OpenRaiser/Intern-Atlas huggingface dataset:
ROSE: retail outlet site evaluation by learning with both sample and feature preference
Authors: Bin Zhang, Ming Xie, Jin Yan Shao, Wen Jun Yin, Jin Dong
Venue: CIKM
Year: 2009

It is critical for retail enterprises to select good sites or locations to open their stores, especially in current competitive retail market. However, evaluating the goodness of sites in real business applications is a complex problem. That is, how to judge whether the market around a store site is good? We don't know the exact mechanism of how a site can be good and it is hard to have correct site goodness values as supervised labels. The Retail Outlet Site Evaluation (ROSE) tool is designed to learn the site evaluation model by integrating city geographic & demographic data and two kinds of expert knowledge: sample preference and feature preference. The feature preference information can help greatly reduce the required number of sample preferences. It enables our application practicable because it is almost impossible to give such amount of sample preference pairs manually by experts when ranking hundreds of data points. In the experiment and case study part, we show that the ROSE tool can achieve good results and useful for users to do site evaluation work in real cases.
sDoc: exploring social wisdom for document enhancement in web mining
Authors: Xiaoxun Zhang, Lichun Yang, Xian Wu, Honglei Guo, Zhili Guo, Shenghua Bao, Yong Yu, Zhong Su
Venue: CIKM
Year: 2009

Web document could be seen to be composed of textual content as well as social metadata of various forms (e.g., anchor text, search query and social annotation), both of which are valuable to indicate the semantic content of the document. However, due to the free nature of the web, the two streams of web data suffer from the serious problems of noise and sparseness, which have actually become the major challenges to the success of many web mining applications. Previous work has shown that it could enhance the content of web document by integrating anchor text and search query. In this paper, we study the problem of exploring emergent social annotation for document enhancement and propose a novel reinforcement framework to generate "social representation" of document. Distinguishing from prior work, textual content and social annotation are enhanced simultaneously in our framework, which is achieved by exploiting a kind of mutual reinforcement relationship behind them. Two convergent models, social content model and social annotation model, are symmetrically derived from the framework to represent enhanced textual content and enhanced social annotation respectively. The enhanced document is referred to as Social Document or sDoc in that it could embed complementary viewpoints from many web authors and many web visitors. In this sense, the document semantics is enhanced exactly by exploring social wisdom. We build the framework on a large Del.icio.us data and evaluate it through three typical web mining applications: annotation, classification and retrieval. Experimental results demonstrate that social representation of web document could boost the performance of these applications significantly.
A method of workload compression basing on characteristics for index selection
Authors: Xin Zhang, Zhihao Zhang, Jinlong Lin
Venue: CIKM
Year: 2009

This paper presents a novel method of database workload compression basing on the queries' characteristics for purpose of solving Index Selection Problem (ISP). The algorithm applies a close analysis on the queries in workload to capture their characteristics, which are used to build the relationships between pairs of queries. Using these relationships, the original workload can be filtered out into the compressed workload, which can be used for the task without degrading the performance. This method has been implemented and the experiments on workloads illustrate that proposed solution can achieve a high performance with a little quality loss.
A tag recommendation system for folksonomy
Authors: Ning Zhang, Yuan Zhang, Jie Tang
Venue: CIKM
Year: 2009

Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for tag recommendation systems, but those traditional collaborative systems may not perform well in the case of a new user and a new resource.
Graph-based seed selection for web-scale crawlers
Authors: Shuyi Zheng, Pavel A. Dmitriev, C. Lee Giles
Venue: CIKM
Year: 2009

One of the most important steps in web crawling is determining the starting points, or seed selection. This paper identifies and explores the problem of seed selection in web-scale incremental crawlers. We argue that seed selection is not a trivial but very important problem. Selecting proper seeds can increase the number of pages a crawler will discover, and can result in a repository with more "good" and less "bad" pages. We propose a graph-based framework for crawler seed selection, and present several algorithms within this framework. Evaluation on real web data showed significant improvements over heuristic seed selection approaches.
Efficient record-level wrapper induction
Authors: Shuyi Zheng, Ruihua Song, Ji-Rong Wen, C. Lee Giles
Venue: CIKM
Year: 2009

Web information is often presented in the form of record, e.g., a product record on a shopping website or a personal profile on a social utility website. Given a host webpage and related information needs, how to identify relevant records as well as their internal semantic structures is critical to many online information systems. Wrapper induction is one of the most effective methods for such tasks. However, most traditional wrapper techniques have issues dealing with web records since they are designed to extract information from a page, not a record. We propose a record-level wrapper system. In our system, we use a novel ``broom'' structure to represent both records and generated wrappers. With such representation, our system is able to effectively extract records and identify their internal semantics at the same time. We test our system on 16 real-life websites from four different domains. Experimental results demonstrate 99\% extraction accuracy in terms of F1-Value.
3se: a semi-structured search engine for heterogeneous data in graph model
Authors: Ming Zhong, Mengchi Liu
Venue: CIKM
Year: 2009

As the ubiquitous interplay of structured, semi-structured and unstructured data from different sources, neither DB-style structured query requiring knowledge of full schema and complex language, nor IR-style keyword search ignoring latent structures, can satisfy users. In this paper, we present a novel Semi-Structured Search Engine (3SE) that provides easy, flexible, precise and rapid access to heterogeneous data represented by a semi-structured graph model.
Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems
Authors: Tom Chao Zhou, Irwin King
Venue: CIKM
Year: 2009

Social tagging systems have recently emerged as an effective way for users to annotate and organize large collections of resources on the Web. Moreover, they also facilitate an efficient sharing of vast amounts of resources among different users. In this paper, we analyze tags' usage pattern in real world data sets and find that among tags representing the same concept, some tags are less popular, resulting in reduced exploring effectiveness in the current social tagging systems. Another limitation is that users cannot roll-up or drill-down the concept hierarchy of tag queries, resulting in the limited scope of service and a failure to meet users' dynamic information needs which often change with the current information provided. In order to overcome these shortcomings, we propose a novel three-phase approach as the following: (1) finding semantically-related tags for tag query; (2) constructing clusters of tags representing the same concept; and (3) building hierarchical relationships among clusters of tags. Based on our approaches, horizontal and hierarchical exploration can be implemented. Experiments employing real world dataset show encouraging results and confirm that the proposed approaches are very effective.
To divide and conquer search ranking by learning query difficulty
Authors: Zeyuan Allen Zhu, Weizhu Chen, Tao Wan, Chenguang Zhu, Gang Wang, Zheng Chen
Venue: CIKM
Year: 2009

Learning to rank plays an important role in information retrieval. In most of the existing solutions for learning to rank, all the queries with their returned search results are learnt and ranked with a single model. In this paper, we demonstrate that it is highly beneficial to divide queries into multiple groups and conquer search ranking based on query difficulty. To this end, we propose a method which first characterizes a query using a variety of features extracted from user search behavior, such as the click entropy, the query reformulation probability. Next, a classification model is built on these extracted features to assign a score to represent how difficult a query is. Based on this score, our method automatically divides queries into groups, and trains a specific ranking model for each group to conquer search ranking. Experimental results on RankSVM and RankNet with a large-scale evaluation dataset show that the proposed method can achieve significant improvement in the task of web search ranking.
A general magnitude-preserving boosting algorithm for search ranking
Authors: Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang Wang, Dong Wang, Zheng Chen
Venue: CIKM
Year: 2009

Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost. However, such a pairwise approach ignores the information about the magnitude of preference in the learning process. In this paper, we present the directed distance function (DDF) as a substitute for binary labels in pairwise approach to preserve the magnitude of preference and propose a new boosting algorithm called MPBoost, which applies GentleBoost optimization and directly incorporates DDF into the exponential loss function. We give the boundedness property of MPBoost through theoretic analysis. Experimental results demonstrate that MPBoost not only leads to better NDCG accuracy as compared to state-of-the-art ranking solutions in both public and commercial datasets, but also has good properties of avoiding the overfitting problem in the task of learning ranking functions.
Multidimensional political spectrum identification and analysis
Authors: Leilei Zhu, Prasenjit Mitra
Venue: CIKM
Year: 2009

In this work, we show the importance of multidimensional opinion representation in the political context combining domain knowledge and results from principal component analysis. We discuss the differences of feature selection between political spectrum analysis and normal opinion mining tasks. We build regression models on each opinion dimension for scoring and placing new opinion entities, e.g. personal blogs or politicians, onto the political opinion spectrum. We apply our methods on the floor statement records of the United States Senate and evaluate it against the uni-dimensional representation of political opinion space. The experimental results show the effectiveness of the proposed model in explaining the voting records of the Senate.
Frequent subgraph pattern mining on uncertain graph data
Authors: Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang
Venue: CIKM
Year: 2009

Graph data are subject to uncertainties in many applications due to incompleteness and imprecision of data. Mining uncertain graph data is semantically different from and computationally more challenging than mining exact graph data. This paper investigates the problem of mining frequent subgraph patterns from uncertain graph data. The frequent subgraph pattern mining problem is formalized by designing a new measure called expected support. An approximate mining algorithm is proposed to find an approximate set of frequent subgraph patterns by allowing an error tolerance on the expected supports of the discovered subgraph patterns. The algorithm uses an efficient approximation algorithm to determine whether a subgraph pattern can be output or not. The analytical and experimental results show that the algorithm is very efficient, accurate and scalable for large uncertain graph databases.
A secure pervasive health care system using location dependent unicast key generation scheme
Authors: Debargh Acharya, Vijay Kumar
Venue: CIKM
Year: 2010

Remote health monitoring has tremendous potential to improve quality of health care services in modern and ubiquitous medical environments. It helps to cut the cost in modern healthcare by avoiding unnecessary hospital visits for frequent checkups. In this context, security and protection of sensitive medical data such as Electronic Health Records (EHR), data integrity and protection of patient's privacy to be monitored are important aspects in order to increase user's acceptance of these new technologies. Secure communication protects data from unauthorized users and usually requires pair-wise keys. In all existing schemes these keys are generated and distributed to nodes wishing to communicate. The key generation phase is usually well-secured but the key distribution is not, as a result, they are vulnerable to security threats. This paper presents an overview of security threats in pervasive healthcare and presents a secure communication scheme which unlike other schemes does not store a key chain in the memory from a universal key space and eliminate key broadcast. Simulation and results indicate that our scheme outperforms the existing schemes by decreasing transmission and storage cost.
Using the past to score the present: extending term weighting models through revision history analysis
Authors: Ablimit Aji, Yu Wang, Eugene Agichtein, Evgeniy Gabrilovich
Venue: CIKM
Year: 2010

The generative process underlies many information retrieval models, notably statistical language models. Yet these models only examine one (current) version of the document, effectively ignoring the actual document generation process. We posit that a considerable amount of information is encoded in the document authoring process, and this information is complementary to the word occurrence statistics upon which most modern retrieval models are based. We propose a new term weighting model, Revision History Analysis (RHA), which uses the revision history of a document (e.g., the edit history of a page in Wikipedia) to redefine term frequency - a key indicator of document topic/relevance for many retrieval models and text processing tasks. We then apply RHA to document ranking by extending two state-of-the-art text retrieval models, namely, BM25 and the generative statistical language model (LM). To the best of our knowledge, our paper is the first attempt to directly incorporate document authoring history into retrieval models. Empirical results show that RHA provides consistent improvements for state-of-the-art retrieval models, using standard retrieval tasks and benchmarks.
Alignment of short length parallel corpora with an application to web search
Authors: Jitendra Ajmera, Hema Swetha Koppula, Krishna P. Leela, Shibnath Mukherjee, Mehul Parsana
Venue: CIKM
Year: 2010

With evolving Web, short length parallel corpora is becoming very common and some of these include user queries, web snippets etc. This paper concerns situations where short length parallel corpora has to be analyzed in order to find meaningful unit-alignment. This is similar to dealing with parallel corpora where a sentence level alignment of translations is required, but differs in that the alignment is to be inferred at unit (word or phrase) level. A Conditional Random Field (CRF) based approach is proposed to discover this unit alignment. Given pairs of semantically or syntactically similar entities, the problem is formulated as that of mutual segmentation and sequence alignment problem. The mutual segmentation refers to the process of segmenting the first entity based on units (or labels) in the second entity and vice-versa. The process of optimizing this mutual segmentation also results in optimal unit alignment. Since our training data is not segmented and unit-aligned, we modify the CRF objective function to accommodate unsupervised data and iterative learning. We have applied this framework to Web Search domain and specifically for query reformulation task. Finally, our experiments suggest that the proposed approach indeed results in meaningful alternatives of the original query.
Selected new training documents to update user profile
Authors: Abdulmohsen Algarni, Yuefeng Li, Yue Xu
Venue: CIKM
Year: 2010

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.
BP-tree: an efficient index for similarity search in high-dimensional metric spaces
Authors: Jurandy Almeida, Ricardo da Silva Torres, Neucimar Jerônimo Leite
Venue: CIKM
Year: 2010

Similarity search in high-dimensional metric spaces is a key operation in many applications, such as multimedia databases, image retrieval, object recognition, and others. The high dimensionality of the data requires special index structures to facilitate the search. Most of existing indexes are constructed by partitioning the data set using distance-based criteria. However, those methods either produce disjoint partitions, but ignore the distribution properties of the data; or produce non-disjoint groups, which greatly affect the search performance. In this paper, we study the performance of a new index structure, called Ball-and-Plane tree (BP-tree), which overcomes the above disadvantages. BP-tree is constructed by recursively dividing the data set into compact clusters. Distinctive from other techniques, it integrates the advantages of both disjoint and non-disjoint paradigms in order to achieve a structure of tight and low overlapping clusters, yielding significantly improved performance. Results obtained from an extensive experimental evaluation with real-world data sets show that BP-tree consistently outperforms state-of-the-art solutions.
Annotations: dynamic semantics in stream processing
Authors: Juan Amiguet-Vercher, Andreas Wombacher, Tim E. Klifman
Venue: CIKM
Year: 2010

In the field of e-science stream data processing is common place facilitating sensor networks, in particular for prediction and supporting decision making. However, sensor data may be erroneous, like e.g. due to measurement errors (outliers) or changes of the environment. While it can be foreseen that there will be outliers, there are a lot of environmental changes which are not foreseen by scientists and therefore are not considered in the data processing. However, these unforeseen semantic changes - represented as annotations - have to be propagated through the processing. Since the annotations represent an unforeseen, hence un-understandable, annotation, the propagation has to be independent of the annotation semantics. It nevertheless has to preserve the significance of the annotation on the data despite structural and temporal transformations. And should remain meaningful for a user at the end of the data processing. In this paper, we identify the relevant research questions.In particular, the propagation of annotations is based on structural, temporal, and significance contribution. While the consumption of the annotation by the user is focusing on clustering information to ease accessibility.
Cross language information retrieval based on concept base and language grid
Authors: Pham Huy Anh, Takashi Yukawa
Venue: CIKM
Year: 2010

This paper describes query translation using multiple language resources and concept base method for the Cross Language Information Retrieval (CLIR). In the proposed method, the queries are translated by multiple machine translation systems on the Language Grid. Then the queries are expanded by translated compound words, noun phrases with the bilingual dictionary. At the same time, document related to the translated query are retrieved with a TF-IDF term weighting model. Then, top 100 retrieved documents are re-ranked by a specificity-considered concept base with noun phrases and compound words extracted from the query. Re-ranked results are combined with the results retrieved by the Probabilistic model. For the evaluation of the proposed method, we use the average precision of the interpolated recall and precision at to compare with the NTCIR1 participation systems. The proposed system achieved the highest precision compared with the NTCIR1 participating systems.
Robust prediction from multiple heterogeneous data sources with partial information
Authors: Mohammad S. Aziz, Chandan K. Reddy
Venue: CIKM
Year: 2010

Significant research efforts for robust integration of information from multiple sources are being pursued at a rapid pace. However, the information in heterogeneous sources is often incomplete and hence making the maximum use of all the available information is a challenging problem. Most of the recent research on data integration have been primarily focused on the cases where the information is available across all the different sources and can not effectively integrate sources in the presence of partial information. We develop an ensemble method that boosts the decisions made from different models on individual sources and obtain robust results for the task of class prediction. We propose a heterogeneous boosting framework that uses all the available information even if some of the sources do not provide any information about some objects. We demonstrate the effectiveness of the proposed framework for the problem of gene function prediction and compare to the state-of-the-art methods using several real-world biological datasets. We also show that the proposed method outperforms any kind of imputation schemes that are widely used while integrating data with partial information
SQR: a semantic query rating scheme
Authors: Hany Azzam, Thomas Roelleke
Venue: CIKM
Year: 2010

We introduce a query rating scheme that identifies the possible interpretations which can be assigned to a semantic query. The interpretations range from the traditional bag-of-words interpretation to more context- and semantic-aware interpretations. The aims of this scheme are to communicate the extent of semantics that is being interpreted for a query and to assign suitable query processing methods for each level of interpretation accordingly.