OpenRaiser/Intern-Atlas huggingface dataset:
Inferring semantic query relations from collective user behavior
Authors: Nish Parikh, Neel Sundaresan
Venue: CIKM
Year: 2008

In this paper we describe how high quality transaction data comprising of online searching, product viewing, and product buying activity of a large online community can be used to infer semantic relationships between queries. We work with a large scale query log consisting of around 115 million queries from eBay. We discuss various techniques to infer semantic relationships among queries and show how the results from these methods can be combined to measure the strength and depict the kinds of relationships. Further, we show how this extraction of relations can be used to improve search relevance, related query recommendations, and recovery from null results in an eCommerce context.
Defining imprecise regions using the web
Authors: Robert C. Pasley
Venue: CIKM
Year: 2008

An overview of PhD research into defining imprecise regions(e.g. "The Midlands" of Great Britain) using unstructured data sources found on the web. A literature review is undertaken and future experiments are suggested. Two preliminary experiments, one into geo-tagging, the other into geographic coverage on the web, are described.
Clustering multi-way data via adaptive subspace iteration
Authors: Wei Peng, Tao Li, Bo Shao
Venue: CIKM
Year: 2008

Clustering multi-way data is a very important research topic due to the intrinsic rich structures in real-world datasets. In this paper, we propose the subspace clustering algorithm on multi-way data, called ASI-T (Adaptive Subspace Iteration on Tensor). ASI-T is a special version of High Order SVD (HOSVD), and it simultaneously performs subspace identification using 2DSVD and data clustering using K-Means. The experimental results on synthetic data and real-world data demonstrate the effectiveness of ASI-T.
Ranked feature fusion models for ad hoc retrieval
Authors: Jeremy Pickens, Gene Golovchinsky
Venue: CIKM
Year: 2008

We introduce the Ranked Feature Fusion framework for information retrieval system design. Typical information retrieval formalisms such as the vector space model, the best-match model and the language model first combine features (such as term frequency and document length) into a unified representation, and then use the representation to rank documents. We take the opposite approach: Documents are first ranked by the relevance of a single feature value and are assigned scores based on their relative ordering within the collection. A separate ranked list is created for every feature value and these lists are then fused to produce a final document scoring. This new "rank then combine" approach is extensively evaluated and is shown to be as effective as traditional "combine then rank" approaches. The model is easy to understand and contains fewer parameters than other approaches. Finally, the model is easy to extend (integration of new features is trivial) and modify. This advantage includes but is not limited to relevance feedback and distribution flattening.
A heuristic approach for checking containment of generalized tree-pattern queries
Authors: Pawel Placek, Dimitri Theodoratos, Stefanos Souldatos, Theodore Dalamagas, Timos K. Sellis
Venue: CIKM
Year: 2008

Query processing techniques for XML data have focused mainly on tree-pattern queries (TPQs). However, the need for querying XML data sources whose structure is very complex or not fully known to the user, and the need to integrate multiple XML data sources with different structures have driven, recently, the suggestion of query languages that relax the complete specification of a tree pattern. In order to implement the processing of such languages in current DBMSs, their containment problem has to be efficiently solved.
Dr. Searcher and Mr. Browser: a unified hyperlink-click graph
Authors: Barbara Poblete, Carlos Castillo, Aristides Gionis
Venue: CIKM
Year: 2008

We introduce a unified graph representation of the Web, which includes both structural and usage information. We model this graph using a simple union of the Web's hyperlink and click graphs. The hyperlink graph expresses link structure among Web pages, while the click graph is a bipartite graph of queries and documents denoting users' searching behavior extracted from a search engine's query log.
Coreex: content extraction from online news articles
Authors: Jyotika Prasad, Andreas Paepcke
Venue: CIKM
Year: 2008

We developed and tested a heuristic technique for extracting the main article from news site Web pages. We construct the DOM tree of the page and score every node based on the amount of text, the number of links it contains and additional heuristics. The method is site-independent and does not use any language-based features. We tested our algorithm on a set of 1120 news article pages from 27 domains. Our algorithm achieved over 97% precision and 98% recall, and an average processing speed of under 15ms per page.
A step towards incremental maintenance of the composed schema mapping
Authors: Gang Qian, Yisheng Dong
Venue: CIKM
Year: 2008

Schema mapping plays a fundamental role in modern information systems. Mapping composition is an operator that combines a chain of successive schema mappings into a single schema mapping. By pre-computing the composed schema mapping, the system can achieve significant performance benefits. However, when a change occurs on any mapping in the chain, the composed schema mapping has to be maintained correspondingly. In this paper we consider a restricted form of the problem in the XML setting and propose an incremental maintenance approach. Specifically, given a chain of successive mappings, we transform intermediately them into trees that consist of atomic rules and then divide the composition into sub-compositions of the atomic rules. The dividing composition approach provides a fine-grained perspective of the composition relationships between the mappings. We depict such information through an auxiliary data structure called composition relationship graph (CRG). When changes occur on any mapping in the chain, the corresponding maintenance algorithms are developed based on the dividing approach and the CRG, which compute the changes on the composed mapping and then repair it into the new version, such that the computation involves only the atomic rules that are relevant with the maintenance. We evaluate our maintenance approach and report the first experiments results, which show that it is efficient.
Using tag semantic network for keyphrase extraction in blogs
Authors: Lizhen Qu, Christof Müller, Iryna Gurevych
Venue: CIKM
Year: 2008

Folksonomies provide a comfortable way to search and browse the blogosphere. As the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach that extract tags from posts using a tag semantic network. We evaluate the two methods on a blog dataset and observe an improvement in F1-measure from 0.23 to 0.50 when compared to the baseline system.
Biological pathways as features for microarray data classification
Authors: Brian Quanz, Meeyoung Park, Jun Huan
Venue: CIKM
Year: 2008

Classification using microarray gene expression data is an important task in bioinformatics. Due to the high dimensionality and small sample size that characterizes microarray data, there has recently been a drive to incorporate any available information in addition to the expression data in the classification process. As a result, much work has begun on selecting biological pathways that are closely related to a clinical outcome of interest using the gene expression data, and incorporating this pathway information opens up new avenues for classification. As opposed to previous approaches that consider individual genes as features, we propose a new approach that treats biological pathways as features. Each pathway found to be significantly related to an outcome of interest is treated as a feature, and is mapped to a feature value. We define several methods for mapping pathways to features, and compare the performance of several classifiers using our feature transformations to that of the classifiers using individual genes as features for different feature selection methods.
A measure for cluster cohesion in semantic overlay networks
Authors: Paraskevi Raftopoulou, Euripides G. M. Petrakis
Venue: CIKM
Year: 2008

Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups by means of a rewiring procedure that is periodically executed by each peer. Rewiring proceeds by establishing new connections to similar peers, and by discarding connections that are outdated or pointing to dissimilar peers. This process aims at improving cluster quality (how well peers with similar content are clustered together) and by this, at improving the flow of information in the network by reducing the number of messages that are exchanged. Therefore, measuring the quality of clustering is an important issue by itself. This is exactly the issue this work is dealing with. In this paper, we introduce a new clustering measure that takes into account the whole neighborhood of a peer (rather than its direct neighbors) thus, providing better insight on the quality of the underlying clustered organisation. Our experimental evaluation with real-word data and queries confirms our assumption that the new measure is better suited for measuring clustering quality than other known measures, such as the (generalised) clustering coefficient.
Using the current browsing context to improve search relevance
Authors: Mandar Rahurkar, Silviu Cucerzan
Venue: CIKM
Year: 2008

In this paper, we investigate the problem of improving the relevance of a Web search engine by adapting it to the dynamic needs of the user. We examine a representative case of sudden information need change, namely the exposure of the user to news data. In our earlier work we showed that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We explore several methods of biasing the search by performing query expansion and re-ranking of the search results of a major search engine for queries identified as good candidates for contextualization. We show that these methods highly increase the similarity between the obtained top 10 search results and the most recently browsed document.
A novel optimization approach to efficiently process aggregate similarity queries in metric access methods
Authors: Humberto Luiz Razente, Maria Camila Nardini Barioni, Agma J. M. Traina, Christos Faloutsos, Caetano Traina Jr.
Venue: CIKM
Year: 2008

A similarity query considers an element as the query center and searches a dataset to find either the elements far up to a bounding radius or the k nearest ones from the query center. Several algorithms have been developed to efficiently execute similarity queries. However, there are queries that require more than one center, which we call Aggregate Similarity Queries. Such queries appear when the user gives multiple desirable examples, and requests data elements that are similar to all of the examples, as in the case of applying relevance feedback. Here we give the first algorithms that can handle aggregate similarity queries on Metric Access Methods (MAM) such as the M-tree and Slim-tree. Our method, which we call Metric Aggregate Similarity Search (MASS) has the following properties: (a) it requires only the triangle inequality property; (b) it guarantees no false-dismissals, as we prove that it lower-bounds the aggregate distance scores; (c) it can work with any MAM; (d) it can handle any number of query centers, which are either scattered all over the space or concentrated on a restricted region. Experiments on both real and synthetic data show that our method scales on both the number of elements and, if the dataset is in a spatial domain, also on its dimensionality. Moreover, it achieves better results than previous related methods.
Concept search in Urdu
Authors: Kashif Riaz
Venue: CIKM
Year: 2008

This paper describes a thesis proposal to do concept search in non English and non European languages. Urdu is chosen as an example language because of its unique nature, morphology and a large number of speakers. Besides its importance, Urdu does not have adequate language resources to do intellectual research in Information Retrieval (IR). It is shown that methods used for English language for concept searching are inadequate for Urdu. Some novel approaches for concept searching are also presented. Pre-processing IR tasks such as stop word identification and stemming require complex research for a morphological rich language like Urdu. Named-entity identification is hypothesized to be useful in determining the concept being sought by the user and research plan includes an implementation of named-entity identification for Urdu. An Urdu language toolkit will be made available to the IR community for Urdu language processing. Finally, a TREC like evaluation criteria is presented with relevance judgments, test collection and queries for Urdu IR.
Baseline for Urdu IR evaluation
Authors: Kashif Riaz
Venue: CIKM
Year: 2008

Goal of conferences like TREC, TIPSTER, NTCIR, CLEF is to judge the performance of different algorithms. Most of these conferences have tracks that deal with new and innovative information retrieval problems, but none has tackled to work with Urdu data, primarily because of the lack of resources. In this paper we present a baseline for Urdu IR evaluation along with resources necessary to do the task. The goal of this paper is to explore the strategy for creation of the test reference collection for Urdu Information Retrieval. A small test reference collection is presented composed of news articles from the Web along with some experiments to show its effectiveness.
Microarray data analysis with pca in a dbms
Authors: Waree Rinsurongkawong, Carlos Ordonez
Venue: CIKM
Year: 2008

Microarray data sets contain expression levels of thousands of genes. The statistical analysis of such data sets is typically performed outside a DBMS with statistical packages or mathematical libraries. In this work, we focus on analyzing them inside the DBMS. This is a difficult problem because microarray data sets have high dimensionality, but small size. First, due to DBMS limitations on a maximum number of columns per table, the data set has to be pivoted and transformed before analysis. More importantly, the correlation matrix on tens of thousands of genes has millions of values. While most high dimensional data sets can be analyzed with the classical PCA method, small, but high dimensional, data sets can only be analyzed with Singular Value Decomposition (SVD). We adapt the Householder tridiagonalization and QR factorization numerical methods to solve SVD inside the DBMS. Since these mathematical methods require many matrix operations, which are hard to express in SQL, query optimizations and efficient UDFs are developed to get good performance. Our proposed techniques achieve processing times comparable with those from the R package, a well-known statistical tool. We experimentally show our methods scale well with high dimensionality.
Exploiting temporal contexts in text classification
Authors: Leonardo Rocha, Fernando Mourão, Adriano C. M. Pereira, Marcos André Gonçalves, Wagner Meira Jr.
Venue: CIKM
Year: 2008

Due to the increasing amount of information being stored and accessible through the Web, Automatic Document Classification (ADC) has become an important research topic. ADC usually employs a supervised learning strategy, where we first build a classification model using pre-classified documents and then use it to classify unseen documents. One major challenge in building classifiers is dealing with the temporal evolution of the characteristics of the documents and the classes to which they belong. However, most of the current techniques for ADC do not consider this evolution while building and using the models. Previous results show that the performance of classifiers may be affected by three different temporal effects (class distribution, term distribution and class similarity). Further, it is shown that using just portions of the pre-classified documents, which we call contexts, for building the classifiers, result in better performance, as a consequence of the minimization of the aforementioned effects.
Corpus microsurgery: criteria optimization for medical cross-language ir
Authors: Monica Rogati, Yiming Yang, Jaime G. Carbonell
Venue: CIKM
Year: 2008

Automatic subset selection from a parallel corpus significantly cross-lingual information retrieval (CLIR) performance, in addition to increasing its efficiency. Our selection method extracts relevant training data by incorporating additional criteria (i.e. estimated corpus quality, taxonomy projection and size) in addition to lexical-based criteria. The challenge lies in combining these criteria using a meaningful scoring function that can be used for ranking parallel sentence candidates. We choose weighted geometric mean for its soft-AND properties, and we optimize criteria weights by wrapping the CLIR task in an optimization shell. Due to the indeterminate nature of the search space convexity properties, we have explored continuous reactive tabu search (CRTS), a global optimization method. We use a large parallel corpus in the medical domain to examine the effect of adaptation criteria and their combination on CLIR performance. In our experiments, 100 selected sentences yield 90% of the performance obtained with 5,000 times more in-domain parallel sentences. Our optimized criteria weights considerably outperform the uniform distribution baseline, as well as lexical
Using sequence classification for filtering web pages
Authors: Binyamin Rosenfeld, Ronen Feldman, Lyle H. Ungar
Venue: CIKM
Year: 2008

Web pages often contain text that is irrelevant to their main content, such as advertisements, generic format elements, and references to other pages on the same site. When used by automatic content-processing systems, e.g., for Web indexing, text classification, or information extraction, this irrelevant text often produces substantial amount of noise. This paper describes a trainable filtering system based on a feature-rich sequence classifier that removes irrelevant parts from pages, while keeping the content intact. Most of the features the system uses are purely form-related: HTML tags and their positions, sizes of elements, etc. This keeps the system general and domain-independent. We also experiment with content words and show that while they perform very poorly alone, they can slightly improve the performance of pure-form features, without jeopardizing the domain-independence. Our system achieves very high accuracy (95% and above) on several collections of Web pages. We also do a series of tests with different features and different classifiers, comparing the contribution of different components to the system performance, and comparing two known sequence classifiers, Robust Risk Minimization (RRM) and Conditional Random Fields (CRF), in a novel setting.
Comparing metrics across TREC and NTCIR: the robustness to system bias
Authors: Tetsuya Sakai
Venue: CIKM
Year: 2008

Test collections are growing larger, and relevance data constructed through pooling are suspected of becoming more and more incomplete and biased. Several studies have used evaluation metrics specifically designed to handle this problem, but most of them have only examined the metrics under incomplete but unbiased conditions, using random samples of the original relevance data. This paper examines nine metrics in a more realistic setting, by reducing the number of pooled systems. Even though previous work has shown that metrics based on a condensed list, obtained by removing all unjudged documents from the original ranked list, are effective for handling very incomplete but unbiased relevance data, we show that these results do not hold in the presence of system bias. In our experiments using TREC and NTCIR data, we first show that condensed-list metrics overestimate new systems while traditional metrics underestimate them, and that the overestimation tends to be larger than the underestimation. We then show that, when relevance data is heavily biased towards a single team or a few teams, the condensed-list versions of Average Precision (AP), Q-measure (Q) and normalised Discounted Cumulative Gain (nDCG), which we call AP', Q' and nDCG', are not necessarily superior to the original metrics in terms of discriminative power, i.e., the overall ability to detect pairwise statistical significance. Nevertheless, even under system bias, AP' and Q' are generally more discriminative than bpref and the condensed-list version of Rank-Biased Precision (RBP), which we call RBP'.
Network dynamics of scholarship: a social network analysis of digital library community
Authors: Monica Sharma, Shalini R. Urs
Venue: CIKM
Year: 2008

The purpose of this research is to investigate the networks of ''academic community'' of Digital Library using various Social Network Analysis (SNA) tools and metrics. This study is expected to identify the figureheads who are responsible for shaping the emerging multidisciplinary community of Digital Library. It would also reveal the factors, which are involved in the networking of academic community. Application of SNA techniques to trace the linkages between community members is not only innovative but also expected to be fruitful in understanding the shaping of a community.
Event gazetteers for navigating humanities resources
Authors: Ryan Shaw
Venue: CIKM
Year: 2008

In history and the other humanities, events and narrative sequences of events are often of primary interest. Yet while named events sometimes appear as subject headings, systems for knowledge organization generally do not provide facilities for identifying and disambiguating events as they do for person or place names. As a result opportunities for collocating resources that pertain to specific events have been limited, and support for navigating among related resources by way of the various relationships represented by events has been weak. The accelerating digitization of document and artifact collections and the ongoing development of digital metadata infrastructure make this an excellent time to address this oversight. This paper describes ongoing research to develop gazetteers for representing events and their relationships and best practices for using such gazetteers to enhance digital resources and information services.
Relating dependent indexes using dempster-shafer theory
Authors: Lixin Shi, Jian-Yun Nie, Guihong Cao
Venue: CIKM
Year: 2008

Traditional information retrieval (IR) approaches assume that the indexing terms are independent, which is not true in reality. Although some previous studies have tried to consider term relationships, strong simplifications had to be made at the very basic indexing step, namely, dependent terms are assigned independent counts or probabilities.
Automatic query structuring from sentences for Japanese web retrieval
Authors: Tetsuya Shibata, Koji Eguchi
Venue: CIKM
Year: 2008

This paper proposes a query structuring method by analyzing Japanese natural language sentences that users input. We use linguistic structure information obtained by morphological analysis and dependency parsing on the natural language inputs. Our proposed method converts a sentence into a structured query on the basis of linguistic structures such as phrases and modification relations. Such structured queries enable effective retrieval with reasonable efficiency. To evaluate our proposed method, we compare it with the method using no structures at all, using a 100-gibabyte web collection mostly written in Japanese. We demonstrate through the experiments that mean average precision was improved about 8.6% using our query structuring method alone, and about 22.5% by combining our query structuring method with pseudo-relevance feedback.