OpenRaiser/Intern-Atlas huggingface dataset:
Text mining in genomics and systems biology
Authors: Alfonso Valencia
Venue: CIKM
Year: 2008

There is an increasing need of complementing the information available for the analysis of biological systems in Systems Biology and Genomics projects. A need that makes interesting the integration of information directly extracted from textual sources using Information Extraction and Text Mining approaches. My group has been working in developing Text Mining approaches and in their integration in large-scale projects together with other experimental and bioinformatics methods. In this occasion I will present the developments related with the characterization of the human mitotic spindle apparatus, developed in the context of the ENFIN NoE. For these, and other, applications it is crucial to have an accurate estimation of the capacity of the current Text Mining systems. The BioCreative II challenge organized by CNIO, MITRE and NCBI in collaboration with the MINT and INTACT databases (http://biocreative.sourceforge.net, Genome Biology, August 2008 Special Issue) provides such an overview. BioCreative II was in two task: 1) gene name identification and normalization, where many systems were able to achieve a consistent 80% balance precision / recall. And 2) protein interaction detection that was divided in four sub-tasks: a) ranking of publications by their relevance on experimental determination of protein interactions, b) detection of protein interaction partners in text, c) detection of key sentences describing protein interactions, and d) detection of the experimental technique used to determine the interactions. The results were quite good in the categories of publication raking, detection of experimental methods, and highlighting of relevant sentences, while they pointed to persistent problems in the correct normalization of gene/protein names. Furthermore BioCreative has channel the collaboration of several teams for the creation of the first Text Mining meta-server (The BioCreative Meta-server, Leitner et al., Genome Biology 2008 BioCreative special issue). We are working now in the preparation of BioCreative III, with particular focus in fostering the creation of Text Mining systems that can be integrated in Genome analysis pipelines, and contribute effectively to the understanding of complex Biological Systems.
Tag-based filtering for personalized bookmark recommendations
Authors: Pavan Kumar Vatturi, Werner Geyer, Casey Dugan, Michael J. Muller, Beth Brownholtz
Venue: CIKM
Year: 2008

This paper investigates using social tags for the purpose of making personalized content recommendations. Our tag-based recommender creates a personalized bookmark recommendation model for each user based on current and general interest tags, defined by different time intervals.
Siphon++: a hidden-webcrawler for keyword-based interfaces
Authors: Karane Vieira, Luciano Barbosa, Juliana Freire, Altigran Soares da Silva
Venue: CIKM
Year: 2008

The hidden Web consists of data that is generally hidden behind form interfaces, and as such, it is out of reach for traditional search engines. With the goal of leveraging the high-quality information in this largely unexplored portion of the Web, in this paper, we propose a new strategy for automatically retrieving data hidden behind keyword-based form interfaces. Unlike previous approaches to this problem, our strategy adapts the query generation and selection by detecting features of the index. We describe an extensive experimental evaluation which shows that: our strategy is able to derive appropriate queries to obtain high coverage while, at the same time, avoiding the retrieval of redundant data; and it obtains higher coverage and is more efficient approaches that use a fixed strategy for query generation.
Estimating retrieval effectiveness using rank distributions
Authors: Vishwa Vinay, Natasa Milic-Frayling, Ingemar J. Cox
Venue: CIKM
Year: 2008

In this paper, we consider the task of estimating query effectiveness, i.e., assessment of the retrieval system performance in absence of the user relevance judgments. In our approach we model the score associated with each document in the result set as a Gaussian random variable. The mean and the variance of each document score can then be used to estimate the probability that a document will be ranked above another one and thus calculate the expected rank of the document in the ranked list. We propose to measure the effectiveness of the system performance by comparing the predicted and actual ranks of the retrieved documents. In our experiments we consider two retrieval models and five document scoring methods and evaluate their impact on the proposed estimation measures. Our experiments with standardized data sets that include document relevance judgments and the task of predicting the relative query effectiveness show that the expected rank metric is robust to variations in document scoring and retrieval algorithms.
Cross-lingual query classification: a preliminary study
Authors: Xuerui Wang, Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, Bo Pang
Venue: CIKM
Year: 2008

The non-English Web is growing at breakneck speed, but available language processing tools are mostly English based. Taxonomies are a case in point: while there are plenty of commercial and non-commercial taxonomies for the English Web, taxonomies for other languages are either not available or of very limited quality. Given that building taxonomies in all non-English languages is prohibitively expensive, it is natural to ask whether existing English taxonomies can be leveraged, possibly via machine translation, to enable information processing tasks in other languages. Preliminary results presented in this paper indicate that the answer is affirmative with respect to query classification, a task which is essential both for understanding the user intent and thus provide better search results, and for better targeting of search-based advertising, the economic underpinning of commercial Web search engines. We propose a robust method for classifying non-English queries against an English taxonomy and classifier using widely available, off-the-shelf machine translation systems. In particular, we show that by viewing the search results in the query's original language as independent sources of information, we can alleviate the impact of poor quality or erroneous machine translations. Empirical results for Chinese queries show that we achieve remarkably encouraging results.
Semi-supervised metric learning by maximizing constraint margin
Authors: Fei Wang, Shouchun Chen, Changshui Zhang, Tao Li
Venue: CIKM
Year: 2008

Distance metric learning is an old problem that has been researched in the supervised learning field for a very long time. In this paper, we consider the problem of learning a proper distance metric under the guidance of some weak supervisory information. Specifically, those information are in the form of pairwise constraints which specify whether a pair of data points are in the same class (must link constraints) or in the different classes (cannot link constraints). Given those constraints, our algorithm aims to learn a distance metric under which the points with must link constraints are pushed as close as possible, while simultaneously the points with cannot link constraints are pulled away as far as possible. Finally the experimental results are presented to show the effectiveness of our method.
Explorations in tag suggestion and query expansion
Authors: Jian Wang, Brian D. Davison
Venue: CIKM
Year: 2008

The query used in a search system is only an approximation to the user's true information need, and as a result, many factors can reduce the quality of search results. One is query ambiguity, causing searchers with different needs to issue the same query. For example, for the query java, some users may want to find java tutorial while others may want to download java software. Other factors include a vocabulary mismatch and a lack of knowledge regarding the contents of the document collection. In any case, many users benefit from assistance in forming a good query. As a result, some commercial services provide query suggestions for many queries. In this paper, we propose a Tag Suggestion System that takes advantage of tags associated with query results to expand a searcher's query. Since not every web page is associated with existing tags, we first build an auto-tagging system which can assign multiple tags to web pages, including news, blogs, etc. The current system contains the most popular 140 tags in del.icio.us, with high precision performance. A small user study is performed to evaluate anecdotally the performance of our Tag Suggestion System, showing better quality than the query suggestion mechanisms provided by Yahoo! and Google. The result pages of expanded queries generated by the Tag Suggestion System are also significantly better than those of the Google original system.
Speed up semantic search in p2p networks
Authors: Qiang Wang, Rui Li, Lei Chen, Jie Lian, M. Tamer Özsu
Venue: CIKM
Year: 2008

Peer-to-peer architectures become popular in modern massively distributed systems, which are often in very large scale and contain a huge volume of heterogeneous data. To facilitate the information retrieval process in P2P networks, we consider semantic search approach, where syntax-based queries are shipped to peers based on semantic correlations. Motivated by an interesting experience in Web information retrieval, we propose a novel ontology-based scheme to measure similarity of peer interests accurately and consistently in a decentralized way, and group peers under a scalable hierarchical overlay network. Given queries, our approach either floods them within local peer groups or guides them towards remote groups based on the similarity of interests. Our work overcomes the limitations of the existing P2P hybrid-search approaches by avoiding costly data popularity measurement. Performance evaluation and comparison against baseline algorithms show that our approach provides a better solution for information retrieval in large-scale P2P networks.
A microscopic view on community detection in complex networks
Authors: Yang Wang, Huaiming Song, Weiping Wang, Mingyuan An
Venue: CIKM
Year: 2008

The community structure is a basic characteristic of complex networks. A strong community structure has high modularity. It has been proven an NP-Complete problem to identify the community structure with the highest modularity. Many approximate algorithms have been proposed to alleviate the problem. However, they suffer from inefficiency or low quality. In this paper, we propose a two-step method. The first step of our method analyze the vertex similarity of the network, which is a microscopic view. If a pair of vertices are similar enough, they will be put into the same community. The second step of our method focuses on the increment of modularity of the similarity-based communities generated by the first step. If the number of edges between two communities is greater than the expected number based on random choice, the two communities will be merged. The second step is implemented by the CNM algorithm or its improvement CNM+HE'. The similarity-based community remedies the defect on microscope introduced by CNM or CNM+HE'. Our method runs efficiently and finds meaningful communities effectively. We tested the method on more than twenty datasets. The modularity of community structure found by the method is higher than the state-of-the-art algorithm.
CE2: towards a large scale hybrid search engine with integrated ranking support
Authors: Haofen Wang, Thanh Tran, Chang Liu
Venue: CIKM
Year: 2008

The Web contains a large amount of documents and increasingly, also semantic data in the form of RDF triples. Many of these triples are annotations that are associated with documents. While structured query is the principal mean to retrieve semantic data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both documents and semantic data can address more complex information needs. In this paper, we present CE2, an integrated solution that leverages mature database and information retrieval technologies to tackle challenges in hybrid search on the large scale. For scalable storage, CE2 integrates database with inverted indices. Hybrid query processing is supported in CE2 through novel algorithms and data structures, which allow for advanced ranking schemes to be integrated more tightly into the process. Experiments conducted on Dbpedia and Wikipedia show that CE2 can provide good performance in terms of both effectiveness and efficiency.
Dual encryption for query integrity assurance
Authors: Haixun Wang, Jian Yin, Chang-Shing Perng, Philip S. Yu
Venue: CIKM
Year: 2008

In database outsourcing, an enterprise contracts its database management tasks to an outside database service provider to eliminate in-house hardware, software, and expertise needs for running DBMSs. This is attractive especially for the parties with limited abilities in managing their own data. Typically, the client applications want to obtain quality assurance (e.g., data authenticity and query completeness) of the outsourced database service at a low cost. Previous work on database outsourcing has focused on issues such as communication overhead, secure data access, and data privacy. Recent work has introduced the issue of query integrity assurance, but usually, to obtain such assurance incurs a high cost. In this paper, we present a new method called dual encryption to provide low-cost query integrity assurance for outsourced database services. Dual encryption enables cross examination of the outsourced data, which consists of the original data stored under a certain encryption scheme, and another small percentage of the original data stored under a different encryption scheme. We generate queries against the additional piece of data and analyze their results to obtain integrity assurance. Our scheme is provable secure, that is, it is impossible to break our scheme unless some security primitives can be broken. Experiments on commercial workloads show the effectiveness of our approach.
Automatic online news topic ranking using media focus and user attention based on aging theory
Authors: Canhui Wang, Min Zhang, Liyun Ru, Shaoping Ma
Venue: CIKM
Year: 2008

News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.
Predicting web spam with HTTP session information
Authors: Steve Webb, James Caverlee, Calton Pu
Venue: CIKM
Year: 2008

Web spam is a widely-recognized threat to the quality and security of the Web. Web spam pages pollute search engine indexes, burden Web crawlers and Web mining services, and expose users to dangerous Web-borne malware. To defend against Web spam, most previous research analyzes the contents of Web pages and the link structure of the Web graph. Unfortunately, these heavyweight approaches require full downloads of both legitimate and spam pages to be effective, making real-time deployment of these techniques infeasible for Web browsers, high-performance Web crawlers, and real-time Web applications. In this paper, we present a lightweight, predictive approach to Web spam classification that relies exclusively on HTTP session information (i.e., hosting IP addresses and HTTP session headers).
Statistical power in retrieval experimentation
Authors: William Webber, Alistair Moffat, Justin Zobel
Venue: CIKM
Year: 2008

The power of a statistical test specifies the sample size required to reliably detect a given true effect. In IR evaluation, the power corresponds to the number of topics that are likely to be sufficient to detect a certain degree of superiority of one system over another. To predict the power of a test, one must estimate the variability of the population being sampled from; here, of between-system score deltas. This paper demonstrates that basing such an estimation either on previous experience or on trial experiments leaves wide margins of error. Iteratively adding more topics to the test set until power is achieved is more efficient; however, we show that it leads to a bias in favour of finding both power and significance. A hybrid methodology is proposed, and the reporting requirements of the experimenter using this methodology are laid out. We also demonstrate that greater statistical power is achieved for the same relevance assessment effort by evaluating a large number of topics shallowly than a small number deeply.
Web-scale named entity recognition
Authors: Casey Whitelaw, Alexander Kehlenbeck, Nemanja Petrovic, Lyle H. Ungar
Venue: CIKM
Year: 2008

Automatic recognition of named entities such as people, places, organizations, books, and movies across the entire web presents a number of challenges, both of scale and scope. Data for training general named entity recognizers is difficult to come by, and efficient machine learning methods are required once we have found hundreds of millions of labeled observations. We present an implemented system that addresses these issues, including a method for automatically generating training data, and a multi-class online classification training method that learns to recognize not only high level categories such as place and person, but also more fine-grained categories such as soccer players, birds, and universities. The resulting system gives precision and recall performance comparable to that obtained for more limited entity types in much more structured domains such as company recognition in newswire, even though web documents often lack consistent capitalization and grammatical sentence construction.
Topological analysis of an online social network for older adults
Authors: Marcella Wilson, Charles K. Nicholas
Venue: CIKM
Year: 2008

Social network systems on the Internet, such MySpace and LinkedIn, are growing in popularity around the world. The level of such activity is now comparable to that associated with email and blogs. Our research addresses the question of whether people in different demographic groups use these systems in the same way. Older Americans tend to use email the same way as Americans in general. The usage of blogs, however, is different, with significant differences in the topological and structural patterns of post and response in blogs being evident in different demographics. We discover important information about the topological structures of online social networks by examining topological patterns in blog posts, also known as cascades. To accomplish this, we create and study the blogosphere, blog and post networks of an online social network used primarily by older adults. We also study the topological patterns of cascades in greater detail, reporting their common shapes, properties and size distribution. Our research has implications for the design of social network software for older Americans, as well as the algorithms used in search engines for such systems.
Routing of structured queries in large-scale distributed systems
Authors: Judith Winter
Venue: CIKM
Year: 2008

In order to search XML-document collections, structural information – given by a user in the form of a structured query or provided by the self-describing structure of XML-documents – have been used in the past years to improve Information Retrieval (IR) quality in terms of recall and precision. However, all known approaches have only been used in classical client-/server (C/S) architectures. None have ever been applied to improve retrieval in large-scale distributed systems such as Peer-to-Peer (P2P) networks, where efficiency issues have to be dealt with carefully, e.g. in order to reduce communication overhead between distributed nodes. As P2P networks can be considered promising alternatives to C/S-systems for storing large amounts of information including XML-documents, possibilities for improving the retrieval in such networks should be investigated. In this paper, we concentrate on query routing in such a scenario and raise the question, how structured queries can be routed in a highly distributed environment so as to increase both efficiency and effectiveness. We provide an infrastructure for investigating this question and propose techniques for performing routing based on a mixture of document-, element-, collection- and peerevidence. We also report on preliminary evaluation results with the INEX collection.
Ranking information resources in peer-to-peer text retrieval: an experimental study
Authors: Hans Friedrich Witschel
Venue: CIKM
Year: 2008

This paper experimentally studies approaches to the problem of ranking information resources w.r.t. user queries in peer-to-peer information retrieval. In distributed environments, for each given user query and a set of information resources that are available, we need to select the right subset of these resources to forward the query to. Here, we study the problem of pruning descriptions of resources to acceptable lengths in a peer-to-peer scenario and two approaches to overcome the mismatch problem that may arise as a consequence of the pruning, namely query expansion and learning better resource descriptions from query streams. The results show that resource descriptions can be pruned to a large extent without ill effects and that learning better descriptions from query streams works much better than query expansion.
Error recovery in human-computer interaction: a preliminary study in a database learning environment
Authors: Lei Wu
Venue: CIKM
Year: 2008

Most studies on errors in human-computer interactions (HCI errors) focused on how to improve system to reduce errors. Because HCI errors are inevitable, studies taking users' perspective on how users recover from errors are equally important to enhance system's error tolerances or help users deal with errors easier. This paper reported on preliminary results of a study on database learning. As a part of a larger project, this study identifies the nature of HCI errors in interactions and analyzes error episodes, recovery trials, and recovery actions using the ACCESS database. Further, it suggests systematic studies to examine how users recovered from different types of errors.