OpenRaiser/Intern-Atlas huggingface dataset:
Finding informative commonalities in concept collections
Authors: Simona Colucci, Eugenio Di Sciascio, Francesco M. Donini, Eufemia Tinelli
Venue: CIKM
Year: 2008

The problem of finding commonalities characterizes several Knowledge Management scenarios involving collection of resources. The automatic extraction of shared features in a collection of resource descriptions formalized in accordance with a logic language has been in fact widely investigated in the past. In particular, with reference to Description Logics concept descriptions, Least Common Subsumers have been specifically introduced.
Unsolved problems in search: (and how we approach them)
Authors: W. Bruce Croft
Venue: CIKM
Year: 2008

Search applications have become ubiquitous and very successful. Major advances have been made in understanding how to deliver effective results very efficiently for a class of queries. As the range of applications broaden to include web search, desktop search, enterprise search, vertical search, social search, etc., the number of new research challenges has appeared to grow rather than shrink. Many of these challenges are variations on underlying themes and principles that information retrieval has focused on for more than 40 years. In this talk, several unsolved problems arising from new search applications will be discussed and some potential paths to solutions for these problems will be outlined.
Data integration for the semantic web with full preferences
Authors: Olivier Curé
Venue: CIKM
Year: 2008

This paper presents a tool that enables the integration of data stored in relational databases into Semantic Web compliant knowledge bases. The resulting knowledge bases are represented using Description Logics and can thus be translated into the Web Ontology language (OWL). Our approach tackles the impedance mismatch problem which is due to the storage of data in (relational) databases and objects in the knowledge bases. This problem is addressed with a mapping language that allows to specify how to transform data into objects. Another issue undertaken by our solution is related to inconsistencies emerging when contradicting values can be integrated into a given object. In order to deal with these inconsistencies, we enable users to set preferences over mapping views and their attributes. This approach provides a fine grained solution to design consistent knowledge bases.
An FCA-based solution for ontology mediation
Authors: Olivier Curé, Robert Jeansoulin
Venue: CIKM
Year: 2008

In this paper, we present an ontology mediation solution based on the methods frequently used in Formal Concept Analysis. Our approach of mediation means that, if we can rely on the existence of instances associated to two source ontologies, then we can generate concepts in a new ontology if and only if they share the same extent. Hence our approach creates a merged ontology which captures the knowledge of the two source ontologies. The main contributions of this work are (i) to enable the creation of concepts not originally in the source ontologies, (ii) to propose a solution to label these new emerging concepts and finally (iii) to optimize the resulting ontology by eliminating redundant or non pertinent concepts. Another contribution of this work is to emphasize that several forms of mediated ontology can be defined based on the relaxation of certain criteria produced from our method. The solution that we propose for tackling these issues is an automatic solution, meaning that it does not require the intervention of the end-user, excepting for the definition of the common set of ontology instances.
Investigating external corpus and clickthrough statistics for query expansion in the legal domain
Authors: Tonya Custis, Khalid Al-Kofahi
Venue: CIKM
Year: 2008

We apply language modeling keyword search augmented with Berger and Lafferty's (1999) translation model for query expansion to formulate three query expansion methods using word co-occurrence statistics from a large external corpus and user clickthrough data. We study the performance of these methods on a vertical domain (case law documents) using standard metrics and an evaluation framework designed specifically to measure the performance of query expansion under varying degrees of query-document term mismatch.
Answering general time sensitive queries
Authors: Wisam Dakka, Luis Gravano, Panagiotis G. Ipeirotis
Venue: CIKM
Year: 2008

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.
Using a graph-based ontological user profile for personalizing search
Authors: Mariam Daoud, Lynda Tamine-Lechani, Mohand Boughanem
Venue: CIKM
Year: 2008

In this poster, we describe a personalized search approach, which involves a graph based user profile issued from ontology and a session boundary recognition mechanism. The user profile refers to the short term user interest and is used for re-ranking the search results of queries in the same search session. The session boundary recognition is based on tracking changes in the dominant concepts held by the query and the user profile. Experimental evaluation was carried out using the HARD 2003 TREC collection and shows that our approach is effective.
Efficient sampling of information in social networks
Authors: Gautam Das, Nick Koudas, Manos Papagelis, Sushruth Puttaswamy
Venue: CIKM
Year: 2008

As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a user's social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.
Dynamic faceted search for discovery-driven analysis
Authors: Debabrata Dash, Jun Rao, Nimrod Megiddo, Anastasia Ailamaki, Guy M. Lohman
Venue: CIKM
Year: 2008

We propose a dynamic faceted search system for discovery-driven analysis on data with both textual content and structured attributes. From a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. Similar to work in OLAP exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. We make two new contributions by proposing a novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. Through a user survey, we find the new expectation and interestingness metric quite effective. We develop an efficient dynamic faceted search system by improving a popular open source engine, Solr. Our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree for fast bitset intersection. We conduct a comprehensive experimental study on large real data sets and show that our engine performs 2 to 3 times faster than Solr.
Predicting individual disease risk based on medical history
Authors: Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, Albert-László Barabási
Venue: CIKM
Year: 2008

The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on a patient's medical history using ICD-9-CM codes in order to predict future diseases risks. CARE uses collaborative filtering to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.
Cache-aware load balancing for question answering
Authors: David Dominguez-Sal, Mihai Surdeanu, Josep Aguilar-Saborit, Josep Lluís Larriba-Pey
Venue: CIKM
Year: 2008

The need for high performance and throughput Question Answering (QA) systems demands for their migration to distributed environments. However, even in such cases it is necessary to provide the distributed system with cooperative caches and load balancing facilities in order to achieve the desired goals. Until now, the literature on QA has not considered such a complex system as a whole. Currently, the load balancer regulates the assignment of tasks based only on the CPU and I/O loads without considering the status of the system cache.
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Authors: Pinar Donmez, Jaime G. Carbonell
Venue: CIKM
Year: 2008

Proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. Active learning seeks to select the most informative unlabeled instances and ask an omniscient oracle for their labels, so as to retrain the learning algorithm maximizing accuracy. However, the oracle is assumed to be infallible (never wrong), indefatigable (always answers), individual (only one oracle), and insensitive to costs (always free or always charges the same). Proactive learning relaxes all four of these assumptions, relying on a decision-theoretic approach to jointly select the optimal oracle and instance, by casting the problem as a utility optimization problem subject to a budget constraint. Results on multi-oracle optimization over several data sets demonstrate the superiority of our approach over the single-imperfect-oracle baselines in most cases.
Understanding the relationship between searchers' queries and information goals
Authors: Doug Downey, Susan T. Dumais, Daniel J. Liebling, Eric Horvitz
Venue: CIKM
Year: 2008

We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. We note the diversity of information goals that searchers have and the differing ways that goals are specified. We examine rare and common information goals that are specified using rare or common queries. We identify several significant differences in user behavior depending on the rarity of the query and the destination URL. We find that searchers are more likely to be successful when the frequencies of the query and destination URL are similar. We also establish that the behavioral differences observed for queries and goals of varying rarity persist even after accounting for potential confounding variables, including query length, search engine ranking, session duration, and task difficulty. Finally, using an information-theoretic measure of search difficulty, we show that the benefits obtained by search and navigation actions depend on the frequency of the information goal.
Overlapping community structure detection in networks
Authors: Nan Du, Bai Wang, Bin Wu
Venue: CIKM
Year: 2008

Many systems in nature and human society take the form of networks with community structures. In this paper, we describe a simple algorithm COCD(Clique-based Overlapping Community Detection) to efficiently mine the overlapping communities in large-scale networks, which is useful for us to have a better understanding of the nested sub-structures embedded in the whole network.
A consensus based approach to constrained clustering of software requirements
Authors: Chuan Duan, Jane Cleland-Huang, Bamshad Mobasher
Venue: CIKM
Year: 2008

Managing large-scale software projects involves a number of activities such as viewpoint extraction, feature detection, and requirements management, all of which require a human analyst to perform the arduous task of organizing requirements into meaningful topics and themes. Automating these tasks through the use of data mining techniques such as clustering could potentially increase both the efficiency of performing the tasks and the reliability of the results. Unfortunately, the unique characteristics of this domain, such as high dimensional, sparse, noisy data sets, resulting from short and ambiguous expressions of need, as well as the need for the interactive engagement of stakeholders at various stages of the process, present difficult challenges for standard clustering algorithms. In this paper, we propose a semi-supervised clustering framework, based on a combination of consensus-based and constrained clustering techniques, which can effectively handle these challenges. Specifically, we provide a probabilistic analysis for informative constraint generation based on a co-association matrix, and utilize consensus clustering to combine multiple constrained partitions in order to generate high-quality, robust clusters. Our approach is validated through a series of experiments on six well-studied TREC data sets and on two sets of user requirements.
How do Greeks search the web?: a query log analysis study
Authors: Efthimis N. Efthimiadis
Venue: CIKM
Year: 2008

This study investigates how Greeks search the web through the analysis of search logs from the AltaVista search engine. The analysis looks at the data as a whole and also examines the use of the Greek language in web searching. Statistics include frequencies of queries, term distributions, languages used including mixed language queries, search features used in query formulation and query length patterns.
Supporting sub-document updates and queries in an inverted index
Authors: Vuk Ercegovac, Vanja Josifovski, Ning Li, Maurício R. Mediano, Eugene J. Shekita
Venue: CIKM
Year: 2008

Inverted indexes have become the standard indexing method for supporting search queries in a variety of content-based applications. Examples of such applications include enterprise document management, e-mail, web search, and social networks. One shortcoming in current inverted index designs is that they support only document-level updates, forcing a full document to be reindexed even if just part of it changes. This paper describes a new inverted index design that enables applications to break a document into semantically meaningful sub-documents or "sections". Each section of a document can be updated separately, but search queries can still work seamlessly across sections. Our index design is motivated by applications where there is metadata associated with each document that tends to be smaller and more frequently updated than the document's content, but at the same time, it is desireable to search the metadata and content with the same index structure. A novel self-optimizing query execution algorithm is described to efficiently join the sections of a document in the inverted index. Experimental results on TREC and patent data are provided, showing that sections can dramatically improve overall system throughput on a mixed workload of updates and queries.
E-discovery
Authors: David A. Evans, Jason R. Baron, Chris Buckley, Robert S. Bauer
Venue: CIKM
Year: 2008

It is common practice in the U.S. for courts to require that the parties to a legal case make available to one another all material relevant to the case, including electronically held data and documents. For large corporations, such relevant information may encompass terabytes of e-mail and other files spanning many years. The challenge of E-Discovery in response to a court order is (in a relatively short amount of time) to identify, assemble, individuate, access, categorize, and analyze an organization's electronically held material, segregate all "privileged" material (which can be legally withheld), and present to the court all (and only) the required documents. The techniques needed to accomplish such a task necessarily include search, clustering, classification, filtering, social network analysis, extraction, and more - and no one of these is sufficient. The panel will describe this problem in detail, providing additional background and context on E-Discovery generally, and will explore specific techniques and cases that amply demonstrate why E-Discovery is quintessentially a "CIKM" problem -- multi-disciplinary, multi-technology, and "multi-difficult".
Towards a model of understanding social search
Authors: Brynn M. Evans, Ed H. Chi
Venue: CIKM
Year: 2008

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main
The social (open) workspace
Authors: David A. Evans, Susan Feldman, Ed H. Chi, Natasa Milic-Frayling, Igor Perisic
Venue: CIKM
Year: 2008

Social networking promises individuals new dimensions of freedom to interact, associate, and give expression to their talents. Recently, systems such as Mechanical Turk have started to facilitate self-organizing collaboration on work-related tasks. Such developments raise interesting questions. Is it possible to create (and sustain) businesses that do not have traditional, formal structure - without traditional "employees"? Can we find and organize (and optimize) talent on the web for task-oriented work - spontaneously and efficiently? How do people relate to one another in possibly evanescent workgroups? One aspect of the challenge in the Social Workspace is understanding and modeling the user behavior and the economic basis for creating, preserving, and exchanging value in the marketplace when workgroup identity, orientations to property, recruiting and managing appropriate talent are not organized under traditional company structures. Another aspect is the technology needed to support virtual organizations and work. The panel will discuss trends in social work and the evolving (scientific) basis of our understanding of new models of workers and organizations.
Peptide programs: applying fragment programs to protein classification
Authors: André O. Falcão, Daniel Faria, António E. N. Ferreira
Venue: CIKM
Year: 2008

Functional prediction/classification of proteins is a central problem in bioinformatics. Alignment methods are a useful approach, but have limitations, which have prompted the development and use of machine learning approaches. However, traditional machine learning approaches are unable to exploit sequence data directly, and instead use derived sequence features or Kernel functions to obtain a feature space. Because theoretically all information necessary to predict a protein's structure and function is contained in its sequence, a methodology that could exploit sequence data directly could be advantageous. A novel machine learning methodology for protein classification, inspired in the concept of fragment programs, is presented. This methodology consists in assigning a minimal computer program to each of the 20 amino acids, and then representing a protein as the program resulting from applying sequentially the programs of the amino acids which compose its sequence. The basic concepts of the methodology presented (peptide programs) are discussed and a framework is proposed for their implementation, including instruction set, virtual machine, evaluation procedures and convergence methods. The methodology is tested in the binary classification of 33,500 enzymes into 182 distinct Enzyme Commission (EC) classes. The average Matthews correlation coefficient of the binary classifiers is 0.75 in training and 0.68 in validation. Overall, the results obtained demonstrate the potential of the proposed methodology, and its ability to extract knowledge from sequence data, using very few computational resources
A metric cache for similarity search
Authors: Fabrizio Falchi, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, Fausto Rabitti
Venue: CIKM
Year: 2008

Similarity search in metric spaces is a general paradigm that can be used in several application fields. It can also be effectively exploited in content-based image retrieval systems, which are shifting their target towards the Web-scale dimension. In this context, an important issue becomes the design of scalable solutions, which combine parallel and distributed architectures with caching at several levels. To this end, we investigate the design of a similarity cache that works in metric spaces. It is able to answer with exact and approximate results: even when an exact match is not present in cache, our cache may return an approximate result set with quality guarantees. By conducting tests on a collection of one million high-quality digital photos, we show that the proposed caching techniques can have a significant impact on performance, like caching on text queries has been proved effective for traditional Web search engines.
GHOST: an effective graph-based framework for name distinction
Authors: Xiaoming Fan, Jianyong Wang, Bing Lv, Lizhu Zhou, Wei Hu
Venue: CIKM
Year: 2008

Name ambiguity stems from the fact that many people or objects share identical names. In this paper, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective graph-based framework, GHOST (abbr. GrapH-based framewOrk for name diStincTion), to solve the problem systematically. We evaluated the framework on the real DBLP dataset, and the experimental results show that GHOST outperforms the state-of-the-art method.