OpenRaiser/Intern-Atlas huggingface dataset:
"Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data
Authors: Felix Bießmann, David Salinas, Sebastian Schelter, Philipp Schmidt, Dustin Lange
Venue: CIKM
Year: 2018

The success of applications that process data critically depends on the quality of the ingested data. Completeness of a data source is essential in many cases. Yet, most missing value imputation approaches suffer from severe limitations. They are almost exclusively restricted to numerical data, and they either offer only simple imputation methods or are difficult to scale and maintain in production. Here we present a robust and scalable approach to imputation that extends to tables with non-numerical values, including unstructured text data in diverse languages. Experiments on public data sets as well as data sets sampled from a large product catalog in different languages (English and Japanese) demonstrate that the proposed approach is both scalable and yields more accurate imputations than previous approaches. Training on data sets with several million rows is a matter of minutes on a single machine. With a median imputation F1 score of 0.93 across a broad selection of data sets our approach achieves on average a 23-fold improvement compared to mode imputation. While our system allows users to apply state-of-the-art deep learning models if needed, we find that often simple linear n-gram models perform on par with deep learning methods at a much lower operational cost. The proposed method learns all parameters of the entire imputation pipeline automatically in an end-to-end fashion, rendering it attractive as a generic plugin both for engineers in charge of data pipelines where data completeness is relevant, as well as for practitioners without expertise in machine learning who need to impute missing values in tables with non-numerical data.
Single-Setup Privacy Enforcement for Heterogeneous Data Ecosystems
Authors: Issac Buenrostro, Abhishek Tiwari, Vasanth Rajamani, Erman Pattuk, Zhixiong Chen
Venue: CIKM
Year: 2018

Strong member privacy in technology enterprises involves, among other objectives, deleting or anonymizing various kinds of data that a company controls. Those requirements are complicated in a heterogeneous data ecosystem where data is stored in multiple stores with different semantics: different indexing or update capabilities require processes specific to a store or even schema. In this demo we showcase a method to enforce record controls of arbitrary data stores via a three step process: generate an offline snapshot, run a policy mechanism to select rows to delete/update, and apply the changes to the original store. The first and third steps work on any store by leveraging Apache Gobblin, an open source data integration framework. The policy computation step runs as a batch Gobblin job where each table can be customized via a dataset metadata tracking system and SQL expressions providing table-specific business logic. This setup allows enforcement of highly-customizable privacy requirements in a variety of systems from hosted databases to third party data storage systems.
Semantic Technologies for Data Access and Integration
Authors: Diego Calvanese, Guohui Xiao
Venue: CIKM
Year: 2018

Recently, semantic technologies have been successfully deployed to overcome the typical difficulties in accessing and integrating data stored in different kinds of legacy sources. In particular, knowledge graphs are being used as a mechanism to provide a uniform representation of heterogeneous information. Such graphs represent data in the RDF format, which is complemented by an ontology and can be queried using the standard SPARQL language. The RDF graph is often obtained by materializing source data, following the traditional extract-transform-load workflow. Alternatively, the sources are declaratively mapped to the ontology, and the RDF graph is maintained virtual. In such an approach, usually called ontology-based data access/integration (OBDA/I), query answering is based on sophisticated query transformation techniques. In this tutorial: (i) we provide a general introduction to semantic technologies; (ii) we illustrate the principles underlying OBDA/I, providing insights into its theoretical foundations, and describing well-established algorithms, techniques, and tools; (iii) we discuss relevant use-cases for OBDA/I; (iv) we provide an overview on some recent advancements.
Efficient Energy Management in Distributed Web Search
Authors: Matteo Catena, Ophir Frieder, Nicola Tonellotto
Venue: CIKM
Year: 2018

Distributed Web search engines (WSEs) require warehouse-scale computers to deal with the ever-increasing size of the Web and the large amount of user queries they daily receive. The energy consumption of this infrastructure has a major impact on the economic profitability of WSEs. Recently several approaches to reduce the energy consumption of WSEs have been proposed. Such solutions leverage dynamic voltage and frequency scaling techniques in modern CPUs to adapt the WSEs' query processing to the incoming query traffic without negative impacts on latencies. A state-of-the-art research approach is the PESOS (Predictive Energy Saving Online Scheduling) algorithm, which can reduce the energy consumption of a WSE' single server by up to 50%. We evaluate PESOS on a simulated distributed WSE composed of a thousand of servers, and we compare its performance w.r.t. an industry-level baseline, called PEGASUS. Our results show that PESOS can reduce the CPU energy consumption of a distributed WSE by up to 18% with respect to PEGASUS, while providing query response times which are in line with user expectations.
Efficient and Reliable Estimation of Cell Positions
Authors: Mirela T. Cazzolato, Agma J. M. Traina, Klemens Böhm
Venue: CIKM
Year: 2018

Sequences of microscopic images feature the dynamics of developing embryos. Automatically tracking the cells from such sequences of images allows understanding the dynamics which a living element demands to know its cells movement, which ideally should take place in real-time. The traditional tracking pipeline starts with image acquisition, data transfer, image segmentation to separate cells from the background, and then the actual tracking step. To speed up this pipeline, we hypothesize that a process capable of predicting the cell motion according to previous observations is useful. The solution must be accurate, fast and lightweight, and be able to iterate between the various components. In this work we propose CM-Predictor, which takes advantage of previous positions of cells to estimate their motion. When estimation takes place, we can omit costly acquisition, transfer and process of images, speeding up the tracking pipeline. The designed solution monitors the error of prediction, adapting the model whenever needed. For validation, we use four different datasets with sequences of images with developing embryos. Then we compare the estimated motion vectors of CM-Predictor with traditional tracking methods. Experimental results show that CM-Predictor is able to accurately estimate the motion vectors. In fact, CM-Predictor maintains the prediction quality of other algorithms and performs faster than them.
Demonstration of GenoMetric Query Language
Authors: Stefano Ceri, Arif Canakoglu, Andrea Gulino, Abdulrahman Kaitoua, Marco Masseroli, Luca Nanni, Pietro Pinoli
Venue: CIKM
Year: 2018

In the last ten years, genomic computing has made gigantic steps due to Next Generation Sequencing (NGS), a high-throughput, massively parallel technology; the cost of producing a complete human sequence dropped to 1000 US$ in 2015 and is expected to drop below 100 US$ by 2020. Several new methods have recently become available for extracting heterogeneous datasets from the genome, revealing data signals such as variations from a reference sequence, levels of expression of coding regions, or protein binding enrichments ('peaks') with their statistical or geometric properties. Huge collections of such datasets are made available by large international consortia. In this new context, we developed GenoMetric Query Language (GMQL), a new data extraction and integration language. GMQL supports queries over thousands of heterogeneous datasets; as such, it is key to genomic data analysis. GMQL queries are executed on the cloud, after being translated and optimized; our best deployment uses Spark over Hadoop. Datasets are described by the Genomic Data Model (GDM), which provides interoperability between many data formats; GDM combines abstractions for genomic region data with the associated experimental, biological and clinical metadata. GMQL is targeted to the bio-informatics community for facilitating data exploration and for integrating data extraction and data analysis; this demonstration highlights its usability and expressive power. We show GMQL at work from a Web-based user interface and from a language embedding (Python).
CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks
Authors: Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, Jung-Tae Lee
Venue: CIKM
Year: 2018

Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.
Estimating Clickthrough Bias in the Cascade Model
Authors: Praveen Chandar, Ben Carterette
Venue: CIKM
Year: 2018

Recently, there has been considerable interest in the use of historical logged user interaction data—queries and clicks—for evaluation of search systems in the context of counterfactual analysis [8,10]. Recent approaches attempt to de-bias the historical log data by conducting randomization experiments and modeling the bias in user behavior. Thus far, the focus has been on addressing bias that arises due to the position of the document being clicked (position-bias) or sparsity of clicks on certain query-document pairs (selection-bias). However, there is another source of bias that could arise: the bias due to the context in which a document was presented to the user. The propensity of the user clicking on a document depends not only on its position but also on many other contextual factors. In this work, we show that the existing counterfactual estimators fail to capture one type of bias, specifically, the effect on click-through rates due to the relevance of documents ranked above. Further, we propose a modification to the existing estimator that takes into account this bias. We rely on full result randomization that allows us to control for the click context at various ranks; we demonstrate the effectiveness of our methods in evaluating retrieval system through experiments on a simulation setup that is designed to cover a wide variety of scenarios.
Towards Partition-Aware Lifted Inference
Authors: Melisachew Wudage Chekol, Heiner Stuckenschmidt
Venue: CIKM
Year: 2018

There is an ever increasing number of rule learning algorithms and tools for automatic knowledge base (KB) construction. These tools often produce weighted rules and facts that make up a probabilistic KB (PKB). In such a PKB, probabilistic inference is used in order to perform marginal inference, consistency checking and other tasks. However, in general, inference is known to be intractable. Hence, recently, there are a number of studies aimed at lifting (making tractable or approximating) inference by exploiting symmetries in the structure of a PKB. These studies alleviate grounding entirely a given PKB which can generate a sizable factor graph for inference (e.g. to compute the probability of a query). In line with this, we propose a novel technique to automatically partition rules based on their structure for efficient parallel grounding. In addition, we perform query expansion so as to generate a factor graph small enough to be used for efficient probability computation. We present a novel approximate marginal inference algorithm that uses N-hop subgraph extraction and query expansion. Moreover, we show that our system is much faster than state-of-the-art systems.
Modeling Users' Exposure with Social Knowledge Influence and Consumption Influence for Recommendation
Authors: Jiawei Chen, Yan Feng, Martin Ester, Sheng Zhou, Chun Chen, Can Wang
Venue: CIKM
Year: 2018

Users' consumption behaviors are affected by both their personal preference and their exposure to items (i.e. whether a user knows the items).Most of the recent works in social recommendation assume that people share similar preference with their socially connected friends. However, this assumption may not hold due to the diversity of social relations, and modeling social influence on users' preference may not be suitable for implicit feedback data (i.e. whether a user has consumed certain items). Since users often share item information with their social relations, it will be less restrictive to model social influence on users' exposure to items. We notice that a user's exposure is affected by the exposure of the other users in his social communities and by the consumption of his connected friends. In this paper, we propose a novel social exposure-based recommendation model SoEXBMF by integrating two kinds of social influence on users' exposure, i.e. social knowledge influence and social consumption influence, into basic EXMF model for better recommendation performance. Furthermore, SoEXBMF uses Bernoulli distribution instead of Gaussian distribution in EXMF to better model the binary implicit feedback data. A variational inference method has been developed for the proposed SoEXBMF model to infer the posterior and make the recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over existing methods in various evaluation metrics.
Exploiting Structural and Temporal Evolution in Dynamic Link Prediction
Authors: Huiyuan Chen, Jing Li
Venue: CIKM
Year: 2018

Link prediction in dynamic networks is an important task with many real-life applications in different domains, such as social networks, cyber-physical systems, and bioinformatics. There are two key processes in dynamic networks: network structural evolution and network temporal evolution, where the former represents interdependency between entities and their neighbors in the network at each timestamp, while the latter captures the evolving behavior of the entire network from the current timestamp to the next. Structural evolution generally assumes that a node is more likely to co-evolve with its neighbors in the near future. Temporal evolution focuses on the trend of network evolution as a whole, based on the accumulation of historical data. It is thus essential to use characteristics of both structural and temporal evolutions to emulate complex behaviors of dynamic networks. However, very few existing work considered both processes. In addition, real-life networks are often very sparse with limited observed links. A missing link between two nodes does not always imply that the two nodes do not have a relation in reality, especially when they share many common neighbors. Most existing methods only focus on the first-order proximity of networks, which is usually insufficient to capture the relationships among nodes. In this work, we propose a novel framework named STEP, to simultaneously integrate both structural and temporal information in link prediction in dynamic networks. STEP first constructs a sequence of higher-order proximity matrices to better capture the implicit relationships among nodes. A regularized optimization problem is then formulated to model those higher-order proximity matrices along with additional structural and temporal constraints. Given the large scale of modern networks, we also develop an efficient block coordinate gradient descent approach to solve the optimization problem efficiently. STEP can be used to solve the link prediction problem in directed or undirected, weighted or unweighted dynamic networks. Extensive experiments on several real world datasets demonstrate that STEP can effectively model link propagation over entire time-varying networks and its superiority over some state-of-the-art algorithms.
Short Text Entity Linking with Fine-grained Topics
Authors: Lihan Chen, Jiaqing Liang, Chenhao Xie, Yanghua Xiao
Venue: CIKM
Year: 2018

A wide range of web corpora are in the form of short text, such as QA queries, search queries and news titles. Entity linking for these short texts is quite important. Most of supervised approaches are not effective for short text entity linking. The training data for supervised approaches are not suitable for short text and insufficient for low-resourced languages. Previous unsupervised methods are incapable of handling the sparsity and noisy problem of short text. We try to solve the problem by mapping the sparse short text to a topic space. We notice that the concepts of entities have rich topic information and characterize entities in a very fine-grained granularity. Hence, we use the concepts of entities as topics to explicitly represent the context, which helps improve the performance of entity linking for short text. We leverage our linking approach to segment the short text semantically, and build a system for short entity text recognition and linking. Our entity linking approach exhibits the state-of-the-art performance on several datasets for the realistic short text entity linking problem.
"Bridge": Enhanced Signed Directed Network Embedding
Authors: Yiqi Chen, Tieyun Qian, Huan Liu, Ke Sun
Venue: CIKM
Year: 2018

Signed directed networks with positive or negative links convey rich information such as like or dislike, trust or distrust. Existing work of sign prediction mainly focuses on triangles (triadic nodes) motivated by balance theory to predict positive and negative links. However, real-world signed directed networks can contain a good number of "bridge'' edges which, by definition, are not included in any triangles. Such edges are ignored in previous work, but may play an important role in signed directed network analysis.%Such edges serve as fundamental building blocks and may play an important role in signed network analysis. In this paper, we investigate the problem of learning representations for signed directed networks. We present a novel deep learning approach to incorporating two social-psychologic theories, balance and status theories, to model both triangles and "bridge'' edges in a complementary manner. The proposed framework learns effective embeddings for nodes and edges which can be applied to diverse tasks such as sign prediction and node ranking. Experimental results on three real-world datasets of signed directed social networks verify the essential role of "bridge" edges in signed directed network analysis by achieving the state-of-the-art performance.
Enhanced Network Embeddings via Exploiting Edge Labels
Authors: Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena
Venue: CIKM
Year: 2018

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.
Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction
Authors: Yingmei Chen, Zhongyu Wei, Xuanjing Huang
Venue: CIKM
Year: 2018

In this paper, we propose to incorporate information of related corporations of a target company for its stock price prediction. We first construct a graph including all involved corporations based on investment facts from real market and learn a distributed representation for each corporation via node embedding methods applied on the graph. Two approaches are then explored to utilize information of related corporations based on a pipeline model and a joint model via graph convolutional neural networks respectively. Experiments on the data collected from stock market in Mainland China show that the representation learned from our model is able to capture relationships between corporations, and prediction models incorporating related corporations' information are able to make more accurate predictions on stock market.
Inferring Trip Occupancies in the Rise of Ride-Hailing Services
Authors: Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Tuan-Anh Hoang
Venue: CIKM
Year: 2018

The knowledge of all occupied and unoccupied trips made by self-employed drivers are essential for optimized vehicle dispatch by ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However, the occupancy status of vehicles is not always known to the service operators due to adoption of multiple ride-hailing apps. In this paper, we propose a novel framework, Learning to INfer Trips (LINT), to infer occupancy of car trips by exploring characteristics of observed occupied trips. Two main research steps, stop point classification and structural segmentation, are included in LINT. In the stop point classification step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels. The classification of vehicle trajectory stop points produces a stop point label sequence. For structural segmentation, we further propose several segmentation algorithms, including greedy segmentation (GS), efficient greedy segmentation (EGS), and dynamic programming-based segmentation (DP) to infer occupied trip from stop point label sequences. Our comprehensive experiments on real vehicle trajectories from self-employed drivers show that (1) the proposed stop point classifier predicts stop point labels with high accuracy, and (2) the proposed segmentation algorithm GS delivers the best accuracy performance with efficient running time.