OpenRaiser/Intern-Atlas huggingface dataset:
LITSEEK: public health literature search by metadata enhancement with external knowledge bases
Authors: Priyanka Sharad Prabhu, Shamkant B. Navathe, Stephen Tyler, Venu Dasigi, Neha Narkhede, Balaji Palanisamy
Venue: CIKM
Year: 2009

Biomedical literature is an important source of information in any researcher's investigation of genes, risk factors, diseases and drugs. Often the information searched by public health researchers is distributed across multiple disparate sources that may include publications from PubMed, genomic, proteomic and pathway databases, gene expression and clinical resources and biomedical ontologies. The unstructured nature of this information makes it difficult to find relevant parts from it manually and comprehensive knowledge is further difficult to synthesize automatically. In this paper we report on LITSEEK (LITerature Search by metadata Enhancement with External Knowledgebases), a system we have developed for the benefit of researchers at the Centers for Disease Control (CDC) to enable them to search the HuGE (Human Genome for Epidemiology) database of PubMed articles, from a pharmacogenomic perspective. Besides analyzing text using TFIDF ranking and indexing of the important terms, the proposed system incorporates an automatic consultation with PharmGKB - a human-curated knowledge base about drugs, related diseases and genes, as well as with the Gene Ontology, a human-curated, well accepted ontology. We highlight the main components of our approach and illustrate how the search is enhanced by incorporating additional concepts in terms of genes/drugs/diseases (called metadata for ease of reference) from PharmGKB. Various measurements are reported with respect to the addition of these metadata terms. Preliminary results in terms of precision based on expert user feedback from CDC are encouraging. Further evaluation of the search procedure by actual researchers is under way.
Pseudo relevance feedback using semantic clustering in relevance language model
Authors: Qiang Pu, Daqing He
Venue: CIKM
Year: 2009

Pseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics. By viewing a document as an interaction of a set of independent hidden topics, we propose a novel semantic clustering technique using independent component analysis. Then within the language modeling framework, we apply the obtained semantic topic clusters into the query sampling process so that the sampling depends on the activated topics rather than on the individual document language model. Therefore, we obtain a semantic cluster based relevance language model, which uses pseudo relevance feedback technique without requiring any relevance training information. We applied the model on five TREC data sets. The experiments show that our model can significantly improve retrieval performance over traditional language models including relevance-based and clustering-based retrieval language models. The main contribution of the improvements comes from the estimation of the relevance model on the semantic clusters that are closely related to the query.
What's behind topic formation and development: a perspective of community core groups
Authors: Tieyun Qian, Qing Li, Bing Liu, Hui Xiong, Jaideep Srivastava, Phillip C.-Y. Sheu
Venue: CIKM
Year: 2009

Over the past several years, there has been a great interest in topic detection and tracking (TDT). Recently, analyzing general research trend from the huge amount of history documents also arouses considerable attention. However, existing work on TDT mainly focuses on overall trend analysis, and is unable to address questions such as "what determines the evolution of a topic?" and "when and how does a new topic get formed?".
Efficient multi-class unlabeled constrained semi-supervised SVM
Authors: Mingjie Qian, Feiping Nie, Changshui Zhang
Venue: CIKM
Year: 2009

Semi-supervised learning has been successfully applied to many fields such as knowledge management, information retrieval and data mining as it can utilize both labeled and unlabeled data. In this paper, we propose a general semi-supervised framework for multi-class categorization. Many classical supervised and semi-supervised method dealing with binary classification or multi-class classification including the standard regularization and the manifold regularization can be viewed as special cases of this framework. Based on this framework, we propose a novel method called multi-class unlabeled constrained SVM(MCUCSVM) and its special case: multi-class Laplacian SVM(MCLapSVM). We then put forward a general kernel version semi-supervised dual coordinate descent algorithm to efficiently solve MCUCSVM and makes it more applicable to problems with large number of classes and large scale labeled data. Both rigorous theory and promising experimental results on four real datasets show the great performance and remarkable efficiency of MCUCSVM and MCLapSVM.
SELC: a self-supervised model for sentiment classification
Authors: Likun Qiu, Weishi Zhang, Changjian Hu, Kai Zhao
Venue: CIKM
Year: 2009

This paper presents the SELC Model (SElf-Supervised, (Lexicon-based and (Corpus-based Model) for sentiment classification. The SELC Model includes two phases. The first phase is a lexicon-based iterative process. In this phase, some reviews are initially classified based on a sentiment dictionary. Then more reviews are classified through an iterative process with a negative/positive ratio control. In the second phase, a supervised classifier is learned by taking some reviews classified in the first phase as training data. Then the supervised classifier applies on other reviews to revise the results produced in the first phase. Experiments show the effectiveness of the proposed model. SELC totally achieves 6.63% F1-score improvement over the best result in previous studies on the same data (from 82.72% to 89.35%). The first phase of the SELC Model independently achieves 5.90% improvement (from 82.72% to 88.62%). Moreover, the standard deviation of F1-scores is reduced, which shows that the SELC Model could be more suitable for domain-independent sentiment classification.
Finding the topical anchors of a context using lexical cooccurrence data
Authors: Aditya Ramana Rachakonda, Srinath Srinivasa
Venue: CIKM
Year: 2009

Lexical cooccurrence in textual data is not uniformly random. The statistics inferred from the term-cooccurrence data enable us to model dependencies between terms as graphs, somewhat resembling the way semantic memory is organised in human beings. In this paper we look at cooccurrence patterns to identify topical anchors of a given context. Topical anchors are those terms whose semantics represent the topic of the whole context. This work is based on computing a stationary distribution in the cooccurrence graph. Topical anchors were computed on a set of 100 contexts and were also evaluated by 86 volunteers and the results show that the algorithm correctly identifies the topical anchors around 62% of the time.
Information extraction meets relation databases
Authors: Davood Rafiei, Andrei Z. Broder, Edward Y. Chang, Patrick Pantel
Venue: CIKM
Year: 2009

Information extraction from unstructured text has much in common with querying in databases systems. Despite some differences on how data is modeled or represented, the general goal remains the same, i.e. to retrieve data or tag elements that satisfy some user-specified constraints. In recent years, the two paradigms have become much closer thanks to the large volume of data on the World Wide Web and the need for more automated search tools for information extraction and often the need for relating the extracted pieces. Several developments have contributed to the growth of the area including the work on named entity recognition (marked by MUC-6 and subsequent conferences) and natural language processing, Web information retrieval and mining, and Web query languages inspired by the query languages in the relational world. This panel explores the areas where the two paradigms overlap, the impacts and contributions they have had on each other and the areas that may be open for further research. The panel will bring together researchers who have worked in some established areas that closely relate to extracting structured information from unstructured text. In the first (role-playing) round, each panelist will strongly take a side on where the intersection is heading, arguing that one area will subsume the other area in near future. In the second round, the panelists will counter one or two others, pointing out the challenges that one area would be facing in subsuming the other and implications for future research directions.
Socializing or knowledge sharing?: characterizing social intent in community question answering
Authors: Eduarda Mendes Rodrigues, Natasa Milic-Frayling
Venue: CIKM
Year: 2009

Knowledge sharing communities, such as Wikipedia or Yahoo! Answers, add greatly to the wealth of information available on the Web. They represent complex social ecosystems that rely on user paricipation and the quality of users' contributions to prosper. However, quality is harder to achieve when knowledge sharing is facilitated through a high degree of personal interactions. The individuals' objectives may change from knowledge sharing to socializing, with a profound impact on the community and the value it delivers to the broader population of Web users. In this paper we provide new insights into the types of content that is shared through Community Question Answering (CQA) services. We demonstrate an approach that combines in-depth content analysis with social network analysis techniques. We adapted the Undirected Inductive Coding method to analyze samples of user questions and arrive at a comprehensive typology of the user intent. In our analysis we focused on two types of intent, social vs. non-social, and define measures of social engagement to characterize the users' participation and content contributions. Our approach is applicable to a broad class of online communities and can be used to monitor the dynamics of community ecosystems.
Efficient and reliable merging of XML documents
Authors: Sebastian Rönnau, Geraint Philipp, Uwe M. Borghoff
Venue: CIKM
Year: 2009

Many knowledge-based processes rely on XML-based office documents. Up to now, versioning and merging XML documents was a difficult and error-prone task, mostly done manually. The support by tools is still in its infancy. We have presented a novel approach to compare and merge XML documents in a reliable way using context fingerprints. In this demonstration, we show the application of our toolset to ODF documents using a new, simple, and user-friendly graphical user-interface.
Terminology mining in social media
Authors: Magnus Sahlgren, Jussi Karlgren
Venue: CIKM
Year: 2009

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining.
Potential collaboration discovery using document clustering and community structure detection
Authors: Cristian Klen dos Santos, Alexandre G. Evsukoff, Beatriz Souza Leite Pires de Lima, Nelson F. F. Ebecken
Venue: CIKM
Year: 2009

Complex network analysis is a growing research area in a wide variety of domains and has recently become closely associated with data, text and web mining. One of the most active areas in the study of complex networks is the detection of community structure, which can be related to the clustering problem in data mining. This paper employs a community structure detection algorithm for document clustering in order to discover potential relationships in a social network. The proposed approach is explored in a case study of potential collaboration discovery among the research staff in the Graduate Civil Engineering Department of the Federal University of Rio de Janeiro, Brazil. The results show that the combined use of both techniques provides useful insights on the relationships, both existent and potential, among individuals in the social network.
Characteristics of document similarity measures for compliance analysis
Authors: Asad B. Sayeed, Soumitra Sarkar, Yu Deng, Rafah Hosn, Ruchi Mahindru, Nithya Rajamani
Venue: CIKM
Year: 2009

Due to increased competition in the IT Services business, improving quality, reducing costs and shortening schedules has become extremely important. A key strategy being adopted for achieving these goals is the use of an asset-based approach to service delivery, where standard reusable components developed by domain experts are minimally modified for each customer instead of creating custom solutions. One example of this approach is the use of contract templates, one for each type of service offered. A compliance checking system that measures how well actual contracts adhere to standard templates is critical for ensuring the success of such an approach. This paper describes the use of document similarity measures - Cosine similarity and Latent Semantic Indexing - to identify the top candidate templates on which a more detailed (and expensive) compliance analysis can be performed. Comparison of results of using the different methods are presented.
A mobile tourist assistance and recommendation system based on complex networks
Authors: Alf-Christian Schering, Martin Düffer, Andreas Finger, Ilvio Bruder
Venue: CIKM
Year: 2009

In recent years, the tourism assistance community is gradually shifting its emphasis to digital, interactive systems. Our project aims for digital tourism assistance by combining mobile guiding and route recommendation based on community networks. We use complex networks to tackle problems, such as deriving optimal routes in conjunction with knowledge deduced from our social tourist network, and giving a respectable prediction of which meta- and multimedia data about touristic objects are needed while the tourist is on tour. This article describes ongoing work on the recommendation system and mobile semantic replication strategies.
DrugNerAR: linguistic rule-based anaphora resolver for drug-drug interaction extraction in pharmacological documents
Authors: Isabel Segura-Bedmar, Mario Crespo, César de Pablo-Sánchez, Paloma Martínez
Venue: CIKM
Year: 2009

DrugNerAR, a drug anaphora resolution system is presented to address the problem of co-referring expressions in pharmacological literature. This development is part of a larger and innovative study about automatic drug-drug interaction extraction. Besides, a corpus has been developed in order to analyze the phenomena and evaluate the current approach. The system uses a set of linguistic rules inspired by Centering Theory over the analysis provided by a biomedical syntactic parser. Semantic information provided by Unified Medical Language System (UMLS) is also integrated in order to improve the recognition and the resolution of nominal drug anaphors. This linguistic rule-based approach shows very promising results for the challenge of accounting for anaphoric expressions in pharmacological texts.
Incremental privacy preservation for associative classification
Authors: Bowonsak Seisungsittisunti, Juggapong Natwichai
Venue: CIKM
Year: 2009

Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known data privacy model, i.e. k-Anonymity. Meanwhile the data mining task to be applied to the given dataset is associative classification. As the problem of privacy preserving for data mining has proven as an NP-hard, we propose to study the characteristics of a proven heuristic algorithm in the incremental scenarios theoretically. Subsequently, we propose a few observations which lead to the techniques to reduce the computational complexity for the problem setting in which the outputs remains the same. In addition, we propose a simple algorithm, which is at most as efficient as the polynomial-time heuristic algorithm in the worst case, for the problem.
Product query classification
Authors: Dou Shen, Ying Li, Xiao Li, Dengyong Zhou
Venue: CIKM
Year: 2009

Web query classification is an effective way to understand Web user intents, which can further improve Web search and online advertising relevance. However, Web queries are usually very short which cannot fully reflect their meanings. What is more, it is quite hard to obtain enough training data for training accurate classifiers. Therefore, previous work on query classification has focused on two issues. One is how to represent Web queries through query expansion. The other is how to increase the amount of training data. In this paper, we took product query classification as an example, which is to classify Web queries into a predefined product taxonomy, and systematically studied the impact of query expansion and the size of training data. We proposed two methods of enriching Web queries and three approaches of collecting training data. Thereafter, we conducted a series of experiments to compare the classification performance of using different combinations of training data and query representations over a real data set. The data set consists of hundreds of thousands queries collected from a popular commercial search engine. From the experiments, we found some interesting observations, which were not discussed before. Finally, we proposed an effective and efficient product query classification method based on our observations.
Exploiting term relationship to boost text classification
Authors: Dou Shen, Jianmin Wu, Bin Cao, Jian-Tao Sun, Qiang Yang, Zheng Chen, Ying Li
Venue: CIKM
Year: 2009

Document classification provides an effective way to handle the explosive online textual data. However, in practical classification settings, we face the so-called feature sparsity problem caused by a lack of training documents or the shortness of text to be classified. In this paper, we solve the sparsity problem by exploiting term relationships along with Naive Bayes classifiers. The first method is to estimate term relationships based on the co-occurrence information of two terms in a certain context. The second method estimates the term relationships based on the distribution of terms over different hierarchical categories in a publicly available document taxonomy. Thereafter, term relationship is used to augment Naive Bayes classifiers. We test our methods on two open-domain data sets to demonstrate its advantages. The experimental results show that our method can significantly improve the classification performance, especially when we do not have enough training data or the texts are Web search queries.
Adaptive web mining of bilingual lexicons for cross language information retrieval
Authors: Lei Shi
Venue: CIKM
Year: 2009

Bilingual web pages contain abundant term translation knowledge which is crucial for query translation in Cross Language Information Retrieval systems. But it is a challenging task to extract term translations from bilingual web pages due to the variation in web page layouts and writing styles. In this paper, based on the observation that translation pairs on the same web page tend to appear following similar patterns, a new extraction model is proposed to adaptively learn extraction patterns and exploit them to facilitate term translation mining from bilingual web pages. Experiments reflect that this model can significantly improve extraction coverage while maintaining high accuracy. It improves query translation in cross-language information retrieval, leading to significantly higher retrieval effectiveness on TREC collections.
Nonlinear static-rank computation
Authors: Shuming Shi, Bin Lu, Yunxiao Ma, Ji-Rong Wen
Venue: CIKM
Year: 2009

Mainstream link-based static-rank algorithms (e.g. PageRank and its variants) express the importance of a page as the linear combination of its in-links and compute page importance scores by solving a linear system in an iterative way. Such linear algorithms, however, may give apparently unreasonable static-rank results for some link structures. In this paper, we examine the static-rank computation problem from the viewpoint of evidence combination and build a probabilistic model for it. Based on the model, we argue that a nonlinear formula should be adopted, due to the correlation or dependence between links. We focus on examining some simple formulas which only consider the correlation between links in the same domain. Experiments conducted on 100 million web pages (with multiple static-rank quality evaluation metrics) show that higher quality static-rank could be yielded by the new nonlinear algorithms. The convergence of the new algorithms is also proved in this paper by nonlinear functional analysis.
Msuggest: a semantic recommender framework for traditional chinese medicine book search engine
Authors: Shaomin Shi, Baogang Wei, Yang Yan
Venue: CIKM
Year: 2009

Learning traditional Chinese medicine knowledge from the digital library is becoming more and more important these days in China. In medicine learning, many readers want to find out the intrinsic relation between two medicines or among thousands of medicines. A semantic recommender system is useful for readers to understand something quickly by means of analogy which is a cognitive process of transferring information from a particular subject to another if they are similar in some aspects. In view of these above, we present a novel recommender framework called Msuggest to give the diverse semantic recommended medicine terminologies and book pages when a reader searching for medicine information in digital library. Users can choose various aspects including medicine property, efficacy, clinical application, place of origin, book provenance and etc. to see different recommended results. We evaluate Msuggest under the t-test on the samples from random sampling. The result shows that Msuggest is effective and efficient in giving the recommended words and book pages.
A novel distributed P2P simulator architecture: D-P2P-sim
Authors: Spyros Sioutas, George Papaloukopoulos, Evangelos Sakkopoulos, Kostas Tsichlas, Yannis Manolopoulos
Venue: CIKM
Year: 2009

In this paper we introduce a novel distributed simulation environment with GUI for P2P simulations (D-P2P-Sim). The key aim is to provide the appropriate integrated set of tools in a single software solution to evaluate the performance of various protocols. The basic architecture of the distributed P2P simulator is based on a multi-threading, asynchronous, message passing and distributed environment with graphical user interface to facilitate ease of use by both researchers and programmers.