OpenRaiser/Intern-Atlas huggingface dataset:
WIDM 2012: the 12th international workshop on web information and data management
Authors: George H. L. Fletcher, Prasenjit Mitra
Venue: CIKM
Year: 2012

We give an overview of WIDM 2012, held in conjunction with CIKM 2012 in Maui, Hawaii. WIDM 2012 is the twelfth in a series of international workshops on Web Information and Data Management held in conjunction with CIKM since 1998. The objective of the workshop is to bring together researchers and industrial practitioners to present and discuss leading research into how web data and information can be extracted, stored, analyzed, and processed to provide useful knowledge to end users for advanced database and web applications.
Is that scene dangerous?: transferring knowledge over a video stream
Authors: Omar U. Florez, Curtis E. Dyreson
Venue: CIKM
Year: 2012

Activity mining in traffic scenes aims to automatically explain the complex interactions among moving objects recorded with a surveillance camera. Traditional machine learning algorithms generate a model and validate it with manually labeled data, which is a time-consuming and expensive task. The common issue is that these models often get outdated when external variables take place during posterior recording such as dynamic background, illumination, and different weather conditions. Those changes practically impose a new domain that often makes the original model inaccurate for clustering and classification tasks. If we directly apply a statistical model trained in one domain to other over the same stream, the performance of the algorithm will notably decrease due to distinct activity representations and different marginal and conditional distributions.
A hybrid approach to finding negated and uncertain expressions in biomedical documents
Authors: Kazuki Fujikawa, Kazuhiro Seki, Kuniaki Uehara
Venue: CIKM
Year: 2012

More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.
Semantics of query rewriting patterns in search logs
Authors: Sumio Fujita, Georges Dupret, Ricardo Baeza-Yates
Venue: CIKM
Year: 2012

Examining past query reformulations of users in the logs of a search engine shows that not only query rewriting that precises the focus of a query but also queries expressing some topical shift from the original query can help users in their search activities. We examine three methods to explore query rewriting patterns, namely co-click based, co-topic based and co-session based methods. Each method has its own semantic characteristic: the co-click based and the co-topic based methods typically exploit specialization patterns with different granularity whereas the co-session based method is able to extract topic shift patterns. We show the different semantic relations of extracted query pairs by using large scale web annotations for Japanese web pages.
Text classification with relatively small positive documents and unlabeled data
Authors: Fumiyo Fukumoto, Takeshi Yamamoto, Suguru Matsuyoshi, Yoshimi Suzuki
Venue: CIKM
Year: 2012

This paper addresses the problem of dealing with a collection of negative training documents which is suitable for relatively small number of positive documents, and presents a method for eliminating the need for manually collecting negative training documents based on supervised machine learning techniques. We applied an error correction technique to the results of negative training data obtained by the Positive Example Based Learning (PEBL). Moreover, we used a boosting technique to learn a set of negative data to train classifiers. The results using Japanese newspaper documents showed that the method contributes for reducing the cost of manual collection of negative training documents.
PriSM: discovering and prioritizing severe technical issues from product discussion forums
Authors: Rashmi Gangadharaiah, Rose Catherine
Venue: CIKM
Year: 2012

Online forums provide a channel for users to report and discuss problems related to products and troubleshooting, for faster resolution. These could garner negative publicity if left unattended by the companies. Manually monitoring these massive amounts of discussions is laborious. This paper makes the first attempt at collecting issues that require immediate action by the product supplier by analyzing the immense information on forums. Features that are specific to forum discussions, in conjunction with linguistic cues help in capturing and better prioritizing issues. Any attempt to collect training data for learning a classifier for this task will require enormous labeling effort. Hence, this paper adopts a co-training approach, which uses minimal manual labeling, coupled with linguistic features extracted using a set-expansion algorithm to discover severe problems. Further, most distinct and recent issues are obtained by incorporating a measure of 'centrality', 'diversity' and temporal aspect of the forum threads. We show that this helps in better prioritizing longstanding issues and identify issues that need to be addressed immediately.
Information-complete and redundancy-free keyword search over large data graphs
Authors: Byron J. Gao, Zhumin Chen, Qi Kang
Venue: CIKM
Year: 2012

Keyword search over graphs has a wide array of applications in querying structured, semi-structured and unstructured data. Existing models typically use minimal trees or bounded subgraphs as query answers. While such models emphasize relevancy, they would suffer from incompleteness of information and redundancy among answers, making it difficult for users to effectively explore query answers. To overcome these drawbacks, we propose a novel cluster-based model, where query answers are relevancy-connected clusters. A cluster is a subgraph induced from a maximal set of relevancy-connected nodes. Such clusters are coherent and relevant, yet complete and redundancy free. They can be of arbitrary shape in contrast to the sphere-shaped bounded subgraphs in existing models. We also propose an efficient search algorithm and a corresponding graph index for large, disk-resident data graphs.
Twitter hyperlink recommendation with user-tweet-hyperlink three-way clustering
Authors: Dehong Gao, Renxian Zhang, Wenjie Li, Yuexian Hou
Venue: CIKM
Year: 2012

Twitter, the most famous micro-blogging service and online social network, collects millions of tweets every day. Due to the length limitation, users usually need to explore other ways to enrich the content of their tweets. Some studies have provided findings to suggest that users can benefit from added hyperlinks in tweets. In this paper, we focus on the hyperlinks in Twitter and propose a new application, called hyperlink recommendation in Twitter. We expect that the recommended hyperlinks can be used to enrich the information of user tweets. A three-way tensor is used to model the user-tweet-hyperlink collaborative relations. Two tensor-based clustering approaches, tensor decomposition-based clustering (TDC) and tensor approximation-based clustering (TAC) are developed to group the users, tweets and hyperlinks with similar interests, or similar contexts. Recommendation is then made based on the reconstructed tensor using cluster information. The evaluation results in terms of Mean Absolute Error (MAE) shows the advantages of both the TDC and TAC approaches over a baseline recommendation approach, i.e., memory-based collaborative filtering. Comparatively, the TAC approach achieves better performance than the TDC approach.
Querying external source code files of programs connecting to a relational database
Authors: Carlos Garcia-Alvarado, Carlos Ordonez, Veerabhadran Baladandayuthapani
Venue: CIKM
Year: 2012

Multiple source code files reference metadata of an existing database. Consequently, any modification that needs to be done in the source code files or in the schema of a database requires asserting the impact of performing such change. This problem of data and control dependencies between a database system and source code has been tackled before by software engineering. Unfortunately, these solutions are cumbersome to implement by requiring a long execution time for obtaining the dependency analysis, and do not allow a flexible analysis of the resulting data. In this research, we present and formalize a novel approach for using keyword search algorithms to analyze the resulting references found between the source code and the database's schema. Our initial findings show that our algorithms are efficient to perform a rapid integration phase, allow complex analysis of the references inside the DBMS (e.g. computation of OLAP data cubes), and provide efficient ranking capabilities.
Towards a more efficient and personalised advertisement content in on-line social networks
Authors: Patxi Galán-García, Carlos Laorden Gómez, Pablo García Bringas
Venue: CIKM
Year: 2012

Knowing what potential clients want, is the most important issue for companies. The current situation of social communication is generating a lot of information about users, such as favourite sites, food, politic tendencies, hopes or needs. This information has an incalculable value for marketing interests but, unfortunately, it is not trivial to process it. One way to obtain this information without disturbing the users is to store their searches, used words on the Internet and clicked items, to construct specific profiles. But, the problem with these techniques is that they do not retrieve current information about the targeted user because they gather information that may or may not be up to date. In light of this background, we propose a methodology to obtain up to date information about user's interests, likes and needs by analysing users conversations in social networks and instant messaging systems to generate personalised and interesting advertisement with better impact and higher success rates.
Modeling online collective emotions
Authors: David García, Frank Schweitzer
Venue: CIKM
Year: 2012

A common phenomenon on the Internet is the appearance of collective emotions, in which many users share an emotional state. Online communities allow users to emotionally interact with large amounts of other users, creating collective states faster than in offline interaction. We present our modeling framework for collective emotions in online communities. This framework allows the analysis and design of agent-based models, including the dynamics of psychological states under emotional interaction. We illustrate the applications of our framework through an overview of two different models. Based on this framework, our first model of emotions in product reviews communities reproduces the empirical distribution of emotions towards products in Amazon. The second model within our framework reproduces the emergence of emotional persistence at the individual and collective level. This persistence pattern is similar to the one revealed by our statistical analysis of IRC chatrooms. Further applications of our framework aim at reproducing collective features of emotions in a variety of online communities.
Information propagation in social rating networks
Authors: Priyanka Garg, Irwin King, Michael R. Lyu
Venue: CIKM
Year: 2012

The polarity of opinion is a crucial part of information and ignoring the asymmetry between them, can potentially result in an inaccurate estimation of the number of product adoptions and incorrect recommendations. We analyze the propagation patterns of the negative and positive opinions on two real world datasets, Flixster and Epinions, and observe that the presence of negative opinions significantly reduces the number of expressed opinions. To account for the asymmetry between the two kind of opinions, we propose extensions of the two most popular information propagation models, Independent Cascade and Linear Threshold models. The proposed extensions give a tractable influence problem and improves the prediction accuracy of future opinions, by more than 3% on Flixster and 5% on Epinions datasets.
Map to humans and reduce error: crowdsourcing for deduplication applied to digital libraries
Authors: Mihai Georgescu, Dang Duc Pham, Claudiu S. Firan, Wolfgang Nejdl, Julien Gaugaz
Venue: CIKM
Year: 2012

Detecting duplicate entities, usually by examining metadata, has been the focus of much recent work. Several methods try to identify duplicate entities, while focusing either on accuracy or on efficiency and speed - with still no perfect solution. We propose a combined layered approach for duplicate detection with the main advantage of using Crowdsourcing as a training and feedback mechanism. By using Active Learning techniques on human provided examples, we fine tune our algorithm toward better duplicate detection accuracy. We keep the training cost low by gathering training data on demand for borderline cases or for inconclusive assessments. We apply our simple and powerful methods to an online publication search system: First, we perform a coarse duplicate detection relying on publication signatures in real time. Then, a second automatic step compares duplicate candidates and increases accuracy while adjusting based on both feedback from our online users and from Crowdsourcing platforms. Our approach shows an improvement of 14% over the untrained setting and is at only 4% difference to the human assessors in accuracy.
DOLAP 2012 workshop summary
Authors: Matteo Golfarelli, Il-Yeol Song
Venue: CIKM
Year: 2012

The ACM DOLAP workshop presents research on data warehousing and On-Line Analytical Processing (OLAP). The DOLAP 2012 program is organized in four interesting sessions on data warehouse design and maintainability, OLAP querying and trends, warehousing of complex data, performance optimization and benchmarking.
Bridging offline and online social graph dynamics
Authors: Manuel Gomez-Rodriguez, Monica Rogati
Venue: CIKM
Year: 2012

The online and offline worlds are converging. Location-based services, ubiquitous mobile devices and on-the-go social network accessibility are blurring the distinction between in-person activities and their virtual counterpart. An important effect of this convergence is the rapid and powerful impact of offline events (meetings, conferences) on the evolution and temporal dynamics of the online connectivity between members of social and professional networks. However, these effects have been largely unexplored.
MADden: query-driven statistical text analytics
Authors: Christan Earl Grant, Joir-dan Gumbs, Kun Li, Daisy Zhe Wang, George Chitouras
Venue: CIKM
Year: 2012

In many domains, structured data and unstructured text are both important natural resources to fuel data analysis. Statistical text analysis needs to be performed over text data to extract structured information for further query processing. Typically, developers will need to connect multiple tools to build off-line batch processes to perform text analytic tasks. MADden is an integrated system developed for relational database systems such as PostgreSQL and Greenplum for real-time ad hoc query processing over structured and unstructured data. MADden implements four important text analytic functions that we have contributed to the MADlib open source library for textual analytics. In this demonstration, we will show the capability of the MADden text analytic library using computational journalism as the driving application. We show real-time declarative query processing over multiple data sources with both structured and text information.
Predicting web search success with fine-grained interaction data
Authors: Qi Guo, Dmitry Lagun, Eugene Agichtein
Venue: CIKM
Year: 2012

Detecting and predicting searcher success is essential for automatically evaluating and improving Web search engine performance. In the past, Web searcher behavior data, such as result clickthrough, dwell time, and query reformulation sequences, have been successfully used for a variety of tasks, including prediction of success in a search session. However, the effectiveness of the previous approaches has been limited, as they tend to ignore how searchers actually view and interact with the visited pages. We show that fine-grained interactions, such as mouse cursor movements and scrolling, provide additional clues for better predicting success of a search session as a whole. To this end, we identify patterns of examination and interaction behavior that correspond to search success, and design a new Fine-grained Session Behavior (FSB) model to capture these patterns. Our experimental results show that FSB is significantly more effective than the state-of-the-art approaches that do not use these additional interaction data.
Learning to recommend with social relation ensemble
Authors: Lei Guo, Jun Ma, Zhumin Chen, Haoran Jiang
Venue: CIKM
Year: 2012

Recommender systems with social networks (RSSN) have been well studied in recent works. However, these methods ignore the relationships among items, which may affect the quality of recommendations. Motivated by the observation that related items often have similar ratings, we propose a framework integrating items' relations, users' social graph and user-item rating matrix for recommendation. Experimental results show that our approach performs better than the state-of-art algorithm and the method with only users' social graph ensemble in terms of MAP and RMSE.
Swimming against the streamz: search and analytics over the enterprise activity stream
Authors: Ido Guy, Tal Steier, Maya Barnea, Inbal Ronen, Tal Daniel
Venue: CIKM
Year: 2012

Activity streams have become prevalent on the web and are starting to emerge in enterprises. In this work, we present Streamz, a novel application that uses a faceted search approach to provide employees with advanced capabilities of search, navigation, attention management, and other types of analytics on top of an enterprise activity stream. We provide a detailed description of the Streamz tool as well as usage analysis based on user interface logs and interviews of active users.
The downside of markup: examining the harmful effects of CSS and javascript on indexing today's web
Authors: Karl Gyllstrom, Carsten Eickhoff, Arjen P. de Vries, Marie-Francine Moens
Venue: CIKM
Year: 2012

The continued development and maturation of advanced HTML features such as Cascading style sheets (CSS), Javascript, and AJAX, as well as their widespread adoption by browsers, has enabled web pages to flourish with sophistication and interactivity. Unfortunately, this presents challenges to the web search community, as a web page's representation in the browser (i.e., what users see) can diverge dramatically from its raw HTML content (i.e., what search engines index and retrieve). For example, interactive pages may contain content in regions that are not visible before a user action, such as focusing a tab, but which are nonetheless still contained within the raw HTML. We study this divergence by comparing raw HTML to its fully rendered form across a number of metrics spanning presentation, geometry, and content, using a large, representative sample of popular web pages. We find that a large divergence currently exists, and we show via a historical analysis that this divergence has grown more pronounced over the last decade. The general finding of our study is that continuing to index the web via simple HTML parsing will diminish the effectiveness of retrieval on the modern web, and that the IR community should work toward more sophisticated web page processing in indexing technology.
Analyzing social media friendship for personalization
Authors: Jonghyun Han, Hyunju Lee
Venue: CIKM
Year: 2012

Since social media users have various purposes such as tightening friendship and obtaining information, it might be easier to model a user's interest and to provide personalized information if the user's purpose can be inferred. In this paper, we analyze the friendship of Twitter users and its effects on Twitter usage. According to our analysis, although the number of offline friends is smaller than that of online friends, a user more actively responds to the microblogs posted by the offline friends. We expect that our analysis is helpful to model a user's social behavior and interest.
The nautilus analyzer: understanding and debugging data transformations
Authors: Melanie Herschel, Hanno Eichelberger
Venue: CIKM
Year: 2012

When developing data transformations - a task omnipresent in applications like data integration, data migration, data cleaning, or scientific data processing -developers quickly face the need to verify the semantic correctness of the transformation. Declarative specifications of data transformations, e.g., SQL or ETL tools, increase developer productivity but usually provide limited or no means for inspection or debugging. In this situation, developers today have no choice but to manually analyze the transformation and, in case of an error, to (repeatedly) fix and test the transformation. The goal of the Nautilus project is to semi-automatically support this analysis-fix-test cycle. This demonstration focuses on one main component of Nautilus, namely the Nautilus Analyzer that helps developers in understanding and debugging their data transformations. The demonstration will show the capabilities of this component for data transformations specified in SQL on scenarios from different domains that are based on real-world data.
Cross-argument inference for implicit discourse relation recognition
Authors: Yu Hong, Xiaopei Zhou, Tingting Che, Jian-Min Yao, Qiaoming Zhu, Guodong Zhou
Venue: CIKM
Year: 2012

Motivated by the critical importance of connectives in recognizing discourse relations, we present an unsupervised cross-argument inference mechanism to implicit discourse relation recognition. The basic idea is to infer the implicit discourse relation of an argument pair from a large number of comparable argument pairs, which are automatically retrieved from the web in an unsupervised way. In this way, the inference proceeds from explicit relations to implicit ones via connective as bridge. This kind of pair-to-pair inference is based on the assumption that two argument pairs with high content similarity (i.e. comparable argument pairs) should have similar discourse relationship. Evaluation on PDTB proves the effectiveness of our inference mechanism in implicit relation recognition to the four level-1 relations. It also shows that our mechanism significantly outperforms other alternatives.
Finding nuggets in IP portfolios: core patent mining through textual temporal analysis
Authors: Po Hu, Minlie Huang, Peng Xu, Weichang Li, Adam K. Usadi, Xiaoyan Zhu
Venue: CIKM
Year: 2012

Patents are critical for a company to protect its core technologies. Effective patent mining in massive patent databases can provide companies with valuable insights to develop strategies for IP management and marketing. In this paper, we study a novel patent mining problem of automatically discovering core patents (i.e., patents with high novelty and influence in a domain). We address the unique patent vocabulary usage problem, which is not considered in traditional word-based statistical methods, and propose a topic-based temporal mining approach to quantify a patent's novelty and influence. Comprehensive experimental results on real-world patent portfolios show the effectiveness of our method.