OpenRaiser/Intern-Atlas huggingface dataset:
A SQL database system for solving constraints
Authors: Sebastien Siva, Lesi Wang
Venue: CIKM
Year: 2008

It has long been recognized that practical contexts for constraint satisfaction problems (CSP) often involve large relational databases (RDB). Early attempts to marry constraint solving systems and relational database systems include deductive and constraint databases that reuse important ideas from logic programming. These techniques required knowledge outside the scope of traditional database users. The recent proposal by Cadoli and Mancini, consql, shows that a simple extension to SQL provides a viable basis for modeling CSP. This opens the possibility for transparently integrating CSP with databases using SQL - the most widely known and popular database language. Such an extension brings the power of constraint problem solving to SQL knowledgeable users. Towards that end, the current research describes a case study in the engineering details of designing and implementing such a prototype. The SQL-based constraint data engine (SCDE) supports key concepts of consql, with several new syntax to better align the administration of CSP related notions with ordinary SQL. SCDE manages the internal representation and solving of constraints through a combination of ordinary SQL and a satisfiability solver, SATO. Initial performance tests on the 1997-98 ACC Basketball Scheduling problem shows the feasibility and potential of the SCDE design. Ongoing and future work include completing a programming environment for end-users to specify, test and debug constraint problem specification; combining the database engine with a more interactive version of SATO to support objective functions, and automatic problem analysis and decomposition for improving the performance of hard constraint problems.
Social capital in online communities
Authors: Matthew S. Smith
Venue: CIKM
Year: 2008

Online communities are connecting hordes of individuals and generating rich social network data. The social capital that resides within these networks is largely unknown. We propose to create a mathematical model of social capital that incorporates the mobilization of social resources through purposive actions. This includes evaluating nodes based not only on their relationships and attributes, but on their social resources as well. Investigating the costs associated with reciprocally connecting to individuals will also be assessed. The result is a quantitative model for characterizing and providing decision support on how to maximize participation within social networks.
Leveraging social context for searching social media
Authors: Marc A. Smith, Vladimir Barash, Lise Getoor, Hady Wirawan Lauw
Venue: CIKM
Year: 2008

The ability to utilize and benefit from today's explosion of social media sites depends on providing tools that allow users to productively participate. In order to participate, users must be able to find resources (both people and information) that they find valuable. Here, we argue that in order to do this effectively, we should make use of a user's "social context". A user's social context includes both their personal social context (their friends and the communities to which they belong) and their community social context (their role and identity in different communities).
A sparse gaussian processes classification framework for fast tag suggestions
Authors: Yang Song, Lu Zhang, C. Lee Giles
Venue: CIKM
Year: 2008

Tagged data is rapidly becoming more available on theWorld Wide Web. Web sites which populate tagging services offer a good way for Internet users to share their knowledge. An interesting problem is how to make tag suggestions when a new resource becomes available. In this paper, we ad-dress the issue of efficient tag suggestion. We first propose a multi-class sparse Gaussian process classification framework (SGPS) which is capable of classifying data with very few training instances. We suggest a novel prototype selection algorithm to select the best subset of points for model learn-ing. The framework is then extended to a novel multi-class multi-label classification algorithm (MMSG) that transforms tag suggestion into the problem of multi-label ranking. Ex-periments on bench-mark data sets and real-world data from Del.icio.us and BibSonomy suggest that our model can greatly improve the performance of tag suggestions when compared to the state-of-the-art. Overall, our model requires linear time to train and constant time to predict per case. The memory consumption is also significantly less than tradi-tional batch learning algorithms such as SVMs. In addition, results on tagging digital data also demonstrate that our model is capable of recommending relevant tags to images and videos by using their surrounding textual information.
Yizkor books: a voice for the silent past
Authors: Jason J. Soo, Rebecca Cathey, Ophir Frieder, Michlean J. Amir, Gideon Frieder
Venue: CIKM
Year: 2008

Yizkor Book collections contain firsthand commemorative accounts of events from the era surrounding the rise and fall of Nazi Germany, including documents from before, during, and after the Holocaust. Prior to our effort, information regarding the content and location of each Yizkor Book volume was limited. We established a centralized index and metadata repository for the Yizkor Book collection and developed a detailed search interface accessible worldwide.
Query selection for improved Greek web searches
Authors: Sofia Stamou, Lefteris Kozanidis, Paraskevi Tzekou, Nikos Zotos
Venue: CIKM
Year: 2008

As the Web becomes an integral part of our everyday life and the Internet-literate population grows rapidly, the Search Engine market is steadily gaining a high monetary value. Unfortunately, today, the distribution of the search market share is dominated by English-speaking users and stakeholders, basically because English is the lingua franca of the Web. Thus, although the majority of the Web users are non-English native speakers, they naturally gravitate to using English in order to explore the plentiful Web content. In this paper, we propose a query selection mechanism for assisting users perform successful non-English Web searches. Our mechanism combines linguistic analysis and Web mining techniques and aims at assisting users select informative and well-specified queries for expressing their information needs in languages other than English. Our technique is validated on a dataset of 70 Greek queries issued to Google search engine over a period of 3 weeks. Obtained results demonstrate that our query selection mechanism yields improved retrieval performance compared to existing non-English search strategies and as such we believe that it can be fruitfully deployed for other natural languages.
Wildcards for lightweight information integration in virtual desktops
Authors: Rodolfo Stecher, Claudia Niederée, Wolfgang Nejdl
Venue: CIKM
Year: 2008

We present a flexible information integration approach which addresses the dynamic integration needs in a personal desktop environment where only partial mappings are defined between the sources to be integrated. Our approach is based on query rewriting using substitution rules. In addition to exploiting defined mappings, we employ substitution strategies, which are inspired by the idea of using wildcards in querying and filtering tasks. Starting from a triple based query language as used for querying RDF data, unmapped ontological elements are substituted in a controlled way with variables, leading to a controlled form of query relaxation. In addition, the approach also provides evidences for refining the existing mapping based on the results of executing the relaxed queries. Different strategies for replacing non-matched ontology elements with variables are presented and evaluated over real-world data sets.
Acquiring advanced properties in ontology mapping
Authors: Suzette Kruger Stoutenburg
Venue: CIKM
Year: 2008

With the emergence of the Semantic Web, ontologies are being exposed on the Web to support a variety of applications, including enhanced search and discovery, rapid enterprise integration and cross-domain knowledge sharing. This explosion of knowledge sharing on the web is also leading to the formation of knowledge silos and a growing need for integration of these sources. Automated solutions to mapping ontologies are emerging that address this growing need with very promising results. However, most approaches have focused on mapping ontologies using relationships of similarity and equivalence and very few have applied knowledge in upper ontologies. We built algorithms to acquire relationships between ontological components beyond similarity and equivalence that include hyponymy and more importantly, algorithms to map ontologies based on relationships that are specified within the ontologies. These algorithms employ the semantics of OWL in conjunction with online linguistic resources and upper ontologies. Initial test results are promising.
An approximate string matching approach for handling incorrectly typed urls
Authors: Mihai Stroe, Radu Berinde, Cosmin Negruseri, Dan Popovici
Venue: CIKM
Year: 2008

In this paper we approach the problem of providing corrections for incorrectly typed URLs. This problem is significantly different from the classical spelling correction problem. We describe our contribution - building a custom data structure and a search algorithm that can find approximate matches for incorrect URLs. We evaluate the quality of our results through experiments with analysts. Our system is now being used in the Google search engine.
Purpose tagging: capturing user intent to assist goal-oriented social search
Authors: Markus Strohmaier
Venue: CIKM
Year: 2008

The terms that are used by users during tagging have been found to be different from the terms that are used when searching for resources, which represents a fundamental problem for search in tagging based systems. To address this problem, we propose purpose tagging as a novel kind of tagging that focuses on capturing aspects of intent rather than content. By capturing the different purposes a given resource can serve, purpose tags appear useful to mediate between the vocabulary of user intent on one hand, and the vocabulary of contents and tags provided by social software applications on the other. The paper at hand makes the following contributions: 1) It extends the set of known kinds of tags with a novel type and 2) it provides first empirical evidence of the principle feasibility of purpose tagging and its potential to facilitate goal-oriented social search in an exploratory case study.
Socialtagger - collaborative tagging for blogs in the long tail
Authors: Shankara B. Subramanya, Huan Liu
Venue: CIKM
Year: 2008

Social bookmarking is the process through which users share tags for online resources like blogs with others. Such collaborative tags provide valuable metadata for retrieval systems. While the successes of collaborative tagging systems have been demonstrated by popular websites like Del.icio.us, these sites cover only a small fraction of the available blogs on the web. The vast majority of the blogs are not available on any collaborative tagging system and are often tagged only by the authors. This lack of coverage of collaborative tags is a considerable roadblock in using the tag metadata in a web scale information retrieval system. To solve this problem we propose and implement a system to automatically recommend collaborative tags for a blog. The automatically generated tags will help to surface the blogs by making them available on social book marking sites and allow them to be easily discovered and potentially further tagged by a wider population.
Search engine architectures from conventional to Brian Ulicny and Ken Baclawski. New metrics for newsblog
Authors: Torsten Suel
Venue: CIKM
Year: 2008

A significant amount of research has focused on the problem of implementing large-scale search engines in peer-to-peer environments. However, gaps in both performance and perspective remain between conventional approaches and those explored in the peer-to-peer community. In this talk, we give a brief overview of search engine architectures and performance issues from the perspective of a researcher focusing on conventional engines. We then discuss the main differences between the two approaches, suggest areas for fruitful interaction, and list open problems.
Measuring user preference changes in digital libraries
Authors: Yang Sun, Huajing Li, Isaac G. Councill, Wang-Chien Lee, C. Lee Giles
Venue: CIKM
Year: 2008

Much research has been conducted using web access logs to study implicit user feedback and infer user preferences from clickstreams. However, little research measures the changes of user preferences of ranking documents over time. We present a study that measures the changes of user preferences based on an analysis of access logs of a large scale digital library over one year. A metric based on the accuracy of predicting future user actions is proposed. The results show that although user preferences change over time, the majority of user actions should be predictable from previous browsing behavior in the digital library.
A data integration method for exploring gene regulatory mechanisms
Authors: Jane Synnergren, Björn Olsson, Jonas Gamalielsson
Venue: CIKM
Year: 2008

Systems biology aims to understand the behavior of and interaction between various components of the living cell, such as genes, proteins, and metabolites. A large number of components are involved in these complex systems and the diversity of relationships between the components can be overwhelming, and there is therefore a need for analysis methods incorporating data integration. We here present a method for exploring gene regulatory mechanisms which integrates various types of data to assist the identification of important components in gene regulation mechanisms. By first analyzing gene expression data, a set of differentially expressed genes is selected. These genes are then further investigated by combining various types of biological information, such as clustering results, promoter sequences, binding sites, transcription factors and other previously published information regarding the selected genes. Inspired by Information Fusion research, we also mapped functions of the proposed method to the well-known OODA-model to facilitate application of this data integration method in other research communities. We have successfully applied the method to genes identified as differentially expressed in human embryonic stem cells at different stages of differentiation towards cardiac cells. We identified 15 novel motifs that may represent important binding sites in the cardiac cell linage.
Knowledge-based gene symbol disambiguation
Authors: He Tan
Venue: CIKM
Year: 2008

Since there is no standard naming convention for genes and gene products, gene symbol disambiguation (GSD) has become a big challenge when mining biomedical literature. Several GSD methods have been proposed based on MEDLINE references to genes. However, nowadays gene databases, e.g. Entrez Gene, provide plenty of information about genes, and many biomedical ontologies, e.g. UMLS Metathesaurus and Semantic Network, have been developed. These knowledge sources could be used for disambiguation, in this paper we propose a method which relies on information about gene candidates from gene databases, contexts of gene symbols and biomedical ontologies. We implement our method, and evaluate the performance of the implementation using BioCreAtIvE II data sets.
Metadata extraction and indexing for map search in web documents
Authors: Qingzhao Tan, Prasenjit Mitra, C. Lee Giles
Venue: CIKM
Year: 2008

In academic scientific articles, maps are widely used to provide the related geographic information and to give readers a visual understanding of the document content. As more digital documents containing maps become accessible on the Web, there is a growing demand for a Web search system to provide users with tools to retrieve documents based on the information available within a document's maps. In this paper, we design methods and algorithms to extract, identify, and index maps from academic and scientific documents in digital libraries. Experimental results show that our approach can accurately locate maps and significantly improve the retrieve quality for maps in digital documents.
Efficient frequent pattern mining over data streams
Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, Young-Koo Lee
Venue: CIKM
Year: 2008

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.
Rewriting of visibly pushdown languages for xml data integration
Authors: Alex Thomo, Srinivasan Venkatesh
Venue: CIKM
Year: 2008

In this paper, we focus on XML data integration by studying rewritings of XML target schemas in terms of source schemas. Rewriting is very important in data integration systems where the system is asked to find and assemble XML documents from the data sources and produce documents which satisfy a target schema.As schema representation, we consider Visibly Pushdown Automata (VPAs) which accept Visibly Pushdown Languages (VPLs). The latter have been shown to coincide with the family of (word-encoded) regular tree languages which are the basis of formalisms for specifying XML schemas. Furthermore, practical semi-formal XML schema specifications (defined by simple pattern conditions on XML) compile into VPAs which are exponentially more concise than other representations based on tree automata.Notably, VPLs enjoy a well-behavedness which facilitates us in addressing rewriting problems for XML data integration. Based on VPAs, we positively solve these problems, and present detailed complexity analyses.
Incorporating place name extents into geo-ir ranking
Authors: Hiroyuki Toda, Norihito Yasuda, Yumiko Matsuura, Ryoji Kataoka
Venue: CIKM
Year: 2008

This paper proposes a novel Geo-IR ranking method that realizes effective searches that emphasize the user's immediate surroundings. It assesses the extent implied by place names in documents and then emphasizes place names that are highly specific in terms of identifying locations.
Challenges in searching social media
Authors: Andrew Tomkins
Venue: CIKM
Year: 2008

I'll start with a discussion of the social media landscape, then will give some recent results regarding new trends in the use of large data in understanding social networks. Finally, I'll present a series of challenges in social media search from the standpoint of a large search engine.
A novel email abstraction scheme for spam detection
Authors: Chi-Yao Tseng, Pin-Chieh Sung, Ming-Syan Chen
Venue: CIKM
Year: 2008

Prior studies on collaborative spam filtering with near-duplicate similarity matching scheme mainly represent each email by a succinct abstraction derived from email content text. Since these abstractions of emails cannot fully catch the evolving nature of spams, we propose in this paper a novel email abstraction scheme, which considers email layout structure to represent emails. With the proposed abstraction, we design a near-duplicate matching scheme to efficiently match each incoming email with a huge spam database.
Passage relevance models for genomics search
Authors: Jay Urbain, Ophir Frieder, Nazli Goharian
Venue: CIKM
Year: 2008

We present a passage relevance model for integrating semantic and statistical evidence of biomedical concepts and topics in context using the framework of a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field, and the probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.
Passage relevance models for genomics search
Authors: Jay Urbain, Ophir Frieder, Nazli Goharian
Venue: CIKM
Year: 2008

We present a passage relevance model for integrating semantic and statistical evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.
Characterization of TPC-H queries for a column-oriented database on a dual-core amd athlon processor
Authors: Pranav Vaidya, Jaehwan John Lee
Venue: CIKM
Year: 2008

Column-oriented databases are reported to perform orders of mag-nitude faster than traditional row-oriented databases for Decision Support System (DSS) workloads. However, there have been few investigations on the detailed hardware characterization of DSS benchmarks namely TPC-H for column-oriented databases. In this paper, we characterize the performance of the TPC-H benchmark for a popular column-oriented database called MonetDB running on a dual-core AMD Athlon machine. Specifically, we measure the performance of key microarchitectural components and provide very detailed analysis of the execution time breakdown of Mon-etDB during the execution of TPC-H queries. We analyze in detail the nature of the various stalls namely cache stalls, branch mis-prediction stalls and resource stalls in order to identify the causes of these performance impediments in column-oriented databases. We then indicate possible research directions to minimize these stalls in column-oriented databases. Finally, we compare our re-sults with published results on the characterization of TPC-H for row-oriented databases. As opposed to the previous approaches that used system-level monitoring, we used thread-level monitor-ing of database threads to study the performance of the database alone in isolation from the rest of the system. This enabled us to characterize the performance of databases independent of any other executing processes. 1.