OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating partial tree-pattern queries on XML streams
Authors: Xiaoying Wu, Dimitri Theodoratos
Venue: CIKM
Year: 2008

The streaming evaluation is a popular way of evaluating queries on XML documents. Besides its many advantages, it is also the only option for a number of important XML applications. Unfortunately, existing algorithms focus almost exclusively on tree-pattern queries (TPQs). Requirements for flexible querying of XML data have motivated recently the introduction of query languages that are more general and flexible than TPQs.
Some rewrite optimizations of DB2 XQuery navigation
Authors: Guangjun Xie, Qi Cheng, Jarek Gryz, Calisto Zuzarte
Venue: CIKM
Year: 2008

IBM® DB2® 9 is a truly hybrid commercial database system that combines XML and relational data. It provides native support for XML storage and indexing, and query evaluation support for XQuery. By building a hybrid system, the designers of DB2 9 were able to use the existing SQL query evaluation and optimization techniques to develop similar methods for XQuery. However, SQL and XQuery are sufficiently different that new optimization techniques can and are being developed in the new XQuery domain. This paper describes a few such techniques, all based on static rewrites of XQuery expressions.
Academic conference homepage understanding using constrained hierarchical conditional random fields
Authors: Xin Xin, Juanzi Li, Jie Tang, Qiong Luo
Venue: CIKM
Year: 2008

We address the problem of academic conference homepage understanding for the Semantic Web. This problem consists of three labeling tasks - labeling conference function pages, function blocks, and attributes. Different from traditional information extraction tasks, the data in academic conference homepages has complex structural dependencies across multiple Web pages. In addition, there are logical constraints in the data. In this paper, we propose a unified approach, Constrained Hierarchical Conditional Random Fields, to accomplish the three labeling tasks simultaneously. In this approach, complex structural dependencies can be well described. Also, the constrained Viterbi algorithm in the inference process can avoid logical errors. Experimental results on real world conference data have demonstrated that this approach performs better than cascaded labeling methods by 3.6% in F1-measure and that the constrained inference process can improve the accuracy by 14.3%. Based on the proposed approach, we develop a prototype system of use-oriented semantic academic conference calendar. The user simply needs to specify what conferences he/she is interested in. Subsequently, the system finds, extracts, and updates the semantic information from the Web, and then builds a calendar automatically for the user. The semantic conference data can be used in other applications, such as finding sponsors and finding experts. The proposed approach can be used in other information extraction tasks as well.
Active relevance feedback for difficult queries
Authors: Zuobing Xu, Ram Akella
Venue: CIKM
Year: 2008

Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback documents, which are abundant if the initial query is difficult. The probabilistic retrieval model has the advantage of being able to naturally improve the estimation of both the relevant and non-relevant models. The Dirichlet compound multinomial (DCM) distribution, which relies on hierarchical Bayesian modeling techniques, is a more appropriate generative model for the probabilistic retrieval model than the traditional multinomial distribution. We propose a new relevance feedback algorithm, based on a mixture model of the DCM distribution, to effectively model the overlaps between the positive and negative feedback documents. Consequently, the new algorithm improves the retrieval performance substantially for difficult queries. To further reduce human relevance evaluation, we propose a new active learning algorithm in conjunction with the new relevance feedback model. The new active learning algorithm implicitly models the diversity, density and relevance of unlabeled data in a transductive experimental design framework. Experimental results on several TREC datasets show that both the relevance feedback and active learning algorithm significantly improve retrieval accuracy.
Entity-based query reformulation using wikipedia
Authors: Yang Xu, Fan Ding, Bin Wang
Venue: CIKM
Year: 2008

Many real world applications increasingly involve both structured data and text, and entity based retrieval is an important problem in this realm. In this paper, we present an automatic query reformulation approach based on entities detected in each query. The aim is to utilize semantics associated with entities for enhancing document retrieval. This is done by expanding a query with terms/phrases related to entities in the query. We exploit Wikipedia as a large repository of entity information. Our reformulated approach consists of three major steps : (1) detect representative entity in a query; (2) expand the query with entity related terms/phrases; and (3) facilitate term dependency features. We evaluate our approach in ad-hoc retrieval task on four TREC collections, including two large web collections. Experiments results show that significant improvement is possible by utilizing entity corresponding information.
Semi-supervised text categorization by active search
Authors: Zenglin Xu, Rong Jin, Kaizhu Huang, Michael R. Lyu, Irwin King
Venue: CIKM
Year: 2008

In automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high classification accuracy. To address this problem, a novel web-assisted text categorization framework is proposed in this paper. Important keywords are first automatically identified from the available labeled documents to form the queries. Search engines are then utilized to retrieve from the Web a multitude of relevant documents, which are then exploited by a semi-supervised framework. To our best knowledge, this work is the first study of this kind. Extensive experimental study shows the encouraging results of the proposed text categorization framework: using Google as the web search engine, the proposed framework is able to reduce the classification error by 30% when compared with the state-of-the-art supervised text categorization method.
Closing the loop in webpage understanding
Authors: Chunyu Yang, Yong Cao, Zaiqing Nie, Jie Zhou, Ji-Rong Wen
Venue: CIKM
Year: 2008

Little work has been done towards an integrated statistical model for understanding webpage structures and processing natural language sentences within the HTML elements. This paper proposed a novel framework called WebNLP which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. Experiments show that the WebNLP framework achieved significantly better performance.
SHOPSMART: product recommendations through technical specifications and user reviews
Authors: Alexander Yates, James Joseph, Ana-Maria Popescu, Alexander D. Cohn, Nick Sillick
Venue: CIKM
Year: 2008

This paper describes a new method for providing recommendations tailored to a user's preferences using text mining techniques and online technical specifications of products. We first learn a model that can predict the price of a product given automatically-determined features describing technical specifications and users' opinions. We then use this model to rank a list of products based on individual users' preferences about various features. On a data set collected from Amazon reviews and online technical specifications, rankings produced by this model rank the best product for a user in the 87th percentile of products in its category, on average. Our approach outperforms several comparison systems by 21 percentiles or more.
PBFilter: indexing flash-resident data through partitioned summaries
Authors: Shaoyi Yin, Philippe Pucheral, Xiaofeng Meng
Venue: CIKM
Year: 2008

NAND Flash has become the most popular persistent data storage medium for mobile and embedded devices. The hardware characteristics of NAND Flash (e.g. page granularity for read/write with a block-erase-before-rewrite constraint, limited number of erase cycles) preclude in-place updates. In this paper, we propose a new indexing scheme, called PBFilter, designed from the outset to exploit the peculiarities of NAND Flash.
Conflict ontology enrichment based on triggers
Authors: Chahnez Zakaria, Olivier Curé, Kamel Smaïli
Venue: CIKM
Year: 2008

In this paper, we propose an ontology-based approach that enables to detect the emergence of relational conflicts between persons that cooperate on computer supported projects. In order to detect these conflicts, we analyze, using this ontology, the e-mails exchanged between these people.
Classifying networked entities with modularity kernels
Authors: Dell Zhang, Robert Mao
Venue: CIKM
Year: 2008

Statistical machine learning techniques for data classification usually assume that all entities are i.i.d. (independent and identically distributed). However, real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network. Although some graph-based classification methods have emerged in recent years, they are not really suitable for complex networks as they do not take the degree distribution of network into consideration. In this paper, we propose a new technique, Modularity Kernel, that can effectively exploit the latent community structure of networked entities for their classification. A number of experiments on hypertext datasets show that our proposed approach leads to excellent classification performance in comparison with the state-of-the-art methods.
REDUS: finding reducible subspaces in high dimensional data
Authors: Xiang Zhang, Feng Pan, Wei Wang
Venue: CIKM
Year: 2008

Finding latent patterns in high dimensional data is an important research problem with numerous applications. The most well known approaches for high dimensional data analysis are feature selection and dimensionality reduction. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging applications, however, scientists are interested in the local latent patterns held by feature subspaces, which may be invisible via any global transformation.In this paper, we investigate the problem of finding strong linear and nonlinear correlations hidden in feature subspaces of high dimensional data. We formalize this problem as identifying reducible subspaces in the full dimensional space. Intuitively, a reducible subspace is a feature subspace whose intrinsic dimensionality is smaller than the number of features. We present an effective algorithm, REDUS, for finding the reducible subspaces. Two key components of our algorithm are finding the overall reducible subspace, and uncovering the individual reducible subspaces from the overall reducible subspace. A broad experimental evaluation demonstrates the effectiveness of our algorithm.
Characterizing and predicting community members from evolutionary and heterogeneous networks
Authors: Qiankun Zhao, Sourav S. Bhowmick, Xin Zheng, Kai Yi
Venue: CIKM
Year: 2008

Mining different types of communities from web data have attracted a lot of research efforts in recent years. However, none of the existing community mining techniques has taken into account both the dynamic as well as heterogeneous nature of web data. In this paper, we propose to characterize and predict community members from the evolution of heterogeneous web data. We first propose a general framework for analyzing the evolution of heterogeneous networks. Then, the academic network, which is extracted from 1 million computer science papers, is used as an example to illustrate the framework. Finally, two example applications of the academic network are presented. Experimental results with a real and very large heterogeneous academic network show that our proposed framework can produce good results in terms of community member recommendation. Also, novel knowledge and insights can be gained by analyzing the community evolution pattern.
Online spam-blog detection through blog search
Authors: Linhong Zhu, Aixin Sun, Byron Choi
Venue: CIKM
Year: 2008

In this work, we propose a novel post-indexing spam-blog (or splog) detection method, which capitalizes on the results returned by blog search engines. More specifically, we analyze the search results of a sequence of temporally-ordered queries returned by a blog search engine, and build and maintain Blog profiles for those blogs whose posts frequently appear in the top-ranked search results. With the blog profiles, 4 splog scoring functions were evaluated using real data collected from a popular blog search engine. Our experiments show that the proposed method could effectively detect splogs with a high accuracy.
Modeling document features for expert finding
Authors: Jianhan Zhu, Dawei Song, Stefan M. Rüger, Xiangji Huang
Venue: CIKM
Year: 2008

We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios.
Can phrase indexing help to process non-phrase queries?
Authors: Mingjie Zhu, Shuming Shi, Nenghai Yu, Ji-Rong Wen
Venue: CIKM
Year: 2008

Modern web search engines, while indexing billions of web pages, are expected to process queries and return results in a very short time. Many approaches have been proposed for efficiently computing top-k query results, but most of them ignore one key factor in the ranking functions of commercial search engines - term-proximity, which is the metric of the distance between query terms in a document. When term-proximity is included in ranking functions, most of the existing top-k algorithms will become inefficient. To address this problem, in this paper we propose to build a compact phrase index to speed up the search process when incorporating the term-proximity factor. The compact phrase index can help more accurately estimate the score upper bounds of unknown documents. The size of the phrase index is controlled by including a small portion of phrases which are possibly helpful for improving search performance. Phrase index has been used to process phrase queries in existing work. It is, however, to the best of our knowledge, the first time that phrase index is used to improve the performance of generic queries. Experimental results show that, compared with the state-of-the-art top-k computation approaches, our approach can reduce average query processing time to 1/5 for typical setttings.
Representative entry selection for profiling blogs
Authors: Jinfeng Zhuang, Steven C. H. Hoi, Aixin Sun, Rong Jin
Venue: CIKM
Year: 2008

Many applications on blog search and mining often meet the challenge of handling huge volume of blog data, in which one single blog could contain hundreds or even thousands of entries. We investigate novel techniques for profiling blogs by selecting a subset of representative entries for each blog. We propose two principles for guiding the entry selection task: representativeness and diversity. Further, we formulate the entry selection task into a combinatorial optimization problem and propose a greedy yet effective algorithm for finding a good approximate solution by exploiting the theory of submodular functions. We suggest blog classification for judging the performance of the proposed entry selection techniques and evaluate their performance on a real blog dataset, in which encouraging results were obtained.
Personalized cluster-based semantically enriched web search for e-learning
Authors: Leyla Zhuhadar, Olfa Nasraoui
Venue: CIKM
Year: 2008

We present an approach for personalized search in an e-learning platform, that takes advantage of semantic Web standards (RDF and OWL) to represent the content and the user profiles. Personalizing the finding of needed information in an e-learning environment based on context requires intelligent methods for representing and matching the learning needs and the variety of learning contexts. Our framework consists of the following phases: (1) building the semantic e-learning domain using the known college and course information as concepts and sub-concepts in a lecture ontology, (2) generating the semantic learner's profile as an ontology from navigation logs that record which lectures have been accessed, (3) clustering the documents to discover more refined sub-concepts (top terms in each cluster) than provided by the available college and course taxonomy, (4) re-ranking the learner's search results based on the matching concepts in the learning content and the user profile, and (5) providing the learner with semantic recommendations during the search process, in the form of terms from the closest matching clusters of their profile. One important aspect of our approach is the combination of an authoritatively supplied taxonomy by the colleges, with the data driven extraction (via clustering) of a taxonomy from the documents themselves, thus making it easier to adapt to different learning platforms, and making it easier to evolve with the document/lecture collection. Our experimental results show that the learner's context can be effectively used for improving the precision and recall in e-learning search, particularly by re-ranking the search results based on the learner's past activities.
A georeferencing multistage method for locating geographic context in web search
Authors: Álvaro Zubizarreta, Pablo de la Fuente, José Manuel Cantera, Mario Arias, Jorge Cabrero Alonso, Guido García Bernardo, César Llamas, Jesús Vegas
Venue: CIKM
Year: 2008

The geographic scope of Web pages is becoming an essential dimension of Web search, especially for mobile users. This paper shows a multistage method for assigning a geographic focus to Web pages (GeoReferencing) according to their text contents. We suggest several heuristics for the disambiguation toponyms and a scoring procedure for focus determination. Furthermore, we provide an experimental methodology for evaluating the accuracy. Finally, we obtained promising results of over 70% accuracy with a city-level resolution.
Using negative voting to diversify answers in non-factoid question answering
Authors: Palakorn Achananuparp, Christopher C. Yang, Xin Chen
Venue: CIKM
Year: 2009

We propose a ranking model to diversify answers of non-factoid questions based on an inverse notion of graph connectivity. By representing a collection of candidate answers as a graph, we posit that novelty, a measure of diversity, is inversely proportional to answer vertices' connectivity. Hence, unlike the typical graph ranking models, which score vertices based on the degree of connectedness, our method assigns a penalty score for a candidate answer if it is strongly connected to other answers. That is, any redundant answers, indicated by a higher inter-sentence similarity, will be ranked lower than those with lower inter-sentence similarity. At the end of the ranking iterations, many redundant answers will be moved toward the bottom on the ranked list. The experimental results show that our method helps diversify answer coverage of non-factoid questions according to F-scores from nugget pyramid evaluation.
Translating relevance scores to probabilities for contextual advertising
Authors: Deepak Agarwal, Evgeniy Gabrilovich, Robert J. Hall, Vanja Josifovski, Rajiv Khanna
Venue: CIKM
Year: 2009

Information retrieval systems conventionally assess document relevance using the bag of words model. Consequently, relevance scores of documents retrieved for different queries are often difficult to compare, as they are computed on different (or even disjoint) sets of textual features. Many tasks, such as federation of search results or global thresholding of relevance scores, require that scores be globally comparable. To achieve this, in this paper we propose methods for non-monotonic transformation of relevance scores into probabilities for a contextual advertising selection engine that uses a vector space model. The calibration of the raw scores is based on historical click data.
Exploiting query views for static index pruning in web search engines
Authors: Ismail Sengör Altingövde, Rifat Ozcan, Özgür Ulusoy
Venue: CIKM
Year: 2009

We propose incorporating query views in a number of static pruning strategies, namely term-centric, document-centric and access-based approaches. These query-view based strategies considerably outperform their counterparts for both disjunctive and conjunctive query processing in Web search engines. Copyright 2009 ACM.
Diverging patterns: discovering significant frequency change dissimilarities in large databases
Authors: Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang
Venue: CIKM
Year: 2009

In this paper, we present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes significantly differently in two data sets, e.g., it changes from a relatively low to a relatively high value in one dataset, but from high to low in the other. In this framework, a measure called diverging ratio is defined and used to discover diverging patterns. We use a four-dimensional vector to represent a pattern, and define the pattern's diverging ratio based on the angular difference between its vectors in two datasets. An algorithm is proposed to mine diverging patterns from a pair of datasets, which makes use of a standard frequent pattern mining algorithm to compute vector components efficiently. We demonstrate the effectiveness of our approach on real-world datasets, showing that the method can reveal novel knowledge from large databases.
A collaborative filtering approach to ad recommendation using the query-ad click graph
Authors: Tasos Anastasakos, Dustin Hillard, Sanjay Kshetramade, Hema Raghavan
Venue: CIKM
Year: 2009

Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a new technique to determine the relevance of an ad document for a search query using click-through data. The method builds on a collaborative filtering approach to discover new ads related to a query using a click graph. It is implemented on a graph with several million edges and scales to larger sizes easily. The proposed method is compared to three different baselines that are state-of-the-art for a commercial search engine. Evaluations on editorial data indicate that the model discovers many new ads not retrieved by the baseline methods. The ads from the new approach are on average of better quality than the baselines.