OpenRaiser/Intern-Atlas huggingface dataset:
Exploration-exploitation tradeoff in interactive relevance feedback
Authors: Maryam Karimzadehgan, ChengXiang Zhai
Venue: CIKM
Year: 2010

We study an interesting optimization problem in interactive feedback that aims at optimizing the tradeoff between presenting search results with the highest immediate utility to a user (but not necessarily most useful for collecting feedback information) and presenting search results with the best potential for collecting useful feedback information (but not necessarily the most useful documents from a user's perspective). Optimizing such an exploration-exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback. We frame this tradeoff as a problem of optimizing the diversification of search results. We propose a machine learning approach to adaptively optimizing the diversification of search results for each query so as to optimize the overall utility in an entire session. Experiment results show that the proposed learning approach can effectively optimize the exploration-exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation without exploration.
Search as if you were in your home town: geographic search by regional context and dynamic feature-space selection
Authors: Makoto P. Kato, Hiroaki Ohshima, Satoshi Oyama, Katsumi Tanaka
Venue: CIKM
Year: 2010

We propose a query-by-example geographic object search method for users that do not know well about the place they are in. Geographic objects, such as restaurants, are often retrieved using an attribute-based or keyword query. These queries, however, are difficult to use for users that have little knowledge on the place where they want to search. The proposed query-by-example method allows users to query by selecting examples in familiar places for retrieving objects in unfamiliar places. One of the challenges is to predict an effective distance metric, which varies for individuals. Another challenge is to calculate the distance between objects in heterogeneous domains considering the feature gap between them, for example, restaurants in Japan and China. Our proposed method is used to robustly estimate the distance metric by amplifying the difference between selected and non-selected examples. By using the distance metric, each object in a familiar domain is evenly assigned to one in an unfamiliar domain to eliminate the difference between those domains. We developed a restaurant search using data obtained from a Japanese restaurant Web guide to evaluate our method.
Latent interest-topic model: finding the causal relationships behind dyadic data
Authors: Noriaki Kawamae
Venue: CIKM
Year: 2010

This paper presents a hierarchical generative model that captures the latent relation of cause and effect underlying user behavioral-originated data such as papers, twitter and purchase history. Our proposel, the Latent Interest Topic model (LIT), introduces a latent variable into each document and each author layor in a coherent generative model. We call the former variable the document class, and the latter variable the author class, where these classes are indicator variables that allow the inclusion of different types of probability, and can be shared over documents with similar content and authors with similar interests, respectively. Significantly, unlike other works, LIT differentiates, respectively, document topics and user interests by using these classes. Consequently, LIT is superior to previous models in explaining the causal relationships behind the data by merging similar distributions; it also makes the computation process easier. Experiments on a research paper corpus show that the proposed model can well capture document and author classes, and reduce the dimensionality of documents to a low-dimensional author-document space, making it useful as a generative model.
Embedding tolerance relations in formal concept analysis: an application in information fusion
Authors: Mehdi Kaytoue, Zainab Assaghir, Amedeo Napoli, Sergei O. Kuznetsov
Venue: CIKM
Year: 2010

This paper shows how to embed a similarity relation between complex descriptions in concept lattices. We formalize similarity by a tolerance relation: objects are grouped within a same concept when having similar descriptions, extending the ability of FCA to deal with complex data. We propose two different approaches.~A first classical manner defines a discretization procedure. A second way consists in representing data by pattern structures, from which a pattern concept lattice can be constructed directly. In this case, considering a tolerance relation can be mathematically defined by a projection in a meet-semi-lattice. This allows to use concept lattices for their knowledge representation and reasoning abilities without transforming data. We show finally that resulting lattices are useful for solving information fusion problems.
3rd BooksOnline workshop: research advances in large digital book repositories and complementary media
Authors: Gabriella Kazai, Peter Brusilovsky
Venue: CIKM
Year: 2010

The goal of the 3rd BooksOnline Workshop is to bring together researchers and industry practitioners in information retrieval, digital libraries, e-books, human computer interaction, publishing industry, and online book services to foster progress on addressing challenges and exploring opportunities around large collections of digital books and complementary media. Towards this goal, the workshop programme consists of contributions both from academia and industry, including two keynote talks: James Crawford from Google Books and John Mark Ockerbloom from the University of Pennsylvania.
Connecting the local and the online in information management
Authors: Gabriella Kazai, Natasa Milic-Frayling, Tim Haughton, Natalia Manola, Katerina Iatropoulou, Antonis Lempesis, Paolo Manghi, Marko Mikulicic
Venue: CIKM
Year: 2010

With the popularity of social media sites, digital content is increasingly stored and managed online. At the same time, the desktop and local storage continues to provide a personal environment in which users perform their daily tasks. Thus, to accomplish their tasks, users need to continuously switch between local and remote resources and applications, often carrying the burden of coordinating and synchronizing these in a consistent way. In this demonstration, we describe a system, called ScholarLynk, that bridges the local and online worlds and allows users to manage both local and online resources in a uniform way and in collaboration with others.
Meta-metadata: a metadata semantics language for collection representation applications
Authors: Andruid Kerne, Yin Qu, Andrew M. Webb, Sashikanth Damaraju, Nic Lupfer, Abhinav Mathur
Venue: CIKM
Year: 2010

Collecting, organizing, and thinking about diverse information resources is the keystone of meaningful digital information experiences, from research to education to leisure. Metadata semantics are crucial for organizing collections, yet their structural diversity exacerbates problems of obtaining and manipulating them, strewing end users and application developers amidst the shadows of a proverbial tower of Babel. We introduce meta-metadata, a language and software architecture addressing a metadata semantics lifecycle: (1) data structures for representation of metadata in programs; (2) metadata extraction from information resources; (3) semantic actions that connect metadata to collection representation applications; and (4) rules for presentation to users. The language enables power users to author metadata semantics wrappers that generalize template-based information sources. The architecture supports development of independent collection representation applications that reuse wrappers. The initial meta-metadata repository of information source wrappers includes Google, Flickr, Yahoo, IMDb, Wikipedia, and the ACM Portal. Case studies validate the approach.
Quantifying uncertainty in multi-dimensional cardinality estimations
Authors: Andranik Khachatryan, Klemens Böhm
Venue: CIKM
Year: 2010

We propose a method for predicting the cardinality distribution of a multi-dimensional query. Compared to conventional 'point-based' estimates, distribution-based estimates enable the query optimizer to predict the cost of a query plan more accurately, as we show experimentally. Our method is computationally efficient and works on top of a histogram already in place. It does not store any information additional to the histogram. Our experiments show that the quality of the predictions with the new method is high.
Skyline query processing for uncertain data
Authors: Mohamed E. Khalefa, Mohamed F. Mokbel, Justin J. Levandoski
Venue: CIKM
Year: 2010

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm.
Building a semantic representation for personal information
Authors: Jin Young Kim, Anton Bakalov, David A. Smith, W. Bruce Croft
Venue: CIKM
Year: 2010

A typical collection of personal information contains many documents and mentions many concepts (e.g., person names, events, etc.). In this environment, associative browsing between these concepts and documents can be useful as a complement for search. Previous approaches in the area of semantic desktops aimed at addressing this task. However, they were not practical because they require tedious manual annotation by the user.
Efficient wikipedia-based semantic interpreter by exploiting top-k processing
Authors: Jong Wook Kim, Ashwin Kashyap, Dekai Li, Sandilya Bhamidipati
Venue: CIKM
Year: 2010

Proper representation of the meaning of texts is crucial to enhancing many data mining and information retrieval tasks, including clustering, computing semantic relatedness between texts, and searching. Representing of texts in the concept space derived from Wikipedia has received growing attention recently, due to its comprehensiveness and expertise, This concept-based representation is capable of extracting semantic relatedness between texts that cannot be deduced with the bag of words model. A key obstacle, however, for using Wikipedia as a semantic interpreter is that the sheer size of the concepts derived from Wikipedia makes it hard to efficiently map texts into concept-space. In this paper, we develop an efficient algorithm which is able to represent the meaning of a text by using the concepts that best match it. In particular, our approach first computes the approximate top-k concepts that are most relevant to the given text. We then leverage these concepts for representing the meaning of the given text. The experimental results show that the proposed technique provides significant gains in execution time over current solutions to the problem.
Entity classification by bag of Wikipedia articles
Authors: Tomás Kliegr
Venue: CIKM
Year: 2010

The input for a Bag-of-Articles (BOA) classifier is a set of unlabeled entities - noun chunks and a set of target labeled entities - Wikipedia articles. The classifier locates Wikipedia articles that might define the unlabeled entity and performs disambiguation selecting one. Both unlabeled and labeled entity is represented with the proposed BOA term weight vector, which is created by aggregating term weight vectors of articles related to the Wikipedia article defining it. The label is assigned by choosing the closest labeled entity, also a BOA term weight vector, with cosine similarity. The paper formally defines the BOA entity representation and BOA-based entity classification and presents a partial software implementation. A BOA-based disambiguation algorithm is presented as a planned extension.
Relational feature engineering of natural language processing
Authors: Hamidreza Kobdani, Hinrich Schütze, Andre Burkovski, Wiltrud Kessler, Gunther Heidemann
Venue: CIKM
Year: 2010

We present a new framework for feature engineering of natural language processing that is based on a relational data model of text. It includes fast and flexible methods for implementing and extracting new features and thereby reduces the effort of creating an NLP system for a particular task.
Using Wikipedia categories for compact representations of chemical documents
Authors: Benjamin Köhncke, Wolf-Tilo Balke
Venue: CIKM
Year: 2010

Today, Web pages are usually accessed using text search engines, whereas documents stored in the deep Web are accessed through domain-specific Web portals. These portals rely on external knowledge bases, respectively ontologies, mapping documents to more general concepts allowing for suitable classifications and navigational browsing. Since automatically generated ontologies are still not satisfactory for advanced information retrieval tasks, most portals heavily rely on hand-crafted domain-specific ontologies. This, however, also leads to high creation and maintaining costs. On the other hand, a freely available community maintained, if somewhat general, knowledge base is offered by Wikipedia. During the last years the coverage of Wikipedia has reached a large pool of information including articles from almost all domains. In this paper, we investigate the use of Wikipedia categories to describe the content of chemical documents in a compact form. We compare the results to the domain-specific ChEBI ontology and the results show that Wikipedia categories indeed allow useful descriptions for chemical documents that are even better than descriptions from the ChEBI ontology.
Temporal query log profiling to improve web search ranking
Authors: Alexander Kotov, Pranam Kolari, Lei Duan, Yi Chang
Venue: CIKM
Year: 2010

Temporal information can be leveraged and incorporated to improve web search ranking. In this work, we propose a method to improve the ranking of search results by identifying the fundamental properties of temporal behavior of low-quality hosts and spam-prone queries in search logs and modeling those properties as quantifiable features. In particular, we introduce the concepts of host churn, a measure of changes in host visibility for user queries, and query volatility, a measure of semantic instability of query results, and propose the methods for construction of temporal profiles from search query logs that can be used for estimation of a set of features based on the introduced concepts. The utility of the proposed concepts has been experimentally demonstrated for two language-independent search tasks: the regression-based ranking of search results and a novel classification problem of detecting spam-prone queries introduced in this work.
Learning to blend rankings: a monotonic transformation to blend rankings from heterogeneous domains
Authors: Zhenzhen Kou, Yi Chang, Zhaohui Zheng, Hongyuan Zha
Venue: CIKM
Year: 2010

There have been great needs to develop effective methods for combining multiple rankings from heterogeneous domains into one single rank list arising from many recent web search applications, such as integrating web search results from multiple engines, facets, or verticals. We define this problem as Learning to blend rankings from multiple domains. We propose a class of learning-to-blend methods that learn a monotonically increasing transformation for each ranking so that the rank order in each domain is preserved and the transformed values are comparable across multiple rankings. The transformation learning can be tackled by solving a quadratic programming problem. The novel machine learning method for blending multiple ranking lists is evaluated with queries sampled from a commercial search engine and a promising improvement of Discounted Cumulative Gain has been observed.
Network growth and the spectral evolution model
Authors: Jérôme Kunegis, Damien Fay, Christian Bauckhage
Venue: CIKM
Year: 2010

We introduce and study the spectral evolution model, which characterizes the growth of large networks in terms of the eigenvalue decomposition of their adjacency matrices: In large networks, changes over time result in a change of a graph's spectrum, leaving the eigenvectors unchanged. We validate this hypothesis for several large social, collaboration, authorship, rating, citation, communication and tagging networks, covering unipartite, bipartite, signed and unsigned graphs. Following these observations, we introduce a link prediction algorithm based on the extrapolation of a network's spectral evolution. This new link prediction method generalizes several common graph kernels that can be expressed as spectral transformations. In contrast to these graph kernels, the spectral extrapolation algorithm does not make assumptions about speci c growth patterns beyond the spectral evolution model. We thus show that it performs particularly well for networks with irregular, but spectral, growth patterns.
Learning to rank relevant and novel documents through user feedback
Authors: Abhimanyu Lad, Yiming Yang
Venue: CIKM
Year: 2010

We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several desirable properties: (i) It measures retrieval performance in terms of relevant as well as novel information, (ii) gives more importance to top ranks to reflect common browsing behavior of users, as opposed to existing objective functions based on set-coverage, (iii) accommodates different levels of tolerance towards redundancy, which is not taken into account by existing evaluation measures, and (iv) extends naturally to the evaluation of session-based retrieval comprising multiple ranked lists. Our ground truth is defined in terms of information which are obviously not known to the retrieval system when processing a new user query. Therefore, our approach uses observable query and document features (words and named entities) as surrogates for nuggets, whose weights are learned based on user feedback in an iterative search session. The ranked list is produced to maximize the weighted coverage of these surrogate nuggets. The optimization of such coverage-based metrics is known to be NP-hard. Therefore, we use a greedy algorithm and show that it guarantees good performance due to the submodularity of the objective function. Our experiments on Topic Detection and Tracking data show that the proposed approach represents an efficient and effective retrieval strategy for maximizing EGU, as compared to a purely-relevance based ranking approach that uses Indri, as well as a MMR-based approach for non-redundant ranking.
Semantic annotations for digital investigations
Authors: Nikolaos Lagos, Stefania Castellani, Aaron N. Kaplan
Venue: CIKM
Year: 2010

In this paper, we describe how we believe semantic annotations can help in the provision of support for digital investigation activities for analysts searching for information in large document collections. In particular, we are interested in developing tools to support the activities of lawyers in corporate litigation. In current applications, information such as key characters and events and relations among them has to be mostly manually identified and collected from the collection of documents. We describe here our current and future work on the construction of a system that provides tools and mechanisms for helping users better extract, navigate, select and store such information. We also discuss interesting challenges that we have identified during this work.
Improved latent concept expansion using hierarchical markov random fields
Authors: Hao Lang, Donald Metzler, Bin Wang, Jin-Tao Li
Venue: CIKM
Year: 2010

Most existing query expansion approaches for ad-hoc retrieval adopt overly simplistic textual representations that treat documents as bags of words and ignore inherent document structure. These simple representations often lead to incorrect independence assumptions in the proposed approaches and result in limited retrieval effectiveness. In this paper, we propose a novel query expansion technique that models the various types of dependencies that exist between original query terms and expansion terms within a robust, unified framework. The proposed model is called Hierarchical Markov random fields (HMRFs), based on Latent Concept Expansion (LCE). By exploiting implicit (or explicit) hierarchical structure within documents, HMRFs can incorporate hierarchical interactions which are important for modeling term dependencies in an efficient manner. Our rigorous experimental evaluation carried out using several TREC data sets shows that our proposed query expansion technique consistently and significantly outperforms the current state-of-the-art query expansion approaches, including relevance-based language models and LCE.
XML schema computations: schema compatibility testing and subschema extraction
Authors: Thomas Yau-tat Lee, David Wai-Wok Cheung
Venue: CIKM
Year: 2010

In this paper, we propose new models and algorithms to perform practical computations on W3C XML Schemas, which are schema minimization, schema equivalence testing, subschema testing and subschema extraction. We have conducted experiments on an e-commerce standard XSD called xCBL to demonstrate the effectiveness of our algorithms. One experiment has refuted the claim that the xCBL 3.5 XSD is compatible with the xCBL 3.0 XSD. Another experiment has shown that the xCBL XSDs can be effectively trimmed into small subschemas for specific applications, which has significantly reduced schema processing time. © 2010 ACM.
Mining interesting link formation rules in social networks
Authors: Cane Wing-ki Leung, Ee-Peng Lim, David Lo, Jianshu Weng
Venue: CIKM
Year: 2010

Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework for measuring the frequency and significance of LF-rules, we introduce the notion of expected support to account for the extent to which LF-rules exist in a social network by chance. Specifically, only LF-rules with higher-than-expected support are considered interesting. We conduct empirical studies on two real-world social networks, namely Epinions and myGamma. We report interesting LF-rules mined from the two networks, and compare our findings with earlier findings in social network analysis.