OpenRaiser/Intern-Atlas huggingface dataset:
Gtensor: Fast and Accurate Tensor Analysis System using GPUs
Authors: Dawon Ahn, Sangjun Son, U Kang
Venue: CIKM
Year: 2020

Given a large tensor, how can we analyze it efficiently? Multi-dimensional arrays or tensors have been widely used to model real-world data. Tensor decomposition plays an important role in analyzing trends and major factors in tensors. While several tensor analysis tools have been developed, they show slow running time and limited scalability due to their heavy computational requirements. In this paper, we propose Gtensor, a fast and accurate tensor mining library using GPUs. Gtensor is carefully designed to utilize GPUs in addition to CPUs for fast analysis of tensors. Gtensor provides versatile tensor algebra tools including tensor decomposition, tensor generation, tensor-tensor operation, and tensor manipulation. We invite our audience to interact with Gtensor and analyze real-world tensor data in the domains of movie recommendation, place recommendations, and academic citation.
Detecting and Measuring the Exposure of Children and Adolescents to Inappropriate Comments in YouTube
Authors: Sultan Alshamrani
Venue: CIKM
Year: 2020

Social media platforms have been growing at a rapid pace, attracting users engagement with contents due to their convenience facilitated by many usable features. Such platforms provide users with interactive options such as likes, dislikes as well as a way of expressing their opinions in the form of text (i.e., comments). The ability of posting comments on these online platforms has allowed some users to post racist, obscene, as well as to spread hate on these platforms. In some cases, this kind of toxic behavior might turn the comment section from a space where users can share their views to a place where hate and profanity are spread. Such issues are observed across various social media platforms and many users are often exposed to these kinds of behaviors which requires comment moderators to spend a lot of time filtering out such inappropriate comments. Moreover, such textual "inappropriate contents" can be targeted towards users irrespective of age, concerning variety of topics (not only controversial), and triggered by various events. My doctoral dissertation work, therefore, is primarily focused on studying, detecting and analyzing users exposure to this kind of toxicity on different social media platforms utilizing the state-of-art techniques in deep learning and natural language processing. This paper presents one example of my works on detecting and measuring kids exposure to inappropriate comments posted on YouTube videos targeting young users. In the meantime, the same pipeline is being examined for measuring users interaction with mainstream news media and sentiment towards various topics in the public discourse in light of the Coronavirus disease 2019 (COVID'19).
Building Test Collections using Bandit Techniques: A Reproducibility Study
Authors: Bahadir Altun, Mücahid Kutlu
Venue: CIKM
Year: 2020

The high cost of constructing test collections led many researchers to develop intelligent document selection methods to find relevant documents with fewer judgments than the standard pooling method requires. In this paper, we conduct a comprehensive set of experiments to evaluate six bandit-based document selection methods, in terms of evaluation reliability, fairness, and reusability of the resultant test collections. In our experiments, the best performing method varies across test collections, showing the importance of using diverse test collections for an accurate performance analysis. Our experiments with six test collections also show that Move-To-Front is the most robust method among the ones we investigate.
GeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs
Authors: Reihaneh Amini, Lu Zhou, Pascal Hitzler
Venue: CIKM
Year: 2020

Since over a decade coreference resolution systems have been developed in order to find simple 1-to-1 equivalent mapping (sameAs relations) between instances of different linked datasets and knowledge graphs. Comparative evaluations of instance matching systems can inform us about the performance of such systems regarding artificial benchmarks or real-world data challenges. However, the lack of real data for evaluating these systems is currently a bottleneck. In this paper, we propose the use of the Cruise entities in the GeoLink data repository as a real-world instance matching benchmark for linked data and knowledge graphs. The GeoLink project has brought together seven datasets related to geoscience research. Both the ontology (T-box) and the instance data (A-box) of GeoLink are significantly larger than current benchmarks, and they have particularly interesting challenges, such as geospatial and temporal data. The benchmark we propose here consists of two real-world datasets in GeoLink called R2R data and BCO-DMO which includes manual curated owl:sameAs links between more than 900 Cruise entities of these two datasets. The reference alignment was discussed and generated by domain experts from different institutions and is expressed in the Alignment API format.
The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020): Special Edition on Dis/Misinformation Mining from Social media
Authors: Ebrahim Bagheri, Huan Liu, Kai Shu, Fattane Zarrinkalam
Venue: CIKM
Year: 2020

For the fifth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN), we organized a special edition with focus on dis/misinformation mining from social media, co-located with CIKM 2020. This topic has attracted a lot of interest from the community since the Coronavirus (COVID-19) epidemic has given rise to an increase of misinformation on social media. The aim of this edition was to bring together researchers from different disciplines interested in mining dis/misinformation on social media. In particular, the distinguishing focus of this special edition was its emphasis on techniques that use social media data for building diagnostic, predictive and prescriptive analysis models related to misinformation. This means that there is rigorous attention for techniques that can be used to understand how and why dis/misinformation is created and spread, to uncover hidden and unexpected aspects of dis/misinformation content, and to recommend insightful countermeasures to restrict the circulation of dis/misinformation and alleviate their negative effects.
FDCM: Towards Balanced and Generalizable Concept-based Models for Effective Medical Ranking
Authors: Mohammad Bahrani, Thomas Roelleke
Venue: CIKM
Year: 2020

Concept-based IR is expected to improve the quality of medical ranking since it captures more semantics than BOW representations. However, bringing concepts and BOW together into a transparent IR framework is challenging. We propose a new aggregation parameter to combine conceptual and term-based Dirichlet Compound Model scores effectively. The determination of this linear parameter is the result of exploring to what degree the difference of the conceptual and term-based sum of IDFs is influential to the integration. Instead of employing heuristics to find combined models, this paper aims to build the grounds for establishing reasonable aggregation standards based on semantic query performance predictors.
Tailoring Entity Matching for Industrial Settings
Authors: Nils Barlaug
Venue: CIKM
Year: 2020

Entity matching has received significant attention from the research community over many years. Despite some limited success, most state-of-the-art methods see no widespread usage in industry. In this paper, we present the author's PhD research, which aims at identifying issues that hold techniques and methods developed by the research community back from use in industry, and look at how they might be adapted to address those issues. In our proposed approach, we implement a modular framework, which will be used for real-world user testing and quantitative experiments of our adapted methods. We will have three main contributions from our research: 1) We develop a modular framework for interactive entity matching combining intra- and inter-session iterations. 2) We show how active learning methods for entity matching can be adapted to learn not only classification of matches but also classification of which records are of interest to the user jointly, and how it compares to current methods. 3) We show how deep learning can be used to synthesize interpretable rules for entity matching, and how it compares to traditional methods.
Optimal End-Biased Histograms for Hierarchical Data
Authors: Rachel Behar, Sara Cohen
Venue: CIKM
Year: 2020

We focus on summarizing hierarchical data by adapting the well-known notion of end biased-histograms to trees. Over relational data, such histograms have been well-studied, as they have a good balance between accuracy and space requirements. Extending histograms to tree data is a non-trivial problem, due to the need to preserve and leverage structure in the output. We develop a fast greedy algorithm, and a polynomial algorithm that finds provably optimal hierarchical end-biased histograms. Preliminary experimentation demonstrates that our histograms work well in practice.
AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Authors: Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas
Venue: CIKM
Year: 2020

The Third Workshop on "Advances in Interpretable Machine Learning and Artificial Intelligence" (AIMLAI) presents contributions in the fields of (i) interpretable ML and AI, i.e., algorithms that are natively interpretable, and (ii) interpretability modules, i.e., explanation layers on top of black-box models, also called post-hoc interpretability. AIMLAI encourages interdisciplinary collaborations with particular emphasis in knowledge management, infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters.
Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs
Authors: Till Blume, David Richerby, Ansgar Scherp
Venue: CIKM
Year: 2020

Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(Δ · dk), where Δ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about $50%$ of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.
Computational Approaches for Drug Repositioning: Towards a Holistic Perspective based on Knowledge Graphs
Authors: Marina Boudin
Venue: CIKM
Year: 2020

Drug development is a costly and time consuming activity. The traditional process relies on extensive experimental efforts to map out the relevant part of the chemical space. Data about molecules, diseases, genes and other entities are present on many isolated databases, be that internal or external and in heterogeneous formats. They either require costly and inflexible data integration, or time-consuming workflows. Computational approaches, and more recently artificial intelligence based techniques, have emerged as a promising alternative for reducing the development cycle through drug repositioning. Knowledge bases are used to predict new links between old drugs and new targets. We present below the overall approach adopted for my PhD thesis, for a more holistic knowledge graph-based drug repositioning that aims to discover hidden or missing links between existing drugs and targets for which no known treatment is available. Currently, eight data and knowledge resources have already been integrated into the designed knowledge graph.
DataMod2020: 9th International Symposium "From Data to Models and Back"
Authors: Juliana Bowles, Giovanna Broccia, Mirco Nanni
Venue: CIKM
Year: 2020

DataMod 2020 aims to bring together practitioners and researchers from academia, industry and research institutions interested in the combined application of computational modelling methods with data-driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest include automata, agents, Petri nets, process algebras and rewriting systems. Application domains include social systems, ecology, biology, medicine, smart cities, governance, security, education, software engineering, and any other field that deals with complex systems and large amounts of data. Papers can present research results in any of the themes of interest for the symposium as well as application experiences, tools and promising preliminary ideas. Papers dealing with synergistic approaches that integrate modelling and knowledge management/discovery or that exploit knowledge management/discovery to develop/syntesise system models are especially welcome.
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings
Authors: Anders H. Brams, Anders Langballe Jakobsen, Theis E. Jendal, Matteo Lissandrini, Peter Dolog, Katja Hose
Venue: CIKM
Year: 2020

Knowledge Graphs (KGs) have been integrated in several models of recommendation to augment the informational value of an item by means of its related entities in the graph. Yet, existing datasets only provide explicit ratings on items and no information is provided about users' opinions of other (non-recommendable) entities. To overcome this limitation, we introduce a new dataset, called the MindReader dataset, providing explicit user ratings both for items and for KG entities. In this first version, the MindReader dataset provides more than 102 thousands explicit ratings collected from 1,174 real users on both items and entities from a KG in the movie domain. This dataset has been collected through an online interview application that we also release as open source. As a demonstration of the importance of this new dataset, we present a comparative study of the effect of the inclusion of ratings on non-item KG entities in a variety of state-of-the-art recommendation models. In particular, we show that most models, whether designed specifically for graph data or not, see improvements in recommendation quality when trained on explicit non-item ratings. Moreover, for some models, we show that non-item ratings can effectively replace item ratings without loss of recommendation quality. This finding, in addition to an observed greater familiarity from users towards certain descriptive entities than movies, motivates the use of KG entities for both warm and cold-start recommendations.
Two Test Collections for Retrieval Using Named Entity Markup
Authors: Jacob Bremerman, Dawn J. Lawrie, James Mayfield, Douglas W. Oard
Venue: CIKM
Year: 2020

Studying the effects of semantic analysis on retrieval effectiveness can be difficult using standard test collections because both queries and documents typically lack semantic markup. This paper describes extensions to two test collections, CLEF 2003/2004 Russian and TDT-3 Chinese, to support study of the utility of named entity annotation. A new set of topic aspects that were expected to benefit from named entity markup were defined for topics in those test collections, with two queries for each aspect. One of these queries uses named entities as bag-of-words query terms or as semantic constraints on a free-text query term; the other is a bag-of-words baseline query without named entity markup. Exhaustive judgment of the documents annotated by CLEF or TDT as relevant to each corresponding topic was performed, resulting in relevance judgments for 133 Russian and 33 Chinese topic aspects that each have at least one relevant document. Named entity tags were automatically generated for the documents in both collections. Use of the test collections is illustrated with some preliminary experiments.
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
Authors: Lukas Brückner, Ioannis Arapakis, Luis A. Leiva
Venue: CIKM
Year: 2020

Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that mouse cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance.
Fast and Scalable Outlier Detection with Sorted Hypercubes
Authors: Eugênio F. Cabral, Robson L. F. Cordeiro
Venue: CIKM
Year: 2020

Outlier detection is the task responsible for finding novel or rare phenomena that provide valuable insights in many areas of the industry. The neighborhood-based algorithms are largely used to tackle this problem due to the intuitive interpretation and wide applicability in different domains. Their major drawback is the intensive neighborhood search that takes hours or even days to complete in large data, thus being impractical in many real-world scenarios. This paper proposes HySortOD -- a novel algorithm that uses an efficient hypercube-ordering-and-searching strategy for fast outlier detection. Its main focus is the analysis of data with many instances and a low-to-moderate number of dimensions. We performed comprehensive experiments using real data with up to ~500k instances and ~120 dimensions, where our new algorithm outperformed 7 state-of-the-art competitors in runtime, being up to 4 orders of magnitude faster in large data. Specifically, 12 well-known benchmark datasets were deeply investigated and one case study in the crucial task of breast cancer detection was also performed to demonstrate that our approach can be successfully used as an out-of-the-box solution for real-world, non-benchmark problems. Based on our experiments, we also identified default parameter values that allow us to be parameter-free and yet report high-quality results.
Laconic Image Classification: Human vs. Machine Performance
Authors: Javier Carrasco, Aidan Hogan, Jorge Pérez
Venue: CIKM
Year: 2020

We propose laconic classification as a novel way to understand and compare the performance of diverse image classifiers. The goal in this setting is to minimise the amount of information (aka. entropy) required in individual test images to maintain correct classification. Given a classifier and a test image, we compute an approximate minimal-entropy positive image for which the classifier provides a correct classification, becoming incorrect upon any further reduction. The notion of entropy offers a unifying metric that allows to combine and compare the effects of various types of reductions (e.g., crop, colour reduction, resolution reduction) on classification performance, in turn generalising similar methods explored in previous works. Proposing two complementary frameworks for computing the minimal-entropy positive images of both human and machine classifiers, in experiments over the ILSVRC test-set, we find that machine classifiers are more sensitive entropy-wise to reduced resolution (versus cropping or reduced colour for machines, as well as reduced resolution for humans), supporting recent results suggesting a texture bias in the ILSVRC-trained models used. We also find, in the evaluated setting, that humans classify the minimal-entropy positive images of machine models with higher precision than machines classify those of humans.
Collective Embedding with Feature Importance: A Unified Approach for Spatiotemporal Network Embedding
Authors: Dakshak Keerthi Chandra, Pengyang Wang, Jennifer L. Leopold, Yanjie Fu
Venue: CIKM
Year: 2020

In the last decade, there has been great progress in the field of machine learning and deep learning. These models have been instrumental in addressing a great number of problems. However, they have struggled when it comes to dealing with high dimensional data. In recent years, representation learning models have proven to be quite efficient in addressing this problem as they are capable of capturing effective lower-dimensional representations of the data. However, most of the existing models are quite ineffective when it comes to dealing with high dimensional spatiotemporal data as they encapsulate complex spatial and temporal relationships that exist among real-world objects. High-dimensional spatiotemporal data of cities represent urban communities. By learning their social structure we can better quantitatively depict them and understand factors influencing rapid growth, expansion, and changes. In this paper, we propose a collective embedding framework that leverages the use of auto-encoders and Laplacian score to learn effective embeddings of spatiotemporal networks of urban communities. In addition, we also develop a weighted degree centrality measure for constructing spatiotemporal heterogeneous networks. To evaluate the performance of our proposed model, we implement it on real-world urban community data. Experimental results demonstrate the effectiveness of our model over state-of-the-art alternatives.
Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation
Authors: Buru Chang, Gwanghoon Jang, Seoyoon Kim, Jaewoo Kang
Venue: CIKM
Year: 2020

Several geographical latent representation models that capture geographical influences among points-of-interest (POIs) have been proposed. Although the models improve POI recommendation performance, they depend on shallow methods that cannot effectively capture highly non-linear geographical influences from complex user-POI networks. In this paper, we propose a new graph-based geographical latent representation model (GGLR) which can capture highly non-linear geographical influences from complex user-POI networks. Our proposed GGLR considers two types of geographical influences: ingoing influences and outgoing influences. Based on a graph auto-encoder, geographical latent representations of ingoing and outgoing influences are trained to increase geographical influences between two consecutive POIs that frequently appear in check-in histories. Furthermore, we propose a graph neural network-based POI recommendation model (GPR) that uses the trained geographical latent representations of ingoing and outgoing influences for the estimation of user preferences. In the experimental evaluation on real-world datasets, we show that GGLR effectively captures highly non-linear geographical influences and GPR achieves state-of-the-art performance.