OpenRaiser/Intern-Atlas huggingface dataset:
Answering XML queries using materialized views revisited
Authors: Xiaoying Wu, Dimitri Theodoratos, Wendy Hui Wang
Venue: CIKM
Year: 2009

Answering queries using views is a well-established technique in databases. In this context, two outstanding problems can be formulated. The first one consists in deciding whether a query can be answered exclusively using one or multiple materialized views. Given the many alternative ways to compute the query from the materialized views, the second problem consists in finding the best way to compute the query from the materialized views. In the realm of XML, there is a restricted number of contributions in the direction of these problems due to the many limitations associated with the use of materialized views in traditional XML query evaluation models.
Group CRM: a new telecom CRM framework from social network perspective
Authors: Bin Wu, Qi Ye, Shengqi Yang, Bai Wang
Venue: CIKM
Year: 2009

The structure of customer communication network provides us a natural way to understand customers' relationships. Traditional customer relationship management (CRM) methods focus on various customer profitability models, and they are short of ways to understand the social interactions. Graph mining and social network analysis provide ways to understand the relationships between customers, and there are already a few applications in CRM using these methods. To transform the traditional CRM methods from individuals to social groups, we propose a novel technical framework (GCRM) to manage the social groups in massive telecom call graphs. Our framework is based on a series of newly emerged methods for social network analysis, such as group detecting, group evolution tracking and group life-cycle modeling in telecom applications. We analyze the relationships between social groups and propose a method to find potential customers in these groups. To evaluate GCRM, we present a comprehensive study to explore the group evolutions in real-world massive telecom call graphs. Empirical results show that by taking this framework, analysts can gain deeper insights into the communication patterns of social groups and their evolutionary patterns which makes the management of these social groups much easier in real-world telecom applications.
Fragment-based clustering ensembles
Authors: Ou Wu, Mingliang Zhu, Weiming Hu
Venue: CIKM
Year: 2009

Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to large. In this paper, we study the properties of the defined 'clustering fragment' and put forward a useful proposition. Solid proofs are presented with two widely used goodness measures for clustering ensembles. Finally, a new ensemble framework termed as fragment-based clustering ensembles is proposed. Theoretically, most of existing methods can be improved by adopting this framework. To evaluate the proposed framework, three new methods are introduced by bring three popular clustering ensemble methods into our framework. The experimental results on several public data sets show that the three introduced methods are greatly improved in computational complexity and also achieved better or similar accurate results than the original methods.
Characterizing the evolution of collaboration network
Authors: Bin Wu, Fengying Zhao, Shengqi Yang, Lijun Suo, Hongqiao Tian
Venue: CIKM
Year: 2009

It has long been realized that social network of scientific collaborations provides a window on patterns of collaboration within the academic community. Investigations and studies about static and dynamic properties of co-authorship network have also been done in the recent years. However, the accent of most of the research is on the analysis about the macroscopic structure of the whole network or community over time such as distance, diameter shrinking and densication phenomenon and microscopic formation analysis of links,groups or communities over time. But in fact, how an individual or a community grows over time may not only provide a new view point to mine copious and valuable information about scientific networks but also reveal important factors that influence the growth process. In this paper,from a temporal and microscopic analytical perspective, we propose a method to trace scientific individual's and community's growth process based on community's evolution path combination with quantifiable measurements. During the process of tracing, we find out that it is the fact that the lifespan of community is related to the ability of altering its membership, but what's more and complementary, we find out that the lifespan of community is also related to the ability of maintaining its core members meaning that community may last for a longer lifespan if its core members are much more stable. Meanwhile, we also trace the growth process of research individuals based on the evolution of communities.
Automatic web data extraction using tree alignment
Authors: Yingju Xia, Hao Yu, Shu Zhang
Venue: CIKM
Year: 2009

This paper investigates the automatic extraction of data from forums, blogs and news web sites. Web pages are increasingly dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment to automatically extract data from these types of web pages. A new tree alignment algorithm is presented for determining the optimal matching structure of the input web pages. Based on the alignment, the trees are merged into one union tree whose nodes record statistical information obtained from multiple web pages. A heuristic method is employed for determining the most probable content block and the alignment algorithm detects repeating patterns on the union tree. A wrapper built on the most probable content block and the repeating patterns extracts data from web pages. Experimental results show that the method achieves high extraction accuracy and has steady performance.
OfCourse: web content discovery, classification and information extraction for online course materials
Authors: Yuhong Xiong, Ping Luo, Yong Zhao, Fen Lin, Shicong Feng, Baoyao Zhou, Liwei Zheng
Venue: CIKM
Year: 2009

In this paper we present OfCourse, a vertical search engine for online course materials. These materials have the following characteristics: they are scattered very sparsely in the university Web sites; and are generated by the teachers with totally different HMTL templates and layouts. These characteristics impose some challenges for Web Classification (to identify the course materials) and Web Information Extraction (to extract course metadata, such as course title, time and ID) from the identified course homepages. Here, we describe our proposed method to tackle these challenges, and the features of this system. OfCourse, containing over 60,000 courses from the top 50 universities in the US, is currently available for public access at http://fusion.hpl.hp.com/OfCourse/.
Online anonymity for personalized web services
Authors: Yabo Xu, Ke Wang, Guoliang Yang, Ada Wai-Chee Fu
Venue: CIKM
Year: 2009

To receive personalized web services, the user has to provide personal information and preferences, in addition to the query itself, to the web service. However, detailed personal information could identify the sender of sensitive queries, thus compromise user privacy. We propose the notion of online anonymity to enable users to issue personalized queries to an untrusted web service while with their anonymity preserved. The challenge for providing online anonymity is dealing with unknown and dynamic web users who can get online and offline at any time. We define this problem, discuss its implications and differences from the problems in the literature, and propose a solution.
Scalable continuous range monitoring of moving objects in symbolic indoor space
Authors: Bin Yang, Hua Lu, Christian S. Jensen
Venue: CIKM
Year: 2009

Indoor spaces accommodate large populations of individuals. The continuous range monitoring of such objects can be used as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. Indoor space differs from outdoor space in that symbolic locations, e.g., rooms, rather than Euclidean positions or spatial network locations are important. In addition, positioning based on presence sensing devices, rather than, e.g., GPS, is assumed. Such devices report the objects in their activation ranges. We propose an incremental, query-aware continuous range query processing technique for objects moving in this setting. A set of critical devices is determined for each query, and only the observations from those devices are used to continuously maintain the query result. Due to the limitations of the positioning devices, queries contain certain and uncertain results. A maximum-speed constraint on object movement is used to refine the latter results. A comprehensive experimental study with both synthetic and real data suggests that our proposal is efficient and scalable.
Unsupervised binning of environmental genomic fragments based on an error robust selection of l-mers
Authors: Bin Yang, Yu Peng, Henry C. M. Leung, Siu-Ming Yiu, Jing-Chi Chen, Francis Y. L. Chin
Venue: CIKM
Year: 2009

With the rapid development of genome sequencing techniques, the traditional research methods of microorganisms based on the isolation and cultivation are gradually replaced by metagenomics, also known as environmental genomics. The first step, which is still a major bottleneck, of metagenomic is the taxonomic characterization of the DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as 'binning'. Existing binning methods belong to supervised or semi-supervised approaches which rely heavily on the reference genomes of known microorganisms and phylogenetic marker genes. Due to the limited availability of reference genomes and the bias and unstable of marker genes, these methods may not be applicable in all cases. In this paper, we present an unsupervised binning method based on the distribution of a careful selected set of l-mers (substrings of length l in reads). From our experiments, we show that our approach can accurately bin DNA fragments with various length and relative species abundance ratio without any reference and training datasets. Another highlight of our approach is error robustness. The binning accuracy only decreases less than 1% while the sequencing error rate increases from 0% to 5% which is much lower than the typical sequencing error rate of entire existing commercial sequencing platform which is less than 2%. The source code of our software tool, the reference genomes of the species for generating the test datasets and the corresponding test datasets are available at http://i.cs.hku.hk/∼alse/MetaCluster/. Copyright 2009 ACM.
A study of information retrieval on accumulative social descriptions using the generation features
Authors: Lichun Yang, Shengliang Xu, Shenghua Bao, Dingyi Han, Zhong Su, Yong Yu
Venue: CIKM
Year: 2009

This paper is concerned with the study of information retrieval (IR) on Accumulative Social Descriptions (ASDs). ASDs refer to Web texts that accumulated by many Web users describing certain Web resources, such as anchor texts, search logs and social annotations. There have been some studies working on leveraging ASDs for improving search performance in both internet and intranet. However, to the best of our knowledge, no prior study has concerned the specific generation features of ASDs, which are the focus point of this paper. Specifically, we consider the generation features from two perspectives, the generation processes and the generated distributions. Further, three probabilistic IR models are derived based on them. The three models are first demonstrated with one toy dataset and then empirically evaluated with two real datasets: an internet dataset consisting of 90,295 Web pages, with 25,845,818 social annotations crawled from Del.icio.us and 31,320,005 pieces of anchor texts crawled through Yahoo! API, and an intranet dataset consisting of 179,835 Web pages with 1,245,522 annotations dumped from the intranet tagging system in IBM, named as Dogear. Extensive experimental results show that the proposed methods, which fully leverage the generation features of ASDs, improve the performance of both internet and intranet search significantly.
Constructing evolutionary taxonomy of collaborative tagging systems
Authors: Junjie Yao, Yuxin Huang, Bin Cui
Venue: CIKM
Year: 2009

Collaborative tagging systems allow users to label online resources. The tags are generally correlated and evolving according to the change of web contents, and the popularity of tags represent evolution of social interests. Tag taxonomy is a promising solution to organize the data in tagging systems. In this demonstration, we propose to construct the evolutionary taxonomy which incorporates the correlation and evolution of tags, as user generated tags grow and change temporally. We demonstrate that our approach is intuitive and efficient in tag organization which exploits the evolving characteristic of collaborative tagging systems.
A graphical browser for XML schema documents
Authors: Jihyeon Yeom, Hyeokman Kim
Venue: CIKM
Year: 2009

Recently, tools for browsing XML Schema documents have become popular, but they cannot support advanced browsing functionalities. We have implemented a new graphical schema browser which provides traversals not only along a composition hierarchy but also along a type hierarchy defined in the documents. In this demonstration, we show how users can quickly and easily understand the semantic structures by freely navigating to any datatypes or elements located at any position in the hierarchies.
Extraction of a latent blog community based on subject
Authors: Seok-Ho Yoon, Jung-Hwan Shin, Sang-Wook Kim, Sunju Park
Venue: CIKM
Year: 2009

In the blogosphere, there exist posts relevant to a particular subject and blogs that show interests in the subject. In this paper, we define a set of such posts and blogs as "blog community" and propose a method for extracting the blog community associated with a particular subject. The proposed method is based on the idea that the blogs who have performed actions to the posts of a particular subject are the ones that have interests in the subject, and that the posts which have received actions from such blogs are the ones that contain the subject. The proposed method selects a small number of seed posts that contain the subject. Then, it selects the blogs that perform actions to the seed posts over some threshold and the posts that have received actions over some threshold. By repeating these two steps, it gradually expands the blog community. The experimental results show that the proposed method exhibits a higher level of accuracy than the methods proposed in prior research.
Advanced metasearch engines
Authors: Clement T. Yu
Venue: CIKM
Year: 2009

A metasearch engine is a system, which is connected to different search engines. In response to a user query, it invokes suitable search engines for the query, merges the information returned by these search engines and output the merged result. There are two types of metasearch engines: one type for unstructured data (mostly text) and the other for structured data. In comparison to a text search engine, a metasearch engine can have a higher coverage of the Web and can have more timely information. A metasearch engine for structured data facilitates comparison shopping and services and is convenient to use. In this talk, we discuss the problems and their potential solutions. In addition, challenges and unsolved problems are sketched.
TSA'09 workshop summary: topic-sentiment analysis
Authors: Bei Yu, Maojin Jiang
Venue: CIKM
Year: 2009

This workshop seeks to bring together researchers in both computer science and social sciences who are interested in developing and using topic-sentiment analysis methods to measure mass opinion, and to foster communications between the research community and industry practitioners as well.
RefMed: relevance feedback retrieval system fo PubMed
Authors: Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko, Sungchul Kim
Venue: CIKM
Year: 2009

Finding related articles from the PubMed (a large biomedical literature repository) is challenging because it is hard to express the user's specific relevance in the given query interface and a keyword query typically retrieves many results. Biomedical researchers spend a critical amount of time (e.g., often more than several days) in the literature search process. This paper proposes RefMed, a novel search system for PubMed, which supports relevance ranking by enabling relevance feedback on PubMed. RefMed first returns initial result documents for a user's keyword query as in PubMed. The user then makes relevance judgments on some of the resultant documents while browsing them. Once the user pushes the feedback, the system induces a relevance function using RankSVM and ranks the results according to the function. To realize the ad-hoc relevance retrieval on PubMed, RefMed tightly integrates RankSVM within RDBMS and runs the rank learning and process on the fly with a response time of a few minutes.Our qualitative experiments with biomedical researchers show that RefMed substantially reduces the amount of effort required to search related PubMed articles. RefMed is accessible at http://dm.postech.ac.kr/refmed.
Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS
Authors: Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko, Sungchul Kim, Wook-Shin Han
Venue: CIKM
Year: 2009

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed.
An integrated discriminative probabilistic approach to information extraction
Authors: Xiaofeng Yu, Wai Lam, Bo Chen
Venue: CIKM
Year: 2009

Probabilistic graphical models for sequence data enable us to effectively deal with inherent uncertainty in many real-world domains. However, they operate on a mostly propositional level. Logic approaches, on the other hand, can compactly represent a wide variety of knowledge, especially first-order ones, but treat uncertainty only in limited ways. Therefore, combining probability and first-order logic is highly desirable for information extraction which requires uncertainty modeling as well as dependency and deeper knowledge representation. In this paper, we model both segmentations in observation sequence and relations of segments simultaneously in our proposed integrated discriminative probabilistic framework. We propose the Metropolis-Hastings, a Markov chain Monte Carlo (MCMC) algorithm for approximate Bayesian inference to find the maximum a posteriori assignment of all the variables of this model. This integrated model has several advantages over previous probabilistic graphical models, and it offers a great capability of extracting implicit relations and new relation discovery for relation extraction from encyclopedic documents, and capturing sub-structures in named entities for named entity recognition. We performed extensive experiments on the above two well-established information extraction tasks, illustrating the feasibility and promise of our approach.
Efficient feature weighting methods for ranking
Authors: Hwanjo Yu, Jinoh Oh, Wook-Shin Han
Venue: CIKM
Year: 2009

Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generalization performance of ranking functions in information retrieval. Due to fundamental differences between classification and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, has been recently proposed, which exploits importance of each feature and similarity between every pair of features. However, GAS must compute the similarity scores of all pairs of features, thus it is not scalable for high-dimensional data and its performance degrades on nonlinear ranking functions. This paper proposes novel algorithms, RankWrapper and RankFilter, which is scalable for high-dimensional data and also performs reasonably well on nonlinear ranking functions. RankWrapper and RankFilter are designed based on the key idea of Relief algorithm. Relief is a feature selection algorithm for classification, which exploits the notions of hits (data points within the same class) and misses (data points from different classes) for classification. However, there is no such notion of hits or misses in ranking. The proposed algorithms instead utilize the ranking distances of nearest data points in order to identify the key features for ranking. Our extensive experiments show that RankWrapper and RankFilter generate higher accuracy overall than the GAS and traditional Relief algorithms adapted for ranking, and run substantially faster than the GAS on high dimensional data.
Transaction synchronization protocol using XML in client-server environment
Authors: Noman Zahid, Muhammad Shahid Ansari, Kyung-Goo Doh
Venue: CIKM
Year: 2009

The problem of transaction synchronization is important in XML-based applications based on client-server model, especially when both server and clients are allowed to store data. When clients modify their local database, writing this transaction back to the server and to all other clients requires transaction synchronization. To achieve reliable and efficient transaction synchronization, there is a need for some sort of efficient protocol that can guarantee the synchronization of databases on the server and all clients. In this paper, a protocol is proposed for transaction synchronization using XML database and XML as a transfer medium between the server and clients. Importantly, this protocol can be used in any type of database with XML as a data transfer medium using some kind of database mapping from any database to XML. This protocol significantly reduces the cost of data transfer between server and clients, thus increasing efficiency.
Fuzzy semantic web ontology learning from fuzzy UML model
Authors: Fu Zhang, Zong Min Ma, Jingwei Cheng, Xiangfu Meng
Venue: CIKM
Year: 2009

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.