OpenRaiser/Intern-Atlas huggingface dataset:
Blogger-centric contextual advertising
Authors: Teng-Kai Fan, Chia-Hui Chang
Venue: CIKM
Year: 2009

This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers' interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers' immediate personal interests in order to improve online contextual advertising. The proposed BCCA (Blogger-Centric Contextual Advertising) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger's personal interests.
L2 norm regularized feature kernel regression for graph data
Authors: Hongliang Fei, Jun Huan
Venue: CIKM
Year: 2009

Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined from graph data are graphs. Such graph features have different number of nodes and edges and usually overlap with each other. In conventional data mining and machine learning applications, the internal structure of features are usually ignored. In this paper we consider a supervised learning problem where the features of the data set have intrinsic complexity, and we further assume that the feature intrinsic complexity may be measured by a kernel function. We hypothesize that by regularizing model parameters using the information of feature complexity, we can construct simple yet high quality model that captures the intrinsic structure of the data. Towards the end of testing this hypothesis, we focus on a regression task and have designed an algorithm that incorporate the feature complexity in the learning process, using a kernel matrix weighted L2 norm for regularization, to obtain improved regression performance over conventional learning methods that does not consider the additional information of the feature. We have tested our algorithm using 5 different real-world data sets and have demonstrate the effectiveness of our method.
Acronym extraction and disambiguation in large-scale organizational web pages
Authors: Shicong Feng, Yuhong Xiong, Conglei Yao, Liwei Zheng, Wei Liu
Venue: CIKM
Year: 2009

In this paper, we focus on the automatic extraction and disambiguation of acronyms in large-scale organizational web pages, which is important but difficult due to the diversity of acronyms and the scale of organizational web pages. We propose two novel algorithms to address the key problems in acronym extraction and disambiguation: (1) An unsupervised ranking algorithm to automatically filter out the incorrect acronym-expansion pairs. Different from the existing approaches, our method does not require any hand-crafted rules; (2) A graph-based algorithm to disambiguate ambiguous acronyms, which leverages the hyperlinks of pages to facilitate the acronym disambiguation. We evaluate the proposed approaches using two large-scale, real-world datasets in two different domains. Our experimental results show that our approach is domain independent, and achieves higher precision and recall than the existing methods.
Using opinion-based features to boost sentence retrieval
Authors: Ronald T. Fernández, David E. Losada
Venue: CIKM
Year: 2009

Opinion mining has become recently a major research topic. A wide range of techniques have been proposed to enable opinion-oriented information seeking systems. However, little is known about the ability of opinion-related information to improve regular retrieval tasks. Our hypothesis is that standard retrieval methods might benefit from the inclusion of opinion-based features. A sentence retrieval scenario is a natural choice to evaluate this claim. We propose here a formal method to incorporate some opinion-based features of the sentences as query-independent evidence. We show that this incorporation leads to retrieval methods whose performance is significantly better than the the performance of state of the art sentence retrieval models.
Evidence of quality of textual features on the web 2.0
Authors: Flavio Figueiredo, Fabiano Belém, Henrique Pinto, Jussara M. Almeida, Marcos André Gonçalves, David Fernandes de Oliveira, Edleno Silva de Moura, Marco Cristo
Venue: CIKM
Year: 2009

The growth of popularity of Web 2.0 applications greatly increased the amount of social media content available on the Internet. However, the unsupervised, user-oriented nature of this source of information, and thus, its potential lack of quality, have posed a challenge to information retrieval (IR) services. Previous work focuses mostly only on tags, although a consensus about its effectiveness as supporting information for IR services has not yet been reached. Moreover, other textual features of the Web 2.0 are generally overseen by previous research.
Scalable indexing of RDF graphs for efficient join processing
Authors: George H. L. Fletcher, Peter W. Beck
Venue: CIKM
Year: 2009

Current approaches to RDF graph indexing suffer from weak data locality, i.e., information regarding a piece of data appears in multiple locations, spanning multiple data structures. Weak data locality negatively impacts storage and query processing costs. Towards stronger data locality, we propose a Three-way Triple Tree (TripleT) secondary memory indexing technique to facilitate flexible and efficient join evaluation on RDF data. The novelty of TripleT is that the index is built over the atoms occurring in the data set, rather than at a coarser granularity, such as whole triples occurring in the data set; and, the atoms are indexed regardless of the roles (i.e., subjects, predicates, or objects) they play in the triples of the data set. We show through extensive empirical evaluation that TripleT exhibits multiple orders of magnitude improvement over the state-of-the-art, in terms of both storage and query processing costs.
A general markov framework for page importance computation
Authors: Bin Gao, Tie-Yan Liu, Zhiming Ma, Taifeng Wang, Hang Li
Venue: CIKM
Year: 2009

We propose a General Markov Framework for computing page importance. Under the framework, a Markov Skeleton Process is used to model the random walk conducted by the web surfer on a given graph. Page importance is then defined as the product of page reachability and page utility, which can be computed from the transition probability and the mean staying time of the pages in the Markov Skeleton Process respectively. We show that this general framework can cover many existing algorithms as its special cases, and that the framework can help us define new algorithms to handle more complex problems. In particular, we demonstrate the use of the framework with the exploitation of a new process named Mirror Semi-Markov Process. The experimental results validate that the Mirror Semi-Markov Process model is more effective than previous models in several tasks.
Efficient itemset generator discovery over a stream sliding window
Authors: Chuancong Gao, Jianyong Wang
Venue: CIKM
Year: 2009

Mining generator patterns has raised great research interest in recent years. The main purpose of mining itemset generators is that they can form equivalence classes together with closed itemsets, and can be used to generate simple classification rules according to the MDL principle. In this paper, we devise an efficient algorithm called StreamGen to mine frequent itemset generators over a stream sliding window. We adopt a novel enumeration tree structure to help keep the information of mined generators and the border between generators and non-generators, and propose some optimization techniques to speed up the mining process. We further extend the algorithm to directly mine a set of high quality classification rules over stream sliding windows while keeping high performance. The extensive performance study shows that our algorithm outperforms other state-of-the-art algorithms which perform similar tasks in terms of both runtime and memory usage efficiency, and has high utility in terms of classification.
The gardener's problem for web information monitoring
Authors: Byron J. Gao, Mingji Xia, Walter Cai, David C. Anastasiu
Venue: CIKM
Year: 2009

We introduce and theoretically study the Gardener's problem that well models many web information monitoring scenarios, where numerous dynamically changing web sources are monitored and local information needs to be periodically updated under communication and computation capacity constraints. Typical such examples include maintenance of inverted indexes for search engines and maintenance of extracted structures for unstructured data management systems. We formulate a corresponding multicriteria optimization problem and propose heuristic solutions.
OLAP with UDFs in digital libraries
Authors: Carlos Garcia-Alvarado, Zhibo Chen, Carlos Ordonez
Venue: CIKM
Year: 2009

Queries on digital libraries generally involve the retrieval of specific documents, but most techniques lack the ability to efficiently explore these collections. The integration of OLAP techniques with digital libraries allows users to navigate throughout these collections on multiple levels. In order to accomplish this, we propose the creation of OLAP networks, a complex data structure that contains summarized representations of the original collection of metadata to enrich traditional retrievals and allow the users to quickly explore the collection. We developed a system that enables OLAP-based exploration on the metadata of digital libraries through the use of a combination of efficient UDFs and optimized SQL queries. In addition, we also incorporated visualization methods into our system to allow fast navigation and exploration.
Automatic link detection: a sequence labeling approach
Authors: James J. Gardner, Li Xiong
Venue: CIKM
Year: 2009

The popularity of Wikipedia and other online knowledge bases has recently produced an interest in the machine learning community for the problem of automatic linking. Automatic hyperlinking can be viewed as two sub problems - link detection which determines the source of a link, and link disambiguation which determines the destination of a link. Wikipedia is a rich corpus with hyperlink data provided by authors. It is possible to use this data to train classifiers to be able to mimic the authors in some capacity. In this paper, we introduce automatic link detection as a sequence labeling problem. Conditional random fields (CRFs) are a probabilistic framework for labeling sequential data. We show that training a CRF with different types of features from the Wikipedia dataset can be used to automatically detect links with almost perfect precision and high recall.
Active learning in partially supervised classification
Authors: Priyanka Garg, S. Sundararajan
Venue: CIKM
Year: 2009

Positive Example based learners reduce human annotation effort significantly by removing the burden of labeling the negative examples. Various methods have been proposed in literature for building classifiers using positive and unlabeled examples. However, we empirically observe that classification accuracy of the state of the art methods degrades significantly as the number of labeled positive examples decreases. In this paper, we propose an active learning based method to address this issue. The proposed method learns starting from a handful of positively labeled examples and a large number of unlabeled examples. Experimental results on benchmark datasets show that the proposed method performs better than the state of the art methods when the percentage of labeled positive examples is small.
A code generation approach to optimizing high-performance distributed data stream processing
Authors: Bugra Gedik, Henrique Andrade, Kun-Lung Wu
Venue: CIKM
Year: 2009

We present a code-generation-based optimization approach to bringing performance and scalability to distributed stream processing applications. We express stream processing applications using an operator-based, stream-centric language called SPADE, which supports composing distributed data flow graphs out of toolkits of type-generic operators. A major challenge in building such applications is to find an effective and flexible way of mapping the logical graph of operators into a physical one that can be deployed on a set of distributed nodes. This involves finding how best operators map to processes and how best processes map to computing nodes. In this paper, we take a two-stage optimization approach, where an instrumented version of the application is first generated by the SPADE compiler to profile and collect statistics about the processing and communication characteristics of the operators within the application. In the second stage, the profiling information is fed to an optimizer to come up with a physical data flow graph that is deployable across nodes in a computing cluster. This approach not only creates highly optimized applications that are tailored to the underlying computing and networking infrastructure, but also makes it possible to re-target the application to a different hardware setup by simply repeating the optimization step and re-compiling the application to match the physical flow graph produced by the optimizer. Using real-world applications, from diverse domains such as finance and radio-astronomy, we demonstrate the effectiveness of our approach on System S -- a large-scale, distributed stream processing platform.
Ranking model adaptation for domain-specific search
Authors: Bo Geng, Linjun Yang, Chao Xu, Xian-Sheng Hua
Venue: CIKM
Year: 2009

Recently, various domain-specific search engines emerge, which are restricted to specific topicalities or document formats, and vertical to the broad-based search. Simply applying the ranking model trained for the broad-based search to the verticals cannot achieve a sound performance due to the domain differences, while building different ranking models for each domain is both laborious for labeling sufficient training samples and time-consuming or the training process. In this paper, to address the above difficulties, we investigate two problems: (1) whether we can adapt the ranking model learned for existing Web page search or verticals, to the new domain, so that the amount of labeled data and the training cost is reduced, while the performance requirement is still satisfied; and (2) how to adapt the ranking model from auxiliary domains to a new target domain. We address the second problem from the regularization framework and an algorithm called ranking adaptation SVM is proposed. Our algorithm is flexible enough, which needs only the prediction from the existing ranking model, rather than the internal representation of the model or the data from auxiliary domains. The first problem is addressed by the proposed ranking adaptability measurement, which quantitatively estimates if an existing ranking model can be adapted to the new domain. Extensive experiments are performed over Letor benchmark dataset and two large scale datasets crawled from different domains through a commercial internet search engine, where the ranking model learned for one domain will be adapted to the other. The results demonstrate the applicabilities of the proposed ranking model adaptation algorithm and the ranking adaptability measurement.
A hybrid index structure for geo-textual searches
Authors: Richard Göbel, Andreas Henrich, Raik Niemann, Daniel Blank
Venue: CIKM
Year: 2009

The efficient execution of multi-criteria queries has gained increasing interest over the last years. In the present paper we propose an R-tree based approach for queries addressing textual as well as geographic filter conditions. Whereas most previous approaches use an index structure optimised for a single criterion adding special treatment for the other criterion at the leaf nodes or end points of this index structure, our approach uses a deeper integration. In short, R-trees are maintained for certain subsets of the whole term set. Furthermore, in each of these R-trees bit sets are used within the nodes to indicate whether entries for the terms associated with the single bits can be found in the corresponding sub-tree. Our index structure aims to be both, time and space efficient. The paper investigates the efficiency and applicability of the proposed index structure via practical experiments based on real-world and synthetic data.
Record linkage performance for large data sets
Authors: Jordi Gómez-Bao, Josep Lluís Larriba-Pey, Josepa Ribes Puig
Venue: CIKM
Year: 2009

We propose new data structures to speed up Record Linkage that take advantage of the value distribution of usual string attributes, like name or surname. Using some additional memory, we increase the processing speed by almost an order of magnitude without losing recall or precision at all. The improvement achieved is independent from the methods used for reducing the number of record comparisons, like Blocking or Sliding Window, and the specific string comparison functions.
LuposDate: a semantic web database system
Authors: Jinghua Groppe, Sven Groppe, Andreas Schleifer, Volker Linnemann
Venue: CIKM
Year: 2009

Managing and querying Semantic Web are important issues for Semantic Web applications. Therefore, we have developed a Semantic Web database system with logically and physically optimized SPARQL engines to manage and query RDF data, named LuposDate. In order to present the functionalities of the LUPOSDATE system and engines, we have developed an online demonstration, which is available at http://www.ifis.uni-luebeck.de/index.php?id=luposdate-demo.
Probabilistic moving range query over RFID spatio-temporal data streams
Authors: Yu Gu, Ge Yu, Na Guo, Yueguo Chen
Venue: CIKM
Year: 2009

Moving range query over RFID data streams is one of the most important spatio-temporal queries to support valuable information analysis. However, the location uncertainty challenges the query strategy. In this paper, we propose a probability evaluation model in the RFID-enabled monitoring environments and discuss the query optimization techniques under the scenarios of continuous moving range query, which can also be applied into more situations. The extensive experimental evaluation verifies the efficiency and effectiveness of our proposed model and methods.
Subspace maximum margin clustering
Authors: Quanquan Gu, Jie Zhou
Venue: CIKM
Year: 2009

In text mining, we are often confronted with very high dimensional data. Clustering with high dimensional data is a challenging problem due to the curse of dimensionality. In this paper, to address this problem, we propose an subspace maximum margin clustering (SMMC) method, which performs dimensionality reduction and maximum margin clustering simultaneously within a unified framework. We aim to learn a subspace, in which we try to find a cluster assignment of the data points, together with a hyperplane classifier, such that the resultant margin is maximized among all possible cluster assignments and all possible subspaces. The original problem is transformed from learning the subspace to learning a positive semi-definite matrix, in order to avoid tuning the dimensionality of the subspace. The transformed problem can be solved efficiently via cutting plane technique and constrained concave-convex procedure (CCCP). Since the sub-problem in each iteration of CCCP is joint convex, alternating minimization is adopted to obtain the global optimum. Experiments on benchmark data sets illustrate that the proposed method outperforms the state of the art clustering methods as well as many dimensionality reduction based clustering approaches.
Detection of orthogonal concepts in subspaces of high dimensional data
Authors: Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl
Venue: CIKM
Year: 2009

In the knowledge discovery process, clustering is an established technique for grouping objects based on mutual similarity. However, in today's applications for each object very many attributes are provided. As multiple concepts described by different attributes are mixed in the same data set, clusters do not appear in all dimensions. In these high dimensional data spaces, each object can be clustered in several projections of the data. However, recent clustering techniques do not succeed in detection of these orthogonal concepts hidden in the data. They either miss multiple concepts for each object by partitioning approaches or provide redundant clusters in very similar subspaces. In this work we propose a novel clustering method aiming only at orthogonal concept detection in subspaces of the data. Unlike existing clustering approaches, OSCLU (Orthogonal Subspace CLUstering) detects for each object the orthogonal concepts described by differing attributes while pruning similar concepts. Thus, each detected cluster in an orthogonal subspace provides novel information about the hidden structure of the data. Thorough experiments on real and synthetic data show that OSCLU yields substantial quality improvements over existing clustering approaches.
Mining linguistic cues for query expansion: applications to drug interaction search
Authors: Sheng Guo, Naren Ramakrishnan
Venue: CIKM
Year: 2009

Given a drug under development, what are other drugs or biochemical compounds that it might interact with? Early answers to this question, by mining the literature, are valuable for pharmaceutical companies, both monetarily and in avoiding public relations nightmares. Inferring drug-drug interactions is also important in designing combination therapies for complex diseases including cancers. We study this problem as one of mining linguistic cues for query expansion. By using (only) positive instances of drug interactions, we show how we can extract linguistic cues which can then be used to expand and reformulate queries to improve the effectiveness of drug interaction search. Our approach integrates many learning paradigms: partially supervised classification, association measures for collocation mining, and feature selection in supervised learning. We demonstrate compelling results on using positive examples from the DrugBank database to seed MEDLINE searches for drug interactions. In particular, we show that purely data-driven linguistic cues can be effectively mined and applied to realize a successful domain-specific query expansion framework.
On-line index maintenance using horizontal partitioning
Authors: Sairam Gurajada, P. Sreenivasa Kumar
Venue: CIKM
Year: 2009

In this paper, we propose a new merge-based index maintenance strategy for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types of sub-indexes. Inverted lists of the terms contained in the queries that are frequently posed to the Information Retrieval systems are kept in one partition, called frequent-term index and the other inverted lists form another partition, called infrequent-term index. We use a lazy-merge strategy for maintaining infrequent-term sub-indexes, and an active merge strategy for maintaining frequent-term sub-indexes. The sub-indexes are also similarly split into frequent and in-frequent parts. Experimental results show that the proposed method improves both index maintenance performance and query performance compared to the existing merge-based strategies.