OpenRaiser/Intern-Atlas huggingface dataset:
Can all tags be used for search?
Authors: Kerstin Bischoff, Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu
Venue: CIKM
Year: 2008

Collaborative tagging has become an increasingly popular means for sharing and organizing Web resources, leading to a huge amount of user generated metadata. These tags represent quite a few different aspects of the resources they describe and it is not obvious whether and how these tags or subsets of them can be used for search. This paper is the first to present an in-depth study of tagging behavior for very different kinds of resources and systems - Web pages (Del.icio.us), music (Last.fm), and images (Flickr) - and compares the results with anchor text characteristics. We analyze and classify sample tags from these systems, to get an insight into what kinds of tags are used for different resources, and provide statistics on tag distributions in all three tagging environments. Since even relevant tags may not add new information to the search procedure, we also check overlap of tags with content, with metadata assigned by experts and from other sources. We discuss the potential of different kinds of tags for improving search, comparing them with user queries posted to search engines as well as through a user survey. The results are promising and provide more insight into both the use of different kinds of tags for improving search and possible extensions of tagging systems to support the creation of potentially search-relevant tags.
A framework for estimating complex probability density structures in data streams
Authors: Arnold P. Boedihardjo, Chang-Tien Lu, Feng Chen
Venue: CIKM
Year: 2008

Probability density function estimation is a fundamental component in several stream mining tasks such as outlier detection and classification. The nonparametric adaptive kernel density estimate (AKDE) provides a robust and asymptotically consistent estimate for an arbitrary distribution. However, its extensive computational requirements make it difficult to apply this technique to the stream environment. This paper tackles the issue of developing efficient and asymptotically consistent AKDE over data streams while heeding the stringent constraints imposed by the stream environment. We propose the concept of local regions to effectively synopsize local density features, design a suite of algorithms to maintain the AKDE under a time-based sliding window, and analyze the estimates' asymptotic consistency and computational costs. In addition, extensive experiments were conducted with real-world and synthetic data sets to demonstrate the effectiveness and efficiency of our approach.
The query-flow graph: model and applications
Authors: Paolo Boldi, Francesco Bonchi, Carlos Castillo, Debora Donato, Aristides Gionis, Sebastiano Vigna
Venue: CIKM
Year: 2008

Query logs record the queries and the actions of the users of engines, and as such they contain valuable information about the interests, the preferences, and the behavior of the users, as well as their implicit feedback to engine results. Mining the wealth of information available in the query logs has many important applications including query-log analysis, user profiling and personalization, advertising, query recommendation, and more.In this paper we introduce the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior. Intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same search mission. Any path over the query-flow graph may be seen as a searching behavior, whose likelihood is given by the strength of the edges along the path.The query-flow graph is an outcome of query-log mining and, at the same time, a useful tool for it. We propose a methodology that builds such a graph by mining time and textual information as well as aggregating queries from different users. Using this approach we build a real-world query-flow graph from a large-scale query log and we demonstrate its utility in concrete applications, namely, finding logical sessions, and query recommendation. We believe, however, that the usefulness of the query-flow graph goes beyond these two applications.
A language for manipulating clustered web documents results
Authors: Gloria Bordogna, Alessandro Campi, Giuseppe Psaila, Stefania Ronchi
Venue: CIKM
Year: 2008

We propose a novel conception language for exploring the results retrieved by several internet search services (like search engines) that cluster retrieved documents. The goal is to offer users a tool to discover relevant hidden relationships between clustered documents.
Clustered subset selection and its applications on it service metrics
Authors: Christos Boutsidis, Jimeng Sun, Nikos Anerousis
Venue: CIKM
Year: 2008

Motivated by the enormous amounts of data collected in a large IT service provider organization, this paper presents a method for quickly and automatically summarizing and extracting meaningful insights from the data. Termed Clustered Subset Selection (CSS), our method enables program-guided data explorations of high-dimensional data matrices. CSS combines clustering and subset selection into a coherent and intuitive method for data analysis. In addition to a general framework, we introduce a family of CSS algorithms with different clustering components such as k-means and Close-to-Rank-One (CRO) clustering, and Subset Selection components such as best rank-one approximation and Rank-Revealing QR (RRQR) decomposition.
An empirical study of required dimensionality for large-scale latent semantic indexing applications
Authors: Roger B. Bradford
Venue: CIKM
Year: 2008

The technique of latent semantic indexing is used in a wide variety of commercial applications. In these applications, the processing time and RAM required for SVD computation, and the processing time and RAM required during LSI retrieval operations are all roughly linear in the number of dimensions, k, chosen for the LSI representation space. In large-scale commercial LSI applications, reducing k values could be of significant value in reducing server costs. This paper explores the effects of varying dimensionality.
Mapping enterprise entities to text segments
Authors: Falk Brauer, Alexander Löser, Hong Hai Do
Venue: CIKM
Year: 2008

Today, valuable business information is increasingly stored as unstructured data (documents, emails, etc.). For example, documents exchanged between business partners capture information on transactions between them like purchases or invoices. A major challenge is to correctly recognize and associate real-world entities in unstructured data, e.g. documents, with those stored in structured data e.g., enterprise databases. To address this, we propose in this paper a robust process methodology consisting of three phases: entity extraction from documents, generation of mapping of recognized entities with structured data, and disambiguation of mappings exploiting relationships from the enterprise data and the documents' structure.
Mirroring your web presence
Authors: Markus Bylund, Jussi Karlgren, Fredrik Olsson, Pedro Sanches, Carl-Henrik Arvidsson
Venue: CIKM
Year: 2008

This paper describes the starting points of how to design and build tools to help individual users track and monitor their presence on the web from the standpoints of individual privacy and identity monitoring. It concludes with an overview technological barriers and possible solutions for their resolution. Our design models represent facets of identity by tracking their mentions in text. It is intended to provide a basis for discussion on how to redress the information imbalance users are subjected to today, due to lack of overview of their own online traces.
Semi-automated logging of contact center telephone calls
Authors: Roy J. Byrd, Mary S. Neff, Wilfried Teiken, Youngja Park, Keh-Shin F. Cheng, Stephen C. Gates, Karthik Visweswariah
Venue: CIKM
Year: 2008

Modern businesses use contact centers as a communication channel with users of their products and services. The largest factor in the expense of running a telephone contact center is the labor cost of its agents. IBM Research has built a new system, Contact-Center Agent Buddies (CAB), which is designed to help reduce the average handle time (AHT) for customer calls, thereby also reducing their cost. In this paper, we focus on the call logging subsystem, which helps agents reduce the time they spend documenting those calls. We built a Template CAB and a Call Logging CAB, using a pipeline consisting of audio capture of a telephone conversation, automatic speech recognition, text analysis, and log generation. We developed techniques for ASR text cleansing, including normalization of expressions and acronyms, domain terms, capitalization, and boundaries for sentences, paragraphs, and call segments. We found that simple heuristics suffice to generate high-quality logs from the normalized sentences. The pipeline yields a candidate call log which the agents can edit in less time than it takes them to generate call logs manually. Evaluation of the Call Logging CAB in an industrial contact center environment shows that it reduces the amount of time agents spend logging calls by at least 50% without compromising the quality of the resulting call documentation.
Aggregate queries over ontologies
Authors: Diego Calvanese, Evgeny Kharlamov, Werner Nutt, Camilo Thorne
Venue: CIKM
Year: 2008

Answering queries over ontologies is an important issue for the Semantic Web. Aggregate queries were widely studied for relational databases but almost no results are known for aggregate queries over ontologies. In this work we investigate the latter problem. We propose syntax and semantics for epistemic aggregate queries over ontologies and study query answering for MAX, MIN, COUNT, CNTD, SUM, AVG queries for the ontology language DL-LiteA.
Table summarization with the help of domain lattices
Authors: K. Selçuk Candan, Huiping Cao, Yan Qi, Maria Luisa Sapino
Venue: CIKM
Year: 2008

Table summarization is necessary in various scenarios where it is hard to display a large table. It can benefit from knowledge about acceptable value clustering alternatives. In this paper, we formulate the problem of table summarization with the help of domain knowledge lattices. We provide the outline of a fuzzy mechanism to express alternative clustering strategies. We further sketch a novel ranked set cover based evaluation mechanism (RSC) to tackle with the inherent complexity.
Handling implicit geographic evidence for geographic ir
Authors: Nuno Cardoso, Mário J. Silva, Diana Santos
Venue: CIKM
Year: 2008

Most geographic information retrieval systems depend on the detection and disambiguation of place names in documents, assuming that the documents with a specific geographic scope contain explicit place names in the text that are strongly related to the document scopes. However, some non-geographic names such as companies, monuments or sport events, may also provide indirect relevant evidence that can significantly contribute to the assignment of geographic scopes to documents. In this paper, we analyze the amount of implicit and explicit geographic evidence in newspaper documents, and measure its impact on geographic information retrieval by evaluating the performance of a retrieval system using the GeoCLEF evaluation data.
Tag data and personalized information retrieval
Authors: Mark James Carman, Mark Baillie, Fabio Crestani
Venue: CIKM
Year: 2008

Researchers investigating personalization techniques for Web Information Retrieval face a challenge; that the data required to perform evaluations, namely query logs and click-through data, is not readily available due to valid privacy concerns. One option for researchers is to perform a user study, however, such experiments are often limited to small (and sometimes biased) samples of users, restricting somewhat the conclusions that can be drawn. Alternatively, researchers can look for publicly available data that can be used to approximate query logs and click-through data. Recently it has been shown that the information contained in social bookmarking (tagging) systems may be useful for improving Web search.
In the development of a spanish metamap
Authors: Francisco M. Carrero, José Carlos Cortizo, José María Gómez, Manuel de Buenaga Rodríguez
Venue: CIKM
Year: 2008

MetaMap is an online application that allows mapping text to UMLS Metathesaurus concepts, which is very useful interoperability among different languages and systems within the biomedical domain. MetaMap Transfer (MMTx) is a Java program that makes MetaMap available to biomedical researchers. Currently there is no Spanish version of MetaMap, which difficults the use of UMLS Metathesaurus to extract concepts from Spanish biomedical texts.
Coreference resolution using expressive logic models
Authors: Ki Chan, Wai Lam, Xiaofeng Yu
Venue: CIKM
Year: 2008

Coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. Traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. However, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. In this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the Markov Logic Network. The proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. Moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. Our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.
Query optimization in xml-based information integration
Authors: Dongfeng Chen, Rada Chirkova, Maxim Kormilitsin, Fereidoon Sadri, Timo J. Salo
Venue: CIKM
Year: 2008

The problem of decentralized data sharing is relevant for a wide range of applications and is still a source of major theoretical and practical challenges, in spite of many years of sustained research in information integration. We focus on the challenge of efficiency of query evaluation in information-integration systems, with the objective of developing query-processing strategies that are widely applicable and easy to implement in real-life applications. In our algorithms we take into account important features of today's data-sharing applications, namely: XML as likely interface to or representation for data sources; potential for information overlap across data sources; and the need for inter-source processing (i.e., joins of data across data sources) in many applications.
On effective presentation of graph patterns: a structural representative approach
Authors: Chen Chen, Cindy Xide Lin, Xifeng Yan, Jiawei Han
Venue: CIKM
Year: 2008

In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. Currently, what the state-of-art techniques give is a lengthy list of exact patterns, which are undesirable in the following two aspects: (1) on the micro side, due to various inherent noises or data diversity, exact patterns are usually not too useful in many real applications; and (2) on the macro side, the rigid structural requirement being posed often generates an excessive amount of patterns that are only slightly different from each other, which easily overwhelm the users.
A matrix-based approach for semi-supervised document co-clustering
Authors: Yanhua Chen, Lijun Wang, Ming Dong
Venue: CIKM
Year: 2008

In order to derive high quality information from text, the field of text mining has advanced swiftly from simple document clustering to co-clustering documents and words. However, document co-clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) based framework for document co-clustering. Our method computes a new word-document matrix by incorporating user provided constraints through distance metric learning. Using an iterative algorithm, we perform tri-factorization of the new matrix to infer the document and word clusters. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for document co-clustering.
Propagation-vectors for trees (PVT): concise yet effective summaries for hierarchical data and trees
Authors: Venkata Snehith Cherukuri, K. Selçuk Candan
Venue: CIKM
Year: 2008

Summarization of hierarchical data and metadata is a fundamental operation in applications in many domains. In particular, similarity search of hierarchical data, such as XML, would benefit greatly from concise and indexable summaries. This is especially true in P2P scenarios, where the search needs to be done in a distributed fashion on multiple peers. This situation requires summaries which are small, yet effective in identifying potential peers that need to be further explored. In this paper, we propose a method, called propagation-vectors for trees (PVT) which constructs very concise and accurate summaries of hierarchical data, such as XML trees. We then show how to use this summary to perform similarity search on summarized data. The proposed summarization scheme relies on a label-propagation mechanism, which constructs an n-dimensional vector from a given tree with n unique data labels. Experimental results have shown that the constructed PVT summaries capture the structure of the input trees very accurately, the representations are highly concise, and that the search based on these summaries are faster than the existing approaches.
Probabilistic polyadic factorization and its application to personalized recommendation
Authors: Yun Chi, Shenghuo Zhu, Yihong Gong, Yi Zhang
Venue: CIKM
Year: 2008

Multiple-dimensional, i.e., polyadic, data exist in many applications, such as personalized recommendation and multiple-dimensional data summarization. Analyzing all the dimensions of polyadic data in a principled way is a challenging research problem. Most existing methods separately analyze the marginal relationships among pairwise dimensions and then combine the results afterwards. Motivated by the fact that various dimensions of polyadic data jointly affect each other, we propose a probabilistic polyadic factorization approach to directly model all the dimensions simultaneously in a unified framework. We then show the connection between the probabilistic polyadic factorization and a non-negative version of the Tucker tensor factorization. We provide detailed theoretical analysis of the new modeling framework, discuss implementation techniques for our models, and propose several extensions to the basic framework. We then apply the proposed models to the application of personalized recommendation. Extensive experiments on a social bookmarking dataset, Delicious, and a paper citation dataset, CiteSeer, demonstrate the effectiveness of the proposed models.
On quantifying changes in temporally evolving dataset
Authors: Rohan Choudhary, Sameep Mehta, Amitabha Bagchi
Venue: CIKM
Year: 2008

In this paper, we present a general framework to quantify changes in temporally evolving data. We focus on changes that materialize due to evolution and interactions of features extracted from the data. The changes are captured by the following key transformations: create, merge, split, continue, and cease. First, we identify various factors which influence the importance of each transformation. These factors are then combined using a weight vector. The weight vector encapsulates domain knowledge. We evaluate our algorithm using the following datasets: DBLP, IMDB, Text and Scientific Dataset.
Integrating web query results: holistic schema matching
Authors: Shui-Lung Chuang, Kevin Chen-Chuan Chang
Venue: CIKM
Year: 2008

The emergence of numerous data sources online has presented a pressing need for more automatic yet accurate data integration techniques. For the data returned from querying such sources, most works focus on how to extract the embedded structured data more accurately. However, to eventually provide an integrated access to these query results, a last but not least step is to combine the extracted data coming from different sources. A critical task is finding the correspondence of the data fields between the sources - a problem well known as schema matching. Query results are a small and biased sample set of instances obtained from sources; the obtained schema information is thus very implicit and incomplete, which often prevents existing schema matching approaches from performing effectively. In this paper, we develop a novel framework for understanding and effectively supporting schema matching on such instance-based data, especially for integrating multiple sources. We view discovering matching as constructing a more complete domain schema that best describes the input data. With this conceptual view, we can leverage various data instances and observed regularities seamlessly with holistic, multiple-source schema matching to achieve more accurate matching results. Our experiments show that our framework consistently outperforms baseline pairwise and clustering-based approaches (raising F-measure from 50-89% to 89-94%) and works uniformly well for the surveyed domains.
Efficient processing of probabilistic spatio-temporal range queries over moving objects
Authors: Bruce S. E. Chung, Wang-Chien Lee, Arbee L. P. Chen
Venue: CIKM
Year: 2008

Range queries for querying the current and future positions of the moving objects have received growing interests in the research community. Existing methods, however, assume that an object only moves along an anticipated path. In this paper, we study the problem of answering probabilistic range queries on moving objects based on an uncertainty model, which captures the possible movements of objects with probabilities. We conduct a performance study, which shows our proposal significantly reduces the number of object examinations and the overall cost of the query evaluation.
TinyLex: static n-gram index pruning with perfect recall
Authors: Derrick Coetzee
Venue: CIKM
Year: 2008

Inverted indexes using sequences of characters (n-grams) as terms provide an error-resilient and language-independent way to query for arbitrary substrings and perform approximate matching in a text, but present a number of practical problems: they have a very large number of terms, they exhibit pathologically expensive worst-case query times on certain natural inputs, and they cannot cope with very short query strings. In word-based indexes, static index pruning has been successful in reducing index size while maintaining precision, at the expense of recall. Taking advantage of the unique inclusion structure of n-gram terms of different lengths, we show that the lexicon size of an n-gram index can be reduced by 7 to 15 times without any loss of recall, and without any increase in either index size or query time. Because the lexicon is typically stored in main memory, this substantially reduces the memory required for queries. Simultaneously, our construction is also the first overlapping n-gram index to place tunable worst-case bounds on false positives and to permit efficient queries on strings of any length. Using this construction, we also demonstrate the first feasible n-gram index using words rather than characters as units, and its applications to phrase searching.