OpenRaiser/Intern-Atlas huggingface dataset:
Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases
Authors: Yan Zhuang, Guoliang Li, Zhuojian Zhong, Jianhua Feng
Venue: CIKM
Year: 2017

With the vigorous development of the World Wide Web, many large-scale knowledge bases (KBs) have been generated. To improve the coverage of KBs, an important task is to integrate the heterogeneous KBs. Several automatic alignment methods have been proposed which achieve considerable success. However, due to the inconsistency and uncertainty of large-scale KBs, automatic techniques for KBs alignment achieve low quality (especially recall). Thanks to the open crowdsourcing platforms, we can harness the crowd to improve the alignment quality. To achieve this goal, in this paper we propose a novel hybrid human-machine framework for large-scale KB integration. We rst partition the entities of different KBs into many smaller blocks based on their relations. We then construct a partial order on these partitions and develop an inference model which crowdsources a set of tasks to the crowd and infers the answers of other tasks based on the crowdsourced tasks. Next we formulate the question selection problem, which, given a monetary budget B, selects B crowdsourced tasks to maximize the number of inferred tasks. We prove that this problem is NP-hard and propose greedy algorithms to address this problem with an approximation ratio of 1--1/e. Our experiments on real-world datasets indicate that our method improves the quality and outperforms state-of-the-art approaches.
Deep Learning Based Forecasting of Critical Infrastructure Data
Authors: Zahra Zohrevand, Uwe Glässer, Mohammad A. Tayebi, Hamed Yaghoubi Shahir, Mehdi Shirmaleki, Amir Yaghoubi Shahir
Venue: CIKM
Year: 2017

Intelligent monitoring and control of critical infrastructure such as electric power grids, public water utilities and transportation systems produces massive volumes of time series data from heterogeneous sensor networks. Time Series Forecasting (TSF) is essential for system safety and security, and also for improving the efficiency and quality of service delivery. Being highly dependent on various external factors, the observed system behavior is usually stochastic, which makes the next value prediction a tricky and challenging task that usually needs customized methods. In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods. Our approach takes advantage of extracting features at different time scales, which improves accuracy without compromising reliability in comparison with the state-of-the-art methods. Our experimental evaluation using real-world SCADA data from a municipal water management system shows that our proposed method outperforms the baseline methods evaluated here.
GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks
Authors: Dhruv Gupta, Klaus Berberich
Venue: CIKM
Year: 2018

In this work, we describe GYANI (gyan stands for knowledge in Hindi), an indexing infrastructure for search and analysis of large semantically annotated document collections. To facilitate the search for sentences or text regions for many knowledge-centric tasks such as information extraction, question answering, and relationship extraction, it is required that one can query large annotated document collections interactively. However, currently such an indexing infrastructure that scales to millions of documents and provides fast query execution times does not exist. To alleviate this problem, we describe how we can effectively index layers of annotations (e.g., part-of-speech, named entities, temporal expressions, and numerical values) that can be attached to sequences of words. Furthermore, we describe a query language that provides the ability to express regular expressions between word sequences and semantic annotations to ease search for sentences and text regions for enabling knowledge acquisition at scale. We build our infrastructure on a state-of-the-art distributed extensible record store. We extensively evaluate GYANI over two large news archives and the entire Wikipedia amounting to more than fifteen million documents. We observe that using GYANI we can achieve significant speed ups of more than 95x in information extraction, 53x on extracting answer candidates for questions, and 12x on relationship extraction task.
Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity
Authors: Xiaoli Li, Jun Huan
Venue: CIKM
Year: 2018

Multi-task Multi-view (MTMV) learning has recently undergone noticeable development for dealing with heterogeneous data. To exploit information from both related tasks and related views, a common strategy is to model task relatedness and view consistency separately. The drawback of this strategy is that it did not consider the interactions between tasks and views. To remedy this, we propose a novel method, racBFA, by adding rank constraints to asymmetric bilinear factor analyzers (aBFA). We then adapt racBFA to our MTMV learning problem and design a new MTMV learning algorithm, racMTMV. We evaluated racMTMV on 3 real-world data sets. The experimental results demonstrated the effectiveness of our proposed method.
A Scalable Algorithm for Higher-order Features Generation using MinHash
Authors: Pooja A, Naveen Nair, Rajeev Rastogi
Venue: CIKM
Year: 2018

Linear models have been widely used in the industry for their low computation time, small memory footprint and interpretability. However, linear models are not capable of leveraging non-linear feature interactions in predicting the target. This limits their performance. A classical approach to overcome this limitation is to use combinations of the original features, referred as higher-order features, to capture non-linearity. The number of higher-order features can be very large. Selecting the informative ones among them that are predictive of the target is essential for scalability. This is computationally expensive, requiring large memory footprint. In this paper, we propose a novel scalable MinHash based scheme to select informative higher-order features. Unlike typical use of MinHash for near-duplicate entity detection and association-rule mining, we use MinHash signature of features to approximate mutual information between higher-order features and target to enable their selection. By analyzing the running time and memory requirements, we show that our proposal is highly efficient in terms of running time and storage compared to existing alternatives. We demonstrate through experiments on multiple benchmark datasets that our proposed approach is not only scalable, but also able to identify the most important feature interactions resulting in improved model performance.
Inferring Probabilistic Contagion Models Over Networks Using Active Queries
Authors: Abhijin Adiga, Vanessa Cedeno-Mieles, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard Edwin Stearns
Venue: CIKM
Year: 2018

The problem of inferring unknown parameters of a networked social system is of considerable practical importance. We consider this problem for the independent cascade model using an active query framework. More specifically, given a network whose edge probabilities are unknown, the goal is to infer the probability value on each edge by querying the system. The optimization objective is to use as few queries as possible in carrying out the inference. We present approximation algorithms that provide provably good estimates of edge probabilities. We also present results from an experimental evaluation of our algorithms on several real-world networks.
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
Authors: Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis, Elizabeth Searles, Joyce C. Ho, Jimeng Sun
Venue: CIKM
Year: 2018

PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. Despite recent improvements on unconstrained PARAFAC2, its model factors are usually dense and sensitive to noise which limits their interpretability. As a result, the following open challenges remain: a) various modeling constraints, such as temporal smoothness, sparsity and non-negativity, are needed to be imposed for interpretable temporal modeling and b) a scalable approach is required to support those constraints efficiently for large datasets. To tackle these challenges, we propose a COnstrained PARAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors. To efficiently support all those constraints, COPA adopts a hybrid optimization framework using alternating optimization and alternating direction method of multiplier (AO-ADMM). As evaluated on large electronic health record (EHR) datasets with hundreds of thousands of patients, COPA achieves significant speedups (up to 36 times faster) over prior PARAFAC2 approaches that only attempt to handle a subset of the constraints that COPA enables. Overall, our method outperforms all the baselines attempting to handle a subset of the constraints in terms of speed, while achieving the same level of accuracy. Through a case study on temporal phenotyping of medically complex children, we demonstrate how the constraints imposed by COPA reveal concise phenotypes and meaningful temporal profiles of patients. The clinical interpretation of both the phenotypes and the temporal profiles was confirmed by a medical expert.
Unbiased Learning to Rank: Theory and Practice
Authors: Qingyao Ai, Jiaxin Mao, Yiqun Liu, W. Bruce Croft
Venue: CIKM
Year: 2018

Implicit feedback (e.g., user clicks) is an important source of data for modern search engines. While heavily biased [8, 9, 11, 27], it is cheap to collect and particularly useful for user-centric retrieval applications such as search ranking. To develop an unbiased learning-to-rank system with biased feedback, previous studies have focused on constructing probabilistic graphical models (e.g., click models) with user behavior hypothesis to extract and train ranking systems with unbiased relevance signals. Recently, a novel counterfactual learning framework that estimates and adopts examination propensity for unbiased learning to rank has attracted much attention. Despite its popularity, there is no systematic comparison of the unbiased learning-to-rank frameworks based on counterfactual learning and graphical models. In this tutorial, we aim to provide an overview of the fundamental mechanism for unbiased learning to rank. We will describe the theory behind existing frameworks, and give detailed instructions on how to conduct unbiased learning to rank in practice.
Re-evaluating Embedding-Based Knowledge Graph Completion Methods
Authors: Farahnaz Akrami, Lingbing Guo, Wei Hu, Chengkai Li
Venue: CIKM
Year: 2018

Incompleteness of large knowledge graphs (KG) has motivated many researchers to propose methods to automatically find missing edges in KGs. A promising approach for KG completion (link prediction) is embedding a KG into a continuous vector space. There are different methods in the literature that learn a continuous representation of KG (latent features of KG). The benchmark dataset FB15k has been widely employed to evaluate these methods. However, It has been noted that FB15k contains many pairs of edges in which a pair represents the same relationship in reverse directions. Therefore, the inverse of numerous test triples occurs in the training set. To address this problem, FB15k-237, a subset of FB15k, was created by removing those inverse-duplicate relations to form a more challenging, realistic dataset. There is not any study that investigates how the aforementioned bias in this widely used benchmark dataset affects the results of embedding-based knowledge graph completion methods and whether their promising results are largely due to the bias. Motivated by this question, we conducted extensive experiments and report the link prediction results on FB15K and FB15k-237 using several embedding-based methods. We compare the results of different methods to see how their performances change in absence of inverse relations. Our experiment results demonstrate that the performance of embedding models in link prediction task diminishes tremendously when the inverse relationships do not exist anymore.
In Situ and Context-Aware Target Apps Selection for Unified Mobile Search
Authors: Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft
Venue: CIKM
Year: 2018

With the recent growth in the use of conversational systems and intelligent assistants such as Google Assistant and Microsoft Cortana, mobile devices are becoming even more pervasive in our lives. As a consequence, users are getting engaged with mobile apps and frequently search for an information need using different apps. Recent work has stated the need for a unified mobile search system that would act as meta search on users' mobile devices: it would identify the target apps for the user's query, submit the query to the apps, and present the results to the user. Moreover, mobile devices provide rich contextual information about users and their whereabouts. In this paper, we introduce the task of context-aware target apps selection as part of a unified mobile search framework. To this aim, we designed an in situ study to collect thousands of mobile queries enriched with mobile sensor data from 255 users during a three month period. With the aid of this dataset, we were able to study user behavior as they performed cross-app search. We finally study the performance of state-of-the-art retrieval models for this task and propose a simple yet effective neural model that significantly outperforms the baselines. Our neural approach is based on learning high-dimensional representations for mobile apps and contextual information. Furthermore, we show that incorporating context improves the performance by 20% in terms of nDCG@5, enabling the model to perform better for 57% of users. Our data is publicly available for research purposes.
Detecting Parkinson's Disease from Interactions with a Search Engine: Is Expert Knowledge Sufficient?
Authors: Liron I. Allerhand, Brit Youngmann, Elad Yom-Tov, David Arkadir
Venue: CIKM
Year: 2018

Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Recently, there has been a growing interest in developing automatic tools that can assess motor function in PD patients. Here we show that mouse tracking data collected during people's interaction with a search engine can be used to distinguish PD patients from similar, non-diseased users and present a methodology developed for the diagnosis of PD from these data. The main challenge we address is the extraction of informative features from raw mouse tracking data. We do so in two complementary ways: First, we manually construct expert-recommended features, aiming to identify abnormalities in motor behaviors. Second, we use an unsupervised representation learning technique to map these raw data to high-level features. Using all the extracted features, a Random Forest classifier is then used to distinguish PD patients from controls, achieving an AUC of 0.92, while results using only expert-generated or auto-generated features are 0.87 and 0.83, respectively. Our results indicate that mouse tracking data can help in detecting users at early stages of the disease and that both expert-generated features and unsupervised techniques for feature generation are required to achieve the best possible performance.
DeepAuth: A Framework for Continuous User Re-authentication in Mobile Apps
Authors: Sara Amini, Vahid Noroozi, Amit Pande, Satyajit Gupte, Philip S. Yu, Chris Kanich
Venue: CIKM
Year: 2018

With the increasing volume of transactions taking place online, mobile fraud has also increased. Mobile applications often authenticate the user only at install time. The user may then remain logged in for hours or weeks. Any unauthorized access may lead to financial, criminal or privacy losses. In this work, we leverage currently available built-in motion sensors in smartphones to learn users' behavioral characteristics while interacting with the mobile device to provide an implicit re-authentication mechanism that enables a frictionless and secure user experience in the application. This approach improves the generality as well as power efficiency of the authentication mechanism compared to using the camera feed which involves (a) specific hardware, (b) higher battery usage and (c) privacy concerns. We present DeepAuth as a generic framework for re-authenticating users in a mobile app. In our approach, we use time and frequency domain features extracted from motion sensors and a long short-term memory (LSTM) model with negative sampling to build a re-authentication framework. The framework is able to re-authenticate a user with 96.70% accuracy in 20 seconds from a set of data collected from 47 volunteers.
Causal Dependencies for Future Interest Prediction on Twitter
Authors: Negar Arabzadeh, Hossein Fani, Fattane Zarrinkalam, Ahmed Navivala, Ebrahim Bagheri
Venue: CIKM
Year: 2018

The accurate prediction of users' future topics of interests on social networks can facilitate content recommendation and platform engagement. However, researchers have found that future interest prediction, especially on social networks such as Twitter, is quite challenging due to the rapid changes in community topics and evolution of user interactions. In this context, temporal collaborative filtering methods have already been used to perform user interest prediction, which benefit from similar user behavioral patterns over time to predict how a user's interests might evolve in the future. In this paper, we propose that instead of considering the whole user base within a collaborative filtering framework to predict user interests, it is possible to much more accurately predict such interests by only considering the behavioral patterns of the most influential users related to the user of interest. We model influence as a form of causal dependency between users. To this end, we employ the concept of Granger causality to identify causal dependencies. We show through extensive experimentation that the consideration of only one causally dependent user leads to much more accurate prediction of users' future interests in a host of measures including ranking and rating accuracy metrics.
Impact of Document Representation on Neural Ad hoc Retrieval
Authors: Ebrahim Bagheri, Faezeh Ensan, Feras N. Al-Obeidat
Venue: CIKM
Year: 2018

Neural embeddings have been effectively integrated into information retrieval tasks including ad hoc retrieval. One of the benefits of neural embeddings is they allow for the calculation of the similarity between queries and documents through vector similarity calculation methods. While such methods have been effective for document matching, they have an inherent bias towards documents that are sized relatively similarly. Therefore, the difference between the query and document lengths, referred to as the query-document size imbalance problem, becomes an issue when incorporating neural embeddings and their associated similarity calculation models into the ad hoc document retrieval process. In this paper, we propose that document representation methods need to be used to address the size imbalance problem and empirically show their impact on the performance of neural embedding-based ad hoc retrieval. In addition, we explore several types of document representation methods and investigate their impact on the retrieval process. We conduct our experiments on three widely used standard corpora, namely Clueweb09B, Clueweb12B and Robust04 and their associated topics. Summarily, we find that document representation methods are able to effectively address the query-document size imbalance problem and significantly improve the performance of neural ad hoc retrieval. In addition, we find that a document representation method based on a simple term-frequency shows significantly better performance compared to more sophisticated representation methods such as neural composition and aspect-based methods.
Attentive Neural Architecture for Ad-hoc Structured Document Retrieval
Authors: Saeid Balaneshinkordan, Alexander Kotov, Fedor Nikolaev
Venue: CIKM
Year: 2018

The problem of ad-hoc structured document retrieval arises in many information access scenarios, from Web to product search. Yet neither deep neural networks, which have been successfully applied to ad-hoc information retrieval and Web search, nor the attention mechanism, which has been shown to significantly improve the performance of deep neural networks on natural language processing tasks, have been explored in the context of this problem. In this paper, we propose a deep neural architecture for ad-hoc structured document retrieval, which utilizes attention mechanism to determine important phrases in keyword queries as well as the relative importance of matching those phrases in different fields of structured documents. Experimental evaluation on publicly available collections for Web document, product and entity retrieval from knowledge graphs indicates superior retrieval accuracy of the proposed neural architecture relative to both state-of-the-art neural architectures for ad-hoc document retrieval and probabilistic models for ad-hoc structured document retrieval.
From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project
Authors: Konstantina Bereta, Hervé Caumont, Erwin Goor, Manolis Koubarakis, Despina-Athanasia Pantazi, George Stamoulis, Sam Ubels, Valentijn Venus, Firman Wahyudi
Venue: CIKM
Year: 2018

Copernicus is the European program for monitoring the Earth. It consists of a set of complex systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data collected by Copernicus is made available freely following an open access policy. Information extracted from Copernicus data is disseminated to users through the Copernicus services which address six thematic areas: land, marine, atmosphere, climate, emergency and security. We present a demo from the Horizon 2020 Copernicus App Lab project which takes big data from the Copernicus land service, makes it available on the Web as linked geospatial data and interlinks it with other useful public data to aid the development of applications by developers that might not be Earth Observation experts. Our demo targets a scenario where we want to study the "greenness" of Paris.