OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Multi-Relational Factorization Models for Multiple Target Relations
Authors: Lucas Rêgo Drumond, Ernesto Diaz-Aviles, Lars Schmidt-Thieme, Wolfgang Nejdl
Venue: CIKM
Year: 2014

Multi-matrix factorization models provide a scalable and effective approach for multi-relational learning tasks such as link prediction, Linked Open Data (LOD) mining, recommender systems and social network analysis. Such models are learned by optimizing the sum of the losses on all relations in the data. Early models address the problem where there is only one target relation for which predictions should be made. More recent models address the multi-target variant of the problem and use the same set of parameters to make predictions for all target relations. In this paper, we argue that a model optimized for each target relation individually has better predictive performance than models optimized for a compromise on the performance on all target relations. We introduce specific parameters for each target but, instead of learning them independently from each other, we couple them through a set of shared auxiliary parameters, which has a regularizing effect on the target specific ones. Experiments on large Web datasets derived from DBpedia, Wikipedia and BlogCatalog show the performance improvement obtained by using target specific parameters and that our approach outperforms competitive state-of-the-art methods while being able to scale gracefully to big data.
A Practical Fine-grained Approach to Resolving Incoherent OWL 2 DL Terminologies
Authors: Jianfeng Du, Guilin Qi, Xuefeng Fu
Venue: CIKM
Year: 2014

Resolving incoherent terminologies is an important task in the maintenance of evolving OWL 2 DL ontologies. Existing approaches to this task are either semi-automatic or based on simple deletion of axioms. There is a need of fine-grained approaches to automatize this task. Since a fine-grained approach should consider multiple choices for modifying an axiom other than the deletion of axioms only, the primary challenges for developing such an approach lie in both the semantics of the repaired results and the efficiency in computing the repaired results. To tackle these challenges, we first introduce the notion of fine-grained repair based on modifying one axiom to zero or more axioms, then propose an efficient incremental method for computing all fine-grained repairs one by one. We also propose a modification function for axioms expressed in OWL 2 DL, which returns weaker axioms. Based on this modification function and the method for computing fine-grained repairs, we develop an automatic approach to resolving incoherent OWL 2 DL terminologies. Our extensive experimental results demonstrate that the proposed approach is efficient and practical.
Modelling Complex Relevance Spaces with Copulas
Authors: Carsten Eickhoff, Arjen P. de Vries
Venue: CIKM
Year: 2014

Modern relevance models consider a wide range of criteria in order to identify those documents that are expected to satisfy the user's information need. With growing dimensionality of the underlying relevance spaces the need for sophisticated score combination and estimation schemes arises. In this paper, we investigate the use of copulas, a model family from the domain of robust statistics, for the formal estimation of the probability of relevance in high-dimensional spaces. Our experiments are based on the MSLR-WEB10K and WEB30K datasets, two annotated, publicly available samples of hundreds of thousands of real Web search impressions, and suggest that copulas can significantly outperform linear combination models for high-dimensional problems. Our models achieved a performance on par with that of state-of-the-art machine learning approaches.
TweetMogaz v2: Identifying News Stories in Social Media
Authors: Eslam Elsawy, Moamen Mokhtar, Walid Magdy
Venue: CIKM
Year: 2014

TweetMogaz is a news portal platform that generates news reports from social media content. It uses an adaptive information filtering technique for tracking tweets relevant to news topics, such as politics and sports in some regions. Relevant tweets for each topic are used to generate a comprehensive report about public reaction toward events happening. Showing a news report about an entire topic may be suboptimal for some users, since users prefer story-oriented presentation. In this demonstration, we present a technique for identifying stories within a stream of microblogs on a given topic. Detected tweets on a news story are used to generate a dynamic pseudo-article that gets its content updated in real-time based on trends on Twitter. Pseudo-article consists of a title, front-page image, set of tweets on the story, and links to external news articles. The platform is running live and tracks news on hot topics including Egyptian politics, Syrian conflict, and international sports.
Ranking Sentiment Explanations for Review Summarization Using Dual Decomposition
Authors: Lei Fang, Qiao Qian, Minlie Huang, Xiaoyan Zhu
Venue: CIKM
Year: 2014

For online reviews, sentiment explanations refer to the sentences that may suggest detailed reasons of sentiment, which are very important for applications in review mining like opinion summarization. In this paper, we address the problem of ranking sentiment explanations by formulating the process as two subproblems: sentence informativeness ranking and structural sentiment analysis. Tractable inference in joint prediction is performed through dual decomposition. Preliminary experiments on publicly available data demonstrate that our approach obtains promising performance.
A Flexible Framework for Projecting Heterogeneous Data
Authors: Aubrey Gress, Ian Davidson
Venue: CIKM
Year: 2014

In many real world settings the data to analyze is heterogeneous consisting of (say) images, text and video. An elegant approach when dealing with such data is to project all the data to a common space so standard learning methods can be used. However, typical projection methods make strong assumptions such as the multi-view assumption (datum in one data set are always associated with a single datum in the other view) or that the multiple data sets have an overlapping feature space. Such strong assumptions limit what data such work can be applied to. We present a framework for projecting heterogeneous data from multiple data sets into a common lower dimensional space using a rich range of guidance which does not assume any overlap between the instances or features in different data sets. Our work can specify inter-dataset (between instances in different data sets) guidance and intra-dataset (between instances in the same data set) guidance, both of which can be positively or negatively weighted. We show our work offers substantially more flexibility over related methods such as Canonical Correlation Analysis (CCA) and Locality Preserving Projections (LPP) and illustrate its superior performance for supervised and unsupervised learning problems.
Identifying Time Intervals of Interest to Queries
Authors: Dhruv Gupta, Klaus Berberich
Venue: CIKM
Year: 2014

We investigate how time intervals of interest to a query can be identified automatically based on pseudo-relevant docu-ments, taking into account both their publication dates and temporal expressions from their contents. Our approach is based on a generative model and is able to determine time in-tervals at di↵erent temporal granularities (e.g., day, month, or year). We evaluate our approach on twenty years ’ worth of newspaper articles from The New York Times using two novel testbeds consisting of temporally unambiguous and temporally ambiguous queries, respectively.
Within-Network Classification Using Radius-Constrained Neighborhood Patterns
Authors: Jialong Han, Ji-Rong Wen, Jian Pei
Venue: CIKM
Year: 2014

Within-Network Classification (WNC) techniques are designed for applications where objects to be classified and those with known labels are interlinked. For WNC tasks like web page classification, the homophily principle succeeds by assuming that linked objects, represented as adjacent vertices in a network, are likely to have the same labels. However, in other tasks like chemical structure completion, recent works suggest that the label of a vertex should be related to the local structure it resides in, rather than equated with those of its neighbors. These works also propose structure-aware vertex features or methods to deal with such an issue.
Identification of Answer-Seeking Questions in Arabic Microblogs
Authors: Maram Hasanain, Tamer Elsayed, Walid Magdy
Venue: CIKM
Year: 2014

Over the past years, Twitter has earned a growing reputation as a hub for communication, and events advertisement and tracking. However, several recent research studies have shown that Twitter users (and microblogging platforms' users in general) are increasingly posting microblogs containing questions seeking answers from their readers. To help those users answer or route their questions, the problem of question identification in tweets has been studied over English tweets; up to our knowledge, no study has attempted it over Arabic (not to mention dialectal Arabic) tweets. In this paper, we tackle the problem of identifying answer-seeking questions in different dialects over a large collection of Arabic tweets. Our approach is 2-stage. We first used a rule-based filter to extract tweets with interrogative questions. We then leverage a binary classifier (trained using a carefully-developed set of features) to detect tweets with answer-seeking questions. In evaluating the classifier, we used a set of randomly-sampled dialectal Arabic tweets that were labeled using crowdsourcing. Our approach achieved a relatively-good performance as a first study of that problem on the Arabic domain, exhibiting 64% recall with 80% precision in identifying tweets with answer-seeking questions.
Anything You Can Do, I Can Do Better: Finding Expert Teams by CrewScout
Authors: Naeemul Hassan, Huadong Feng, Ramesh Venkataraman, Gautam Das, Chengkai Li, Nan Zhang
Venue: CIKM
Year: 2014

CrewScout is an expert-team finding system based on the concept of skyline teams and efficient algorithms for finding such teams. Given a set of experts, CrewScout finds all k-expert skyline teams, which are not dominated by any other k-expert teams. The dominance between teams is governed by comparing their aggregated expertise vectors. The need for finding expert teams prevails in applications such as question answering, crowdsourcing, panel selection, and project team formation. The new contributions of this paper include an end-to-end system with an interactive user interface that assists users in choosing teams and an demonstration of its application domains.
Estimating the Number and Sizes of Fuzzy-Duplicate Clusters
Authors: Arvid Heise, Gjergji Kasneci, Felix Naumann
Venue: CIKM
Year: 2014

Duplicates in a dataset are multiple representations of the same real-world entity and constitute a major data quality problem. This paper investigates the problem of estimating the number and sizes of duplicate record clusters in advance and describes a sampling-based method for solving this problem. In extensive experiments, on multiple datasets, we show that the proposed method reliably estimates the number of duplicate clusters, while being highly efficient.
Exploring Document Collections with Topic Frames
Authors: Alexander Hinneburg, Frank Rosner, Stefan Peßler, Christian Oberländer
Venue: CIKM
Year: 2014

Topics automatically derived by topic models are not always easy and clearly interpretable by humans. The most probable top words of a topic may leave room for ambiguous interpretations, especially when the top words are exclusively nouns. We demonstrate how part-of-speech (POS) tagging and co-location analysis of terms can be used to derive linguistic frames that yield more interpretable topic representations. The so-called topic frames are demonstrated as feature of the TopicExplorer system that allows to explore document collections using topic models, visualizations and key word search. Demo versions of TopicExplorer are available at http://topicexplorer.informatik.uni-halle.de/ .
AESTHETICS: Analytics with Strings, Things, and Cats
Authors: Johannes Hoffart, Dragan Milchevski, Gerhard Weikum
Venue: CIKM
Year: 2014

This paper describes an advanced news analytics and exploration system that allows users to visualize trends of entities like politicians, countries, and organizations in continuously updated news articles. Our system improves state-of-the-art text analytics by linking ambiguous names in news articles to entities in knowledge bases like Freebase, DBpedia or YAGO. This step enables indexing entities and interpreting the contents in terms of entities. This way, the analysis of trends and co-occurrences of entities gains accuracy, and by leveraging the taxonomic type hierarchy of knowledge bases, also in expressiveness and usability. In particular, we can analyze not only individual entities, but also categories of entities and their combinations, including co-occurrences with informative text phrases. Our Web-based system demonstrates the power of this approach by insightful anecdotic analysis of recent events in the news.
An Eye-tracking Study of User Interactions with Query Auto Completion
Authors: Katja Hofmann, Bhaskar Mitra, Filip Radlinski, Milad Shokouhi
Venue: CIKM
Year: 2014

Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query.
A Mixtures-of-Trees Framework for Multi-Label Classification
Authors: Charmgil Hong, Iyad Batal, Milos Hauskrecht
Venue: CIKM
Year: 2014

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution <i>P</i>(<b>Y</b>|<b>X</b>). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods.
Mining and Planning Time-aware Routes from Check-in Data
Authors: Hsun-Ping Hsieh, Cheng-Te Li
Venue: CIKM
Year: 2014

Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf, to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed correspondingly. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.
Improving Tail Query Performance by Fusion Model
Authors: Shuai Huo, Min Zhang, Yiqun Liu, Shaoping Ma
Venue: CIKM
Year: 2014

Tail queries, which occur with low frequency, make up a large fraction of unique queries and often affect a user's experience during Web searching. Because of the data sparseness problem, information that can be leveraged for tail queries is not sufficient. Hence, it is important and difficult to improve the tail query performance. According to our observation, 26% of the tail queries are not essentially scarce: they are expressed in an unusual way, but the information requirements are not rare. In this study, we improve the tail query performance by fusing the results from original query and the query reformulation candidates. Other than results re-ranking, new results can be introduced by the fusion model. We emphasize that queries that can be improved are not only bad queries, and we propose to extract features that predict whether the performance can be improved. Then, we utilize a learning-to-rank method, which is trained to directly optimize a retrieval metric, to fuse the documents and obtain a final results list. We conducted experiments using data from two popular Chinese search engines. The results indicate that our fusion method significantly improves the performance of the tail queries and outperforms the state-of-the-art approaches on the same reformulations. Experiments show that our method is effective for the non-tail queries as well.
AIDA-Social: Entity Linking on the Social Stream
Authors: Yusra Ibrahim, Mohamed Amir Yosef, Gerhard Weikum
Venue: CIKM
Year: 2014

Named Entity Linking (NEL) in microblogs is a challenging task due to the use of cryptic abbreviations, insufficient contextual information, and the time-varying importance of entities. We propose three techniques to target these challenges: Mention Normalization, Contextual Enrichment, and Temporal Entity Importance. By combining these novel techniques, we achieve 13% improvement in precision over a state-of-the-art NEL tool.
A Probabilistic Concept Annotation for IT Service Desk Tickets
Authors: Ea-Ee Jan, Kuan-Yu Chen, Tsuyoshi Idé
Venue: CIKM
Year: 2014

Ticket annotation and search has become an important research subject in the IT service desk delivery. Millions of tickets are created yearly to address business users' IT related problems. In IT service desk management, it is critical to first capture the pain points for a group of tickets to determine root cause; secondly, to obtain the respective distributions in order to layout the priority of addressing these pain points. An advanced ticket analytics system utilizes a combination of topic modeling and clustering to address the above issues and the integration of these features into information architecture will allow for a wider distribution of this technology and progress to a remarkable financial impact for IT industry. Topic modeling has been used to extract topics from given documents; each topic is represented by unigram distributions. However, it is not clear how to interpret the results. Due to the inadequacy to render top concepts, in this paper, we propose a probabilistic framework, which integrates topic models, POS tags, query expansion and so on, for the practical challenge. The rigorously empirical experiments demonstrate the consistent and utility performance of the proposed method on real datasets.
Analysis on Community Variational Trend in Dynamic Networks
Authors: Xiaowei Jia, Nan Du, Jing Gao, Aidong Zhang
Venue: CIKM
Year: 2014

Temporal analysis on dynamic networks has become a popularly discussed topic today, with more and more emerging data over time. In this paper we investigate the problem of detecting and tracking the variational communities within a given time period. We first define a metric to measure the strength of a community, called the normalized temporal community strength. And then, we propose our analysis framework. The community may evolve over time, either split to multiple communities or merge with others. We address the problem of evolutionary clustering with requirement on temporal smoothness and propose a revised soft clustering method based on non-negative matrix factorization. Then we use a clustering matching method to find the soft correspondence between different community distribution structures. This matching establishes the connection between consecutive snapshots. To estimate the variational rate and meanwhile address the smoothness during continuous evolution, we propose an objective function that combines the conformity of current variation and historical variational trend. In addition, we integrate the weights to the objective function to identify the temporal outliers. An iterative coordinate descent method is proposed to solve the optimization framework. We extensively evaluate our method with a synthetic dataset and several real datasets. The experimental results demonstrate the effectiveness of our method, which is greatly superior to the baselines on detection of the communities with significant variation over time.
Semantic Annotation with RescoredESA: Rescoring Concept Features Generated From Explicit Semantic Analysis
Authors: Zhuoren Jiang, Miao Chen, Xiaozhong Liu
Venue: CIKM
Year: 2014

Concepts have been used extensively in semantic annotating. Explicit Semantic Analysis (ESA) is a concept feature generator, which represents text by a concept-level vector, such as a vector of Wikipedia concepts. It is also considered a human-friendly way to annotate text - it generates concept vector that can be easily interpreted by human. We propose an approach, RescoredESA, based on ESA, according to aspects upon which ESA can enhance: 1) sometimes the output vectors do not assign high scores to concepts relevant to the text; 2) it considers words in the text when representing the text to concept-level vector while not considering the concepts explicitly occurring in the text, which can be an important source for assigning scores to ESA vector dimensions. We evaluate it against the 20 newsgroup classification task, and the result shows a slight enhancement when combining vectors from RescoredESA and bag-of-words.
Multi-task Multi-view Learning for Heterogeneous Tasks
Authors: Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo, Qing He
Venue: CIKM
Year: 2014

Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the tasks with several views correspond to different set of class labels. This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study. Then, we propose a Multi-tAsk MUlti-view Discriminant Analysis (MAMUDA) method to solve this problem. Specifically, this method collaboratively learns the feature transformations for different views in different tasks by exploring the shared task-specific and problem intrinsic structures. Additionally, MAMUDA method is convenient to solve the multi-class classification problems. Finally, the experiments on two real-world problems demonstrate the effectiveness of MAMUDA for heterogeneous tasks.