OpenRaiser/Intern-Atlas huggingface dataset:
Possibility and necessity measures for relevance assessment
Authors: Fatma-Zohra Bessai Mechmache, Mohand Boughanem, Zaia Alimazighi
Venue: CIKM
Year: 2007

The major question raised in information retrieval on semi-structured documents relates to the manner of effectively handling the structure and the contents of the document for better answering the user's needs. These needs can be formulated by queries composed of only key words or key words and structural constraints. In this paper, we are interested in Information Retrieval in semi-structured document like XML. For these purposes, we present a model for the semi-structured information retrieval, based on the possibilistic networks. The document - elements and elements - terms relations are modelled by measures of possibility and necessity. In this model, the user's query starts a process of propagation to recover documents or portions of documents necessarily or at least possibly relevant. An example of such a research is proposed in order to illustrate the presented approach.
Utilizing a geometry of context for enhanced implicit feedback
Authors: Massimo Melucci, Ryen W. White
Venue: CIKM
Year: 2007

Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users' needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or directly improve retrieval performance. In this paper we present a geometric framework that utilizes multiple sources of evidence present in this interaction context (e.g., display time, document retention) to develop enhanced implicit feedback models personalized for each user and tailored for each search task. We use rich interaction logs (and associated metadata such as relevance judgments), gathered during a longitudinal user study, as relevance stimuli to compare an implicit feedback algorithm developed using the framework with alternative algorithms. Our findings demonstrate both the effectiveness of our proposed algorithm and the potential value of incorporating multiple sources of interaction evidence when developing implicit feedback algorithms.
Exploiting web 2.0 forallknowledge-based information retrieval
Authors: David N. Milne
Venue: CIKM
Year: 2007

This paper describes ongoing research into obtaining and using knowledge bases to assist information retrieval. These structures are prohibitively expensive to obtain manually, yet automatic approaches have been researched for decades with limited success. This research investigates a potential shortcut: a way to provide knowledge bases automatically, without expecting computers to replace expert human indexers. Instead we aim to replace the professionals with thousands or even millions of amateurs: with the growing community of contributors who form the core of Web 2.0. Specifically we focus on Wikipedia, which represents a rich tapestry of topics and semantics and a huge investment of human effort and judgment. We show how this can be directly exploited to provide manually-defined yet inexpensive knowledge-bases that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We are also concerned with how best to make these structures available to users, and aim to produce a complete knowledge-based retrieval system-both the knowledge base and the tools to apply it-that can be evaluated by how well it assists real users in performing realistic and practical information retrieval tasks. To this end we have developed Koru, a new search engine that offers concrete evidence of the effectiveness of our Web 2.0 based techniques for assisting information retrieval.
ChemXSeer: a digital library and data repository for chemical kinetics
Authors: Prasenjit Mitra, C. Lee Giles, Bingjun Sun, Ying Liu
Venue: CIKM
Year: 2007

In this paper, we describe the ChemXSeer system that hosts data and scholarly articles related to chemical kinetics. Domain scientists have different needs that are not served by general search engines. ChemXSeer enables chemists (and others) to search for chemical formulae. As part of the project, we have designed TableSeer that extracts data from PDF documents and enters them into a database. Scientists also submit the raw data obtained as results of their experiments to the data repository. Metadata is associated with the data and can be searched. A mediated system allows searching across data and documents using the same search query interface using keyword queries.
Collaborative framework for supporting indigenous knowledge management
Authors: Theodora Mondo T. Mwebesa, Venansius Baryamureeba, Ddembe Williams
Venue: CIKM
Year: 2007

Indigenous knowledge (IK) is an integral part of the culture and history of local communities. We need to learn from local communities to enrich the development process. IK systems are also dynamic; new knowledge is continuously added. IK is stored in people's memories, activities and is expressed and communicated orally and this posses a serious threat to its preservation and development. However this depicts Indigenous knowledge Management (IKM) as a complex, global and dynamic issue and hence a need for a collaborative framework that will enhance information exchange. Utilization of collaborative frameworks, are increasingly being used in solving problems whose efficiency depends on an interactive nature. Networking or collaboration allows for a more effective pooling of resources and sharing of experiences and information on indigenous knowledge, both among various individuals and organizations.
A dual-view approach to interactive network visualization
Authors: Galileo Namata, Brian Staats, Lise Getoor, Ben Shneiderman
Venue: CIKM
Year: 2007

Visualizing network data, from tree structures to arbitrarily connected graphs, is a difficult problem in information visualization. A large part of the problem is that in network data, users not only have to visualize the attributes specific to each data item, but also the links specifying how those items are connected to each other. Past approaches to resolving these difficulties focus on zooming, clustering, filtering and applying various methods of laying out nodes and edges. Such approaches, however, focus only on optimizing a network visualization in a single view, limiting the amount of information that can be shown and explored in parallel. Moreover, past approaches do not allow users to cross reference different subsets or aspects of large, complex networks. In this paper, we propose an approach to these limitations using multiple coordinated views of a given network. To illustrate our approach, we implement a tool called DualNet and evaluate the tool with a case study using an email communication network. We show how using multiple coordinated views improves navigation and provides insight into large networks with multiple node and link properties and types.
Metadata management for federated databases
Authors: Carlos Ordonez, Zhibo Chen, Javier García-García
Venue: CIKM
Year: 2007

A federated database consists of several loosely integrated databases, where each database may contain hundreds of tables and thousands of columns, interrelated by complex foreign key relationships. In general, there exists a lot of semistructured data elements outside the database repre-sented by documents ( les), created and updated by mul-tiple users and programs. Documents have references to multiple databases and subsets of their tables and columns. Manually tracking which specic tables and columns are re-ferred to by a document, accessed by a specic program or user is a daunting task. With such a goal in mind, we present a system that builds metadata models for a feder-ated database using a relational database as a central ob-ject type and metadata repository. Metadata includes table and columns coming from logical data models corresponding to each database, as well as documents representing exter-nal semistructured data sources. SQL statements assemble metadata and data types to create objects and relationships as relational tables for easy querying. We discuss potential applications in federated scientic databases. 1.
Measuring referential integrity in distributed databases
Authors: Carlos Ordonez, Javier García-García, Zhibo Chen
Venue: CIKM
Year: 2007

Distributed relational databases are used by different organizations located at multiple sites that work together on common projects. In this article, we focus on distributed relational databases with incomplete and inconsistent content. We propose to measure referential integrity errors in them for integration and interoperability purposes. We propose local and global referential integrity metrics at three levels: column, table and database. We assume each table can be asynchronously updated at any site and new records are periodically broadcasted to all sites. We explain several distributed query optimization issues. Our proposal is useful in database integration, multiple database interoperability and data quality assurance. We discuss applications of our proposal in distributed scientific databases.
Hybrid results merging
Authors: Georgios Paltoglou, Michail Salampasis, Maria Satratzemi
Venue: CIKM
Year: 2007

The problem of results merging in distributed information retrieval environments has been approached by two different directions in research. Estimation approaches attempt to calculate the relevance of the returned documents through ad-hoc methodologies (weighted score merging, regression etc) while download approaches, download all the documents locally, partially or completely, in order to estimate "first hand" their relevance. Both have their advantages and disadvantages. It is assumed that download algorithms are more effective but they are very expensive in terms of time and bandwidth. Estimation approaches on the other hand, usually rely on document relevance scores being returned by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging, rely on a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced reconciles the above two approaches, combining their strengths, while minimizing their weaknesses. It is based on downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. The proposed algorithm is tested in a variety of settings and its performance is found to be better than estimation approaches, while approximating that of download.
Grid-based subspace clustering over data streams
Authors: Nam Hun Park, Won Suk Lee
Venue: CIKM
Year: 2007

A real-life data stream usually contains many dimensions and some dimensional values of its data elements may be missing. In order to effectively extract the on-going change of a data stream with respect to all the subsets of the dimensions of the data stream, a grid-based subspace clustering algorithm is proposed in this paper. Given an n-dimensional data stream, the on-going distribution statistics of data elements in each one-dimension data space is firstly monitored by a list of grid-cells called a sibling list. Once a dense grid-cell of a first-level sibling list becomes a dense unit grid-cell, new second-level sibling lists are created as its child nodes in order to trace any cluster in all possible two-dimensional rectangular subspaces. In such a way, a sibling tree grows up to the nth level at most and a k-dimensional subcluster can be found in the kth level of the sibling tree. The proposed method is comparatively analyzed by a series of experiments to identify its various characteristics.
Mining web multi-resolution community-based popularity for information retrieval
Authors: Laurence Anthony F. Park, Kotagiri Ramamohanarao
Venue: CIKM
Year: 2007

The PageRank algorithm is used in Web information retrieval to calculate a single list of popularity scores for each page in the Web. These popularity scores are used to rank query results when presented to the user. By using the structure of the entire Web to calculate one score per document, we are calculating a general popularity score, not particular to any community. Therefore, the PageRank scores are more suited to general queries. In this paper, we introduce a more general form of PageRank, using Web multi-resolution community-based popularity scores, where each document obtains a popularity score dependent on a given Web community. When a query is related to a specific community, we choose the associated set of popularity scores and order the query results accordingly. Using Web-community based popularity scores, we achieved an 11% increase in precision over PageRank.
Lightweight web-based fact repositories for textual question answering
Authors: Marius Pasca
Venue: CIKM
Year: 2007

Since answers to fact-seeking questions usually reside within small factual text nuggets, often "hidden" within full-length documents, their relevance to a question is not necessarily correlated to the relevance of the full-length document to the question. Yet previous approaches to open-domain textual question answering from large document collections quasi-unanimously employ a document retrieval stage, in order to apply widely different, often expensive answer mining techniques to only a small subset of documents. Depending on the collection size, 95% or more of the documents in the collection (much more in the case of the Web) are left out of the selected subset for any given query, and thus become invisible to subsequent processing stages for actual answer mining. This paper introduces a new model for answer retrieval for question answering. The collection is distilled offline into large repositories of facts. Each fact constitutes a potential direct answer to questions seeking a particular kind of entity or relation, such as questions asking about the date of particular events. Question answering becomes equivalent to online fact retrieval, which greatly simplifies the de-facto system architecture for fact-seeking question answering. In addition to simplicity, experiments on a fact repository acquired from approximately a billion Web documents illustrate the impact of fact repositories in extracting accurate answers to a standard evaluation set of open-domain test questions and additional sets of domain-specific questions.
The role of documents vs. queries in extracting class attributes from text
Authors: Marius Pasca, Benjamin Van Durme, Nikesh Garera
Venue: CIKM
Year: 2007

Challenging the implicit reliance on document collections, this paper discusses the pros and cons of using query logs rather than document collections, as self-contained sources of data in textual information extraction. The differences are quantified as part of a large-scale study on extracting prominent attributes or quantifiable properties of classes (e.g., top speed, price and fuel consumption for CarModel) from unstructured text. In a head-to-head qualitative comparison, a lightweight extraction method produces class attributes that are 45% more accurate on average, when acquired from query logs rather than Web documents.
Using word similarity to eradicate junk emails
Authors: Maria Soledad Pera, Yiu-Kai Ng
Venue: CIKM
Year: 2007

Emails are one of the most commonly used modern communication media these days; however, unsolicited emails obstruct this otherwise fast and convenient technology for information exchange and jeopardize the continuity of this popular communication tool. Waste of valuable resources and time and exposure to offensive content are only a few of the problems that arise as a result of junk emails. In addition, the monetary cost of processing junk emails reaches billions of dollars per year and is absorbed by public users and Internet service providers. Even though there has been extensive work in the past dedicated to eradicate junk emails, none of the existing junk email detection approaches has been highly successful in solving these problems, since spammers have been able to infiltrate existing detection techniques. In this paper, we present a new tool, JunEX, which relies on the content similarity of emails to eradicate junk emails. JunEX compares each incoming email to a core of emails marked as junk by each individual user to identify unwanted emails while reducing the number of legitimate emails treated as junk, which is critical. Conducted experiments on JunEX verify its high accuracy.
Learning to join everything
Authors: Fernando Pereira
Venue: CIKM
Year: 2007

Text, speech, images, video, DNA sequences provide information about entities that people can recognize when looking at a particular instance. But those entities and their attributes and relationships are not directly accessible to queries that join across types of sources. Information extraction methods based on supervised machine learning recognize mentions of entities and relationships of predefined types in different kinds of sources, which can then be used to answer some useful types of queries. However, supervised learning relies on hand-annotated training sets that are difficult to create and limit what types of entities and relationships can be joined for new applications. These limitations have prompted research into unsupervised extraction methods that rely on correlations among sources rather than hand-annotated training sets. While these methods are not yet as accurate as those based on supervised learning, they have the potential for a new query-by-example approach to information integration in which seed sets of query answers are expanded into ranked lists of potential answers by learning occurrence patterns from the seed answers. I will give examples of both types of methods from our research on biomedical information extraction, leading to some ideas on a possible convergence of search and databases through machine learning.
Predictive user click models based on click-through history
Authors: Benjamin Piwowarski, Hugo Zaragoza
Venue: CIKM
Year: 2007

Web search engines consistently collect information about users interaction with the system: they record the query they issued, the URL of presented and selected documents along with their ranking. This information is very valuable: It is a poll over millions of users on the most various topics and it has been used in many ways to mine users interests and preferences. Query logs have the potential to partially alleviate the search engines from thousand of searches by providing a way to predict answers for a subset of queries and users without knowing the content of a document. Even if the predicted result is at rank one, this analysis might be of interest: If there is enough confidence on a user's click, we might redirect the user directly to the page whose link would be clicked. In this paper, we present three different models for predicting user clicks, ranging from most specific ones (using only past user history for the query) to very general ones (aggregating data over all users for a given query). The former model has a very high precision at low recall values, while the latter can achieve high recalls. We show that it is possible to combine the different models to predict with high accuracy (over 90%) a high subset of query sessions (24% of all the sessions).
Modeling historical and future movements of spatio-temporal objects in moving objects databases
Authors: Reasey Praing, Markus Schneider
Venue: CIKM
Year: 2007

Spatio-temporal databases deal with geometries changing over time. In general, geometries do not only change discretely but continuously; hence we are dealing with moving objects. In the past, a few moving object data models and query languages have been proposed. Each of them supports either historical movements or future movements but not both together. Consequently, queries that start in the past and extend into the future cannot be supported. To model both historical and future movements of an object, two separate concepts with different properties are required, and extra attention is necessary to avoid their conflicts. Furthermore, current definitions of moving objects are too general and vague. It is unclear how a moving object is allowed to move through space and time. For instance, the continuity or discontinuity of motion is not specified. In this paper, we propose a new moving object data model called Balloon model which provides integrated support for both historical and future movements of moving objects. As part of the model, we provide formal definitions of moving objects with respect to their past and future movements. All kinds of queries including past queries, future queries, and queries that start in the past and end in the future are supported in our model.
Semantic components enhance retrieval of domain-specific documents
Authors: Susan Price, Marianne Lykke Nielsen, Lois M. L. Delcambre, Peter Vedsted
Venue: CIKM
Year: 2007

We seek to leverage knowledge about information organization in a domain to effectively and efficiently meet targeted information needs of expert users. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. Semantic component instances are segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. This paper describes the semantic components model and presents experimental evidence from a large interactive searching study showing that semantic components, used to supplement full text and keyword indexing and to extend the query language, enhanced the retrieval of domain-specific documents in response to realistic queries posed by real users.
Web search: from information retrieval to microeconomic modeling
Authors: Prabhakar Raghavan
Venue: CIKM
Year: 2007

In scarcely a decade, web search has gone from simply scaling traditional information retrieval, to a groundswell of new opportunities that are changing marketing as we know it. In this lecture, we begin by reviewing the progress, pointing out that web search is no longer a purely computer sceince problem. We then hint at the role of other disciplines in this ongoing revolution and a number of directions for research.
Towards efficient search on unstructured data: an intelligent-storage approach
Authors: Aravindan Raghuveer, Meera Jindal, Mohamed F. Mokbel, Biplob K. Debnath, David Hung-Chang Du
Venue: CIKM
Year: 2007

Applications that create and consume unstructured data have grown both in scale of storage requirements and complexity of search primitives. We consider two such applications: exhaustive search and integration of structured and unstructured data. Current block-based storage systems are either incapable or inefficient to address the challenges bought forth by the above applications. We propose a storage framework to efficiently store and search unstructured and structured data while controlling storage management costs. Experimental results based on our prototype show that the proposed system can provide impressive performance and feature benefits.