1999 have been very influential in collaborative filtering. Yet, the age of both invites re-examining their applicability. We use Netflix challenge data to re-visit the prior results. In particular, we re-evaluate the parameters of Herlocker et al.'s method on two critical factors: measuring similarity between users and normalizing the ratings of the users. We find that normalization plays a significant role and that Pearson Correlation is not necessarily the best similarity metric.
A method to predict social annotations
Authors: Ming-Hung Hsu, Hsin-Hsi Chen
Venue: CIKM
Year: 2008

This paper predicts the stabilized tag set of a resource, with feedback of a small amount of user annotations, aiming to reduce the requirement of sufficient user annotations and to resolve the cold-start problem in a social annotation system.