OpenRaiser/Intern-Atlas huggingface dataset:
Automated News Suggestions for Populating Wikipedia Entity Pages
Authors: Besnik Fetahu, Katja Markert, Avishek Anand
Venue: CIKM
Year: 2015

Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.
Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF
Authors: Hongchang Gao, Feiping Nie, Tom Weidong Cai, Heng Huang
Venue: CIKM
Year: 2015

As an important matrix factorization model, Nonnegative Matrix Factorization (NMF) has been widely used in information retrieval and data mining research. Standard Nonnegative Matrix Factorization is known to use the Frobenius norm to calculate the residual, making it sensitive to noises and outliers. It is desirable to use robust NMF models for practical applications, in which usually there are many data outliers. It has been studied that the 2,1, or 1-norm can be used for robust NMF formulations to deal with data outliers. However, these alternatives still suffer from the extreme data outliers. In this paper, we present a novel robust capped norm orthogonal Nonnegative Matrix Factorization model, which utilizes the capped norm for the objective to handle these extreme outliers. Meanwhile, we derive a new efficient optimization algorithm to solve the proposed non-convex non-smooth objective. Extensive experiments on both synthetic and real datasets show our proposed new robust NMF method consistently outperforms related approaches.
DOLAP 2015 Workshop Summary
Authors: Carlos Garcia-Alvarado, Carlos Ordonez, Il-Yeol Song
Venue: CIKM
Year: 2015

The ACM DOLAP workshop presents research that bridges data warehousing, On-Line Analytical Processing (OLAP), and other large-scale data processing platforms. The program has four interesting sessions on data warehouse design, database modeling, query processing, and text processing, as well as an invited paper on Big Data Database Design.
Scalable Facility Location for Massive Graphs on Pregel-like Systems
Authors: Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Mauro Sozio
Venue: CIKM
Year: 2015

We propose a new scalable algorithm for the facility-location problem. We study the graph setting, where the cost of serving a client from a facility is represented by the shortest-path distance on a graph. This setting is applicable to various problems arising in the Web and social media, and allows to leverage the inherent sparsity of such graphs. To obtain truly scalable performance, we design a parallel algorithm that operates on clusters of shared-nothing machines. In particular, we target modern Pregel-like architectures, and we implement our algorithm on Apache Giraph. Our work builds upon previous results: a facility location algorithm for the PRAM model, a recent distance-sketching method for massive graphs, and a parallel algorithm to finding maximal independent sets. The main challenge is to adapt those building blocks to the distributed graph setting, while maintaining the approximation guarantee and limiting the amount of distributed communication. Extensive experimental results show that our algorithm scales gracefully to graphs with billions of edges, while, in terms of quality, being competitive with state-of-the-art sequential algorithms.
Clustered Semi-Supervised Relevance Feedback
Authors: Kripabandhu Ghosh, Swapan Kumar Parui
Venue: CIKM
Year: 2015

In relevance feedback, first-round search results are used to boost second-round search results. Two forms have been traditionally considered: exhaustively labelled feedback, where all first-round results to depth k are annotated for relevance by the user; and blind feedback, where the top-k results are all assumed to be relevant. In this paper, we consider an intermediate, semi-supervised scheme, in which only a subset of results is selected for annotation, and then their labels are propagated to their nearest neighbours. Specifically, we use clustering to determine the nearest-neighbour groups, and seed selection to choose documents for annotation. We find that the effectiveness of this method is indistinguishable from the exhaustive relevance feedback, and is significantly higher than both blind feedback and the use of the annotated subset alone. We show that this approach works well in environments in which some but limited amounts of human feedback are available, such as early case assessment in e-discovery.
Comprehensible Models for Reconfiguring Enterprise Relational Databases to Avoid Incidents
Authors: Ioana Giurgiu, Mirela Botezatu, Dorothea Wiesmann
Venue: CIKM
Year: 2015

Configuring enterprise database management systems is a notoriously hard problem. The combinatorial parameter space makes it intractable to run and observe the DBMS behavior in all scenarios. Thus, the database administrator has the difficult task of choosing DBMS configurations that potentially lead to critical incidents, thus hindering its availability or performance. We propose using machine learning to understand how configuring a DBMS can lead to such high risk incidents. We collect historical data from three IT environments that run both IBM DB2 and Oracle DBMS. Then, we implement several linear and non-linear multivariate models to identify and learn from high risk configurations. We analyze their performance, in terms of accuracy, cost, generalization and interpretability. Results show that high risk configurations can be identified with extremely high accuracy and that the database administrator can potentially benefit from the rules extracted to reconfigure in order to prevent incidents.
Who Will You "@"?
Authors: Yeyun Gong, Qi Zhang, Xuyang Sun, Xuanjing Huang
Venue: CIKM
Year: 2015

In Twitter-like social networking services, people can use the "@" symbol to mention other users in tweets and send them a message or link to their profiles. In recent years, social media services are rapidly growing with thousands of millions of users participating in them every day. When the "@" symbol is entered, there should be an automatic suggestion function which recommends a small list of candidates in order to help users to easily identify and input usernames. In this paper, we present our work on building a recommendation system for the mention function in microblogging services. The recommendation strategy we used takes into consideration not only content of the microblog but also histories of candidate users. To better handle these textual information, we propose a novel method that extends the translation-based model. Experimental results on the dataset we collected from a real world microblogging service demonstrate that the proposed method outperforms state-of-the-art approaches.
On the Effect of "Stupid" Search Components on User Interaction with Search Engines
Authors: Lidia Grauer, Aleksandra Lomakina
Venue: CIKM
Year: 2015

Using eye-tracking, we investigate how searchers interact with Web search engines which get affected by nonsensical results. We conduct a user survey to choose "stupid" components for our laboratory experiment and explore the most conspicuous ones.
A Clustering-based Approach to Detect Probable Outcomes of Lawsuits
Authors: Daniel Lemes Gribel, Maíra Gatti de Bayser, Leonardo Guerreiro Azevedo
Venue: CIKM
Year: 2015

The numerous lawsuits in progress or already judged by the Brazilian Supreme Court consists of a large amount of non-structured data. This leads to a large number of hidden or unknown information, since some relationships between lawsuits are not explicit in the available data; and contributes to generate non-intuitive influences between variables, which in addition increases the degree of uncertainty on judicial outcomes. This work proposes an approach to identify possible judgment outcomes that considers the use of similarity calculations and clustering mechanisms based on lawsuits patterns. The similarity problem was tackled by analysing metadata manually extracted from lawsuits; and this work also presents an approach to detect clusters and to compile past votes. From the results, it is possible to verify lawsuits most likely outcomes and to detect their degree of uncertainty.
A Novel Class Noise Estimation Method and Application in Classification
Authors: Lin Gui, Qin Lu, Ruifeng Xu, Minglei Li, Qikang Wei
Venue: CIKM
Year: 2015

Noise in class labels of any training set can lead to poor classification results no matter what machine learning method is used. In this paper, we first present the problem of binary classification in the presence of random noise on the class labels, which we call class noise. To model class noise, a class noise rate is normally defined as a small independent probability of the class labels being inverted on the whole set of training data. In this paper, we propose a method to estimate class noise rate at the level of individual samples in real data. Based on the estimation result, we propose two approaches to handle class noise. The first technique is based on modifying a given surrogate loss function. The second technique eliminates class noise by sampling. Furthermore, we prove that the optimal hypothesis on the noisy distribution can approximate the optimal hypothesis on the clean distribution using both approaches. Our methods achieve over 87% accuracy on a synthetic non-separable dataset even when 40% of the labels are inverted. Comparisons to other algorithms show that our methods outperform state-of-the-art approaches on several benchmark datasets in different domains with different noise rates.
On Query-Update Independence for SPARQL
Authors: Nicola Guido, Pierre Genevès, Nabil Layaïda, Cécile Roisin
Venue: CIKM
Year: 2015

This paper investigates techniques for detecting independence of SPARQL queries from updates. A query is independent of an update when the execution of the update does not affect the result of the query. Determining independence is especially useful in the context of huge RDF repositories, where it permits to avoid expensive yet useless re-evaluation of queries. While this problem has been intensively studied for fragments of relational calculus, very few works exist for the standard query language for the semantic web. We report on our investigations on how a notion of independence can be defined in the SPARQL context.
Fast Distributed Correlation Discovery Over Streaming Time-Series Data
Authors: Tian Guo, Saket Sathe, Karl Aberer
Venue: CIKM
Year: 2015

The dramatic rise of time-series data in a variety of contexts, such as social networks, mobile sensing, data centre monitoring, etc., has fuelled interest in obtaining real-time insights from such data using distributed stream processing systems. One such extremely valuable insight is the discovery of correlations in real-time from large-scale time-series data. A key challenge in discovering correlations is that the number of time-series pairs that have to be analyzed grows quadratically in the number of time-series, giving rise to a quadratic increase in both computation cost and communication cost between the cluster nodes in a distributed environment. To tackle the challenge, we propose a framework called AEGIS. AEGIS exploits well-established statistical properties to dramatically prune the number of time-series pairs that have to be evaluated for detecting interesting correlations. Our extensive experimental evaluations on real and synthetic datasets establish the efficacy of AEGIS over baselines.
Social-Relational Topic Model for Social Networks
Authors: Weiyu Guo, Shu Wu, Liang Wang, Tieniu Tan
Venue: CIKM
Year: 2015

Social networking services, such as Twitter and Sina Weibo, have tremendous popularity in recent years. Mass of short texts and social links are aggregated into these service platforms. To realize personalized services on social network, topic inference from both short texts and social links plays more and more important role. Most conventional topic modeling methods focus on analyzing formal texts, e.g., papers, news and blogs, and usually assume that the links are only generated by topical factors. As a result, on social network, the learned topics of these methods are usually affected by topic-irrelevant links. Recently, a few approaches use artificial priors to recognize the links generated by the popularity factor in topic modeling. However, employing global priors, these methods can not well capture the distinct properties of each link and still suffer from the effect of topic-irrelevant links. To address the above limitations, we propose a novel Social-Relational Topic Model (SRTM), which can alleviate the effect of topic-irrelevant links by analyzing relational users' topics of each link. SRTM jointly models texts and social links for learning the topic distribution and topical influence of each user. The experimental results show that, our model outperforms the state-of-the-arts in topic modeling and social link prediction.
Learning Relative Similarity from Data Streams: Active Online Learning Approaches
Authors: Shuji Hao, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao
Venue: CIKM
Year: 2015

Relative similarity learning, as an important learning scheme for information retrieval, aims to learn a bi-linear similarity function from a collection of labeled instance-pairs, and the learned function would assign a high similarity value for a similar instance-pair and a low value for a dissimilar pair. Existing algorithms usually assume the labels of all the pairs in data streams are always made available for learning. However, this is not always realistic in practice since the number of possible pairs is quadratic to the number of instances in the database, and manually labeling the pairs could be very costly and time consuming. To overcome the limitation, we propose a novel framework of active online similarity learning. Specifically, we propose two new algorithms: (i)~PAAS: Passive-Aggressive Active Similarity learning; (ii)~CWAS: Confidence-Weighted Active Similarity learning, and we will prove their mistake bounds in theory. We have conducted extensive experiments on a variety of real-world data sets, and we find encouraging results that validate the empirical effectiveness of the proposed algorithms.
Efficient Computation of Trips with Friends and Families
Authors: Tanzima Hashem, Sukarna Barua, Mohammed Eunus Ali, Lars Kulik, Egemen Tanin
Venue: CIKM
Year: 2015

A group of friends located at their working places may want to plan a trip to visit a shopping center, have dinner at a restaurant, watch a movie at a theater, and then finally return to their homes with the minimum total trip distance. For a group of spatially dispersed users a group trip planning (GTP) query returns points of interests (POIs) of different types such as a shopping center, a restaurant and a movie theater that minimize the aggregate trip distance for the group. The aggregate trip distance could be the sum or maximum of the trip distances of all users in the group, where the users travel from their source locations via the jointly visited POIs to their individual destinations. In this paper, we develop both optimal and approximation algorithms for GTP queries for both Euclidean space and road networks. Processing GTP queries in real time is a computational challenge as trips involve POIs of multiple types and computation of aggregate trip distances. We develop novel techniques to refine the POI search space for a GTP query based on geometric properties of ellipses, which in turn significantly reduces the number of aggregate trip distance computations. An extensive set of experiments on a real and synthetic datasets shows that our approach outperforms the most competitive approach on an average by three orders of magnitude in terms of processing time.
Extracting Interest Tags for Non-famous Users in Social Network
Authors: Wei He, Hongyan Liu, Jun He, Shu Tang, Xiaoyong Du
Venue: CIKM
Year: 2015

Inferring interests of users in social network is important for many applications such as personalized search, recommender systems and online advertising. Most previous studies inferred users' interests based on text posted in social network, which is usually not related to their interests. In this paper, we propose a modified topic model, Bi-Labeled LDA with a term weighting scheme, to extract interest tags for users in social network. The proposed model utilize only users' relationship information without requirement for text information, and incorporates supervision into traditional LDA. Specifically, we introduce method to extract tags for non-famous user through their relationship with famous users in Twitter, and study why a non-famous user follows famous users simultaneously. Comparison with state-of-the-art methods on real dataset shows that our method is far more superior in terms of precision and recall of the extracted tag set, and also more applicable for many personalized applications. Besides, we find that a reasonable term weighting scheme can actually improve the performance further.
Discovering Canonical Correlations between Topical and Topological Information in Document Networks
Authors: Yuan He, Cheng Wang, Changjun Jiang
Venue: CIKM
Year: 2015

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in viably modeling such datasets is to discover proper denominators beneath the two different types of data, text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous latent factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. Logistic normal prior is also employed in modeling network to better capture the community correlations. We derive efficient inference and learning algorithms based on variational EM methods. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are namely hyperlinked networks of web pages, social networks of friends and coauthor networks of publications. Experimental results show that our approach achieves significant improvements on both topic modeling and community detection compared with the current state of the art. Meanwhile, our model is impressive in discovering correlations between extracted topics and communities.
Clustering-based Active Learning on Sensor Type Classification in Buildings
Authors: Dezhi Hong, Hongning Wang, Kamin Whitehouse
Venue: CIKM
Year: 2015

Commercial and industrial buildings account for a considerable portion of all energy consumed in the U.S., and thus reducing this energy consumption is a national grand challenge. Based on the large deployment of sensors in modern commercial buildings, many organizations are applying data analytic solutions to the thousands of sensing and control points to detect wasteful and incorrect operations for energy savings. Scaling this approach is challenging, however, because the metadata about these sensing and control points is inconsistent between buildings, or even missing altogether. Moreover, normalizing the metadata requires significant integration effort. In this work, we demonstrate a first step towards an automatic metadata normalization solution that requires minimal human intervention. We propose a clustering-based active learning algorithm to differentiate sensors in buildings by type, e.g., temperature v.s. humidity. Our algorithm exploits data clustering structure and propagates labels to their nearby unlabeled neighbors to accelerate the learning process. We perform a comprehensive study on metadata collected from over 20 different sensor types and 2,500 sensor streams in three commercial buildings. Our approach is able to achieve more than 92% accuracy for type classification with much less labeled examples than baselines. As a proof-of-concept, we also demonstrate a typical analytic application enabled by the normalized metadata.
Where You Go Reveals Who You Know: Analyzing Social Ties from Millions of Footprints
Authors: Hsun-Ping Hsieh, Rui Yan, Cheng-Te Li
Venue: CIKM
Year: 2015

This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.
A Parallel GPU-Based Approach to Clustering Very Fast Data Streams
Authors: Pengtao Huang, Xiu Li, Bo Yuan
Venue: CIKM
Year: 2015

Clustering data streams has become a hot topic in the era of big data. Driven by the ever increasing volume, velocity and variety of data, more efficient algorithms for clustering large-scale complex data streams are needed. In this paper, we present a parallel algorithm called PaStream, which is based on advanced Graphics Processing Unit (GPU) and follows the online-offline framework of CluStream. Our approach can achieve hundreds of times speedup on high-speed and high-dimensional data streams compared with CluStream. It can also discover clusters with arbitrary shapes and handle outliers properly. The efficiency and scalability of PaStream are demonstrated through comprehensive experiments on synthetic and standard benchmark datasets with various problem factors.