OpenRaiser/Intern-Atlas huggingface dataset:
Diversifying Search Results using Self-Attention Network
Authors: Xubo Qin, Zhicheng Dou, Ji-Rong Wen
Venue: CIKM
Year: 2020

Search results returned by search engines need to be diversified in order to satisfy different information needs of different users. Several supervised learning models have been proposed for diversifying search results in recent years. Most of the existing supervised methods greedily compare each candidate document with the selected document sequence and select the next local optimal document. However, the information utility of each candidate document is not independent with each other, and research has shown that the selection of a candidate document will affect the utilities of other candidate documents. As a result, the local optimal document rankings will not lead to the global optimal rankings. In this paper, we propose a new supervised diversification framework to address this issue. Based on a self-attention encoder-decoder structure, the model can take the whole candidate document sequence as input, and simultaneously leverage both the novelty and the subtopic coverage of the candidate documents. We call this framework Diversity Encoder with Self-Attention (DESA). Comparing with existing supervised methods, this framework can model the interactions between all candidate documents and return their diversification scores based on the whole candidate document sequence. Experimental results show that our proposed framework outperforms existing methods. These results confirm the effectiveness of modeling all the candidate documents for the overall novelty and subtopic coverage globally, instead of comparing every single candidate document with the selected sequence document selection.
G-CREWE: Graph CompREssion With Embedding for Network Alignment
Authors: Kyle Kai Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra
Venue: CIKM
Year: 2020

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarity based on node representations. However, the process of aligning every pair of nodes between relatively large networks is time-consuming and resource-intensive. In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem. G-CREWE uses node embeddings to align the networks on two levels of resolution, a fine resolution given by the original network and a coarse resolution given by a compressed version, to achieve an efficient and effective network alignment. The framework first extracts node features and learns the node embedding via a Graph Convolutional Network (GCN). Then, node embedding helps to guide the process of graph compression and finally improve the alignment performance. As part of G-CREWE, we also propose a new compression mechanism called MERGE (Minimum DEgRee NeiGhbors ComprEssion) to reduce the size of the input networks while preserving the consistency in their topological structure. Experiments on all real networks show that our method is more than twice as fast as the most competitive existing methods while maintaining high accuracy.
Hierarchical Query Graph Generation for Complex Question Answering over Knowledge Graph
Authors: Yunqi Qiu, Kun Zhang, Yuanzhuo Wang, Xiaolong Jin, Long Bai, Saiping Guan, Xueqi Cheng
Venue: CIKM
Year: 2020

Knowledge Graph Question Answering aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge graphs. When faced with a complex question with compositional semantics, query graph generation is a practical semantic parsing-based method. But existing works rely on heuristic rules with limited coverage, making them impractical on more complex questions. This paper proposes a Director-Actor-Critic framework to overcome these challenges. Through options over a Markov Decision Process, query graph generation is formulated as a hierarchical decision problem. The Director determines which types of triples the query graph needs, the Actor generates corresponding triples by choosing nodes and edges, and the Critic calculates the semantic similarity between the generated triples and the given questions. Moreover, to train from weak supervision, we base the framework on hierarchical Reinforcement Learning with intrinsic motivation. To accelerate the training process, we pre-train the Critic with high-reward trajectories generated by hand-crafted rules, and leverage curriculum learning to gradually increase the complexity of questions during query graph generation. Extensive experiments conducted over widely-used benchmark datasets demonstrate the effectiveness of the proposed framework.
Category-aware Graph Neural Networks for Improving E-commerce Review Helpfulness Prediction
Authors: Xiaoru Qu, Zhao Li, Jialin Wang, Zhipeng Zhang, Pengcheng Zou, Junxiao Jiang, Jiaming Huang, Rong Xiao, Ji Zhang, Jun Gao
Venue: CIKM
Year: 2020

Helpful reviews in e-commerce sites can help customers acquire detailed information about a certain item, thus affecting customers' buying decisions. Predicting review helpfulness automatically in Taobao is an essential but challenging task for two reasons: (1) whether a review is helpful not only relies on its text, but also is related with the corresponding item and the user who posts the review, (2) the criteria of classifying review helpfulness under different items are not the same. To handle these two challenges, we propose CA-GNN (Category Aware Graph Neural Networks), which uses graph neural networks (GNNs) to identify helpful reviews in a multi-task manner --- we employ GNNs with one shared and many item-specific graph convolutions to learn the common features and each item's specific criterion for classifying reviews simultaneously. To reduce the number of parameters in CA-GNN and further boost its performance, we partition the items into several clusters according to their category information, such that items in one cluster share a common graph convolution.We conduct solid experiments on two public datasets and demonstrate that CA-GNN outperforms existing methods by up to 10.9% in AUC. We also deployed our system in Taobao with online A/B Test and verify that CA-GNN still outperforms the baseline system in most cases.
Learning to Form Skill-based Teams of Experts
Authors: Radin Hamidi Rad, Hossein Fani, Mehdi Kargar, Jaroslaw Szlichta, Ebrahim Bagheri
Venue: CIKM
Year: 2020

We focus on the composition of teams of experts that collectively cover a set of required skills based on their historical collaboration network and expertise. Prior works are primarily based on the shortest path between experts on the expert collaboration network, and suffer from three major shortcomings: (1) they are computationally expensive due to the complexity of finding paths on large network structures; (2) they use a small portion of the entire historical collaboration network to reduce the search space; hence, may form sub-optimal teams; and, (3) they fall short in sparse networks where the majority of the experts have only participated in a few teams in the past. Instead of forming a large network of experts, we propose to learn relationships among experts and skills through a variational Bayes neural architecture wherein: i) we consider all past team compositions as training instances to predict future teams; ii) we bring scalability for large networks of experts due to the neural architecture; and, iii) we address sparsity by incorporating uncertainty on the neural network's parameters which yields a richer representation and more accurate team composition. We empirically demonstrate how our proposed model outperforms the state-of-the-art approaches in terms of effectiveness and efficiency based on a large DBLP dataset.
Training Sensitivity in Graph Isomorphism Network
Authors: Md. Khaledur Rahman
Venue: CIKM
Year: 2020

Graph neural network (GNN) is a popular tool to learn the lower-dimensional representation of a graph. It facilitates the applicability of machine learning tasks on graphs by incorporating domain-specific features. There are various options for underlying procedures (such as optimization functions, activation functions, etc.) that can be considered in the implementation of GNN. However, most of the existing tools are confined to one approach without any analysis. Thus, this emerging field lacks a robust implementation ignoring the highly irregular structure of the real-world graphs. In this paper, we attempt to fill this gap by studying various alternative functions for a respective module using a diverse set of benchmark datasets. Our empirical results suggest that the generally used underlying techniques do not always perform well to capture the overall structure from a set of graphs.
A Large Test Collection for Entity Aspect Linking
Authors: Jordan Ramsdell, Laura Dietz
Venue: CIKM
Year: 2020

Given a text with entity links, the task of entity aspect linking is to identify which aspect of an entity is referred to in the context. For example, if a text passage mentions the entity "USA'', is USA mentioned in the context of the 2008 financial crisis, American cuisine, or else? Complementing efforts of Nanni et al (2018), we provide a large-scale test collection which is derived from Wikipedia hyperlinks in a dump from 01/01/2020. Furthermore, we offer strong baselines with results and broken-out feature sets to stimulate more research in this area. Data, code, feature sets, runfiles and results are released under a CC-SA license and offered on our aspect linking resource web page http://www.cs.unh.edu/~dietz/eal-dataset-2020/
Securing Bloom Filters for Privacy-preserving Record Linkage
Authors: Thilina Ranbaduge, Rainer Schnell
Venue: CIKM
Year: 2020

Privacy-preserving record linkage (PPRL) facilitates the matching of records that correspond to the same real-world entities across different databases while preserving the privacy of the individuals in these databases. A Bloom filter (BF) is a space efficient probabilistic data structure that is becoming popular in PPRL as an efficient privacy technique to encode sensitive information in records while still enabling approximate similarity computations between attribute values. However, BF encoding is susceptible to privacy attacks which can re-identify the values that are being encoded. In this paper we propose two novel techniques that can be applied on BF encoding to improve privacy against attacks. Our techniques use neighbouring bits in a BF to generate new bit values. An empirical study on large real databases shows that our techniques provide high security against privacy attacks, and achieve better similarity computation accuracy and linkage quality compared to other privacy improvements that can be applied on BF encoding.
Product Insights: Analyzing Product Intents in Web Search
Authors: Nikitha Rao, Chetan Bansal, Subhabrata Mukherjee, Chandra Shekhar Maddila
Venue: CIKM
Year: 2020

Web search engines are frequently used to access information about products. This has increased in recent times with the rising popularity of e-commerce. However, there is limited understanding of what users search for and their intents when it comes to product search on the web. In this work, we study search logs from Bing web search engine to characterize user intents and study user behavior for product search. We propose a taxonomy of product intents by analyzing product search queries. This itself is a challenging task given that only 15%-17% of queries in the web refer to products. We train machine learning classifiers with query log features to classify queries based on intent with an overall F1-score of 78%. We further analyze various characteristics of product search queries in terms of search metrics like dwell time, success, popularity and session-specific information.
Structured Knowledge: Have we made progress? An extrinsic study of KB coverage over 19 years
Authors: Simon Razniewski, Priyanka Das
Venue: CIKM
Year: 2020

Structured world knowledge is at the foundation of knowledge-centric AI applications. Despite considerable research on knowledge base construction, beyond mere statement counts, little is known about the progress of KBs, in particular concerning their coverage, and one may wonder whether there is constant progress, or diminishing returns. In this paper we employ question answering and entity summarization as extrinsic use cases for a longitudinal study of the progress of KB coverage. Our analysis shows a near-continuous improvement of two popular KBs, DBpedia and Wikidata, over the last 19 years, with little signs of flattening out or leveling off.
Robust Irregular Tensor Factorization and Completion for Temporal Health Data Analysis
Authors: Yifei Ren, Jian Lou, Li Xiong, Joyce C. Ho
Venue: CIKM
Year: 2020

Electronic health records (EHR) are often generated and collected across a large number of patients featuring distinctive medical conditions and clinical progress over a long period of time, which results in unaligned records along the time dimension. EHR is also prone to missing and erroneous data due to various practical reasons. Recently, PARAFAC2 has been re-popularized for successfully extracting meaningful medical concepts (phenotypes) from such temporal EHR by irregular tensor factorization. Despite recent advances, existing PARAFAC2 methods are unable to robustly handle erroneousness and missing data which are prevalent in clinical practice. We propose REPAIR, a Robust tEmporal PARAFAC2 method for IRregular tensor factorization and completion method, to complete an irregular tensor and extract phenotypes in the presence of missing and erroneous values. To achieve this, REPAIR designs a new effective low-rank regularization function for PARAFAC2 to handle missing and erroneous entries, which has not been explored for irregular tensors before. In addition, the optimization of REPAIR allows it to enjoy the same computational scalability and incorporate a variety of constraints as the state-of-the-art PARAFAC2 method for efficient and meaningful phenotype extraction. We evaluate REPAIR on two real temporal EHR datasets to verify its robustness in tensor factorization against various missing and outlier conditions. Furthermore, we conduct two case studies to demonstrate that REPAIR is able to extract meaningful and useful phenotypes from such corrupted temporal EHR. Our implementation is publicly available https://github.com/Emory-AIMS/Repair.
A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?
Authors: Julian Risch, Ralf Krestel
Venue: CIKM
Year: 2020

The comment sections of online news platforms are an important space to indulge in political conversations and to discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists directly, e.g., with questions, recommendations for future topics, thanks and appreciation, or article corrections. However, the sheer number of comments makes it infeasible for journalists to follow discussions around their articles in extenso. A better understanding of this data could support journalists in gaining insights into their audience and fostering engaging and respectful discussions. To this end, we present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction. As a baseline, we trained a neural network model with the help of a pair-wise comment ranking task. Our experiment reveals the challenges of this task and we outline promising paths for future work. The data and our code are available for research purposes from: https://hpi.de/naumann/projects/repeatability/text-mining.html
Expert-in-the-loop AI for Polymer Discovery
Authors: Petar Ristoski, Dmitry Yu. Zubarev, Anna Lisa Gentile, Nathaniel Park, Daniel Sanders, Daniel Gruhl, Linda Kato, Steve Welch
Venue: CIKM
Year: 2020

The use of AI in knowledge dense domains, e.g., chemistry, medicine, biology, etc. - is extremely promising, but often suffers from slow deployment and adaptation to different tasks. We propose a methodology to quickly capture the intent and expertise of a domain expert in order to train personalized AI models for specific tasks. Specifically we focus on the domain of polymer materials design and discovery: it often takes 10 years or more to design, synthesize, test, and introduce a new polymer material into the market. One way to accelerate up the design of polymer materials is through the use of computational methods to design the material, such as combinatorial screening, generative models, inverse design, etc. The drawback of these methods is that they generate a large number of candidates for new molecules, which then need to be manually reviewed by subject matter experts who select only a dozen for further investigation. Our solution is a human-in-the-loop methodology where we rank the candidates according to a utility function that is learned via the continued interaction with the subject matter experts, but which is also constrained by specific chemical knowledge. We prove the viability of our proposed methodology in a polymer production lab and we (i) evaluate against datasets of polymers previously produced in the lab as well as (ii) producing several novel materials that are undergoing experimental development, and (iii) quantitatively show that standard synthetic accessibility scores do not inform about patterns of SME decisions.
An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening
Authors: José F. Rodrigues Jr., Jean Louis Pépin, Lorraine Goeuriot, Sihem Amer-Yahia
Venue: CIKM
Year: 2020

The identification of Obstructive Sleep Apnea (OSA) relies on laborious and expensive polysomnography (PSG) exams. However, it is known that other factors, easier to measure, can be good indicators of OSA and its severity. In this work, we extensively investigate the use of Machine Learning techniques in the task of determining which factors are more revealing with respect to OSA along with a discussion of the challenges to perform such a task. We ran extensive experiments over 1,042 patients from the Centre Hospitalier Universitaire of the city of Grenoble, France. The data included ordinary clinical information, and PSG results as baseline. We employed data preparation techniques including cleaning of outliers, imputation of missing values, and synthetic data generation. Following, we performed an exhaustive attribute selection scheme to find the most representative features. We found that the prediction of OSA depends largely on variables related to age, body mass, and sleep habits more than the ones related to alcoholism, tabagism, and depression. Next, we tested 60 regression/classification algorithms to predict the Apnea-Hypopnea Index (AHI), and the AHI-based severity of OSA. We achieved performances significantly superior to the state of the art both for AHI regression and classification. Our results can benefit the development of tools for the automatic screening of patients who should go through polysomnography and further treatments of OSA -- currently, our work in under consideration for production by the Centre Hospitalier Universitaire of Grenoble. Our thorough methodology enables experimental reproducibility on similar OSA-detection problems, and more generally, on other problems with similar data models.
Computing and Illustrating Query Rewritings on Path Views with Binding Patterns
Authors: Julien Romero, Nicoleta Preda, Antoine Amarilli, Fabian M. Suchanek
Venue: CIKM
Year: 2020

In this system demonstration, we study views with binding patterns, which are a formalization of REST Web services. Such views are database queries that can be evaluated using the service, but only if values for the input variables are provided. We investigate how to use such views to answer a complex user query, by rewriting it as an execution plan, i.e., an orchestration of calls to the views. In general, it is undecidable to determine whether a given user query can be answered with the available views. In this demo, we illustrate a particular scenario studied in our earlier work [11], where the problem is not only decidable but has a particularly intuitive graphical solution. Our demo allows users to play with views defined by real Web services, and to animate the construction of execution plans visually.
ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
Authors: Anurag Roy, Vinay Kumar Verma, Kripabandhu Ghosh, Saptarshi Ghosh
Venue: CIKM
Year: 2020

Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.g., text) to the mode of the documents (e.g., images) from a given training set. Such a setup assumes that the training set contains an exhaustive representation of all possible classes of queries. In reality, a retrieval model may need to be deployed on previously unseen classes, which implies a zero-shot IR setup. In this paper, we propose a novel GAN-based model for zero-shot text to image retrieval. When given a textual description as the query, our model can retrieve relevant images in a zero-shot setup. The proposed model is trained using an Expectation-Maximization framework. Experiments on multiple benchmark datasets show that our proposed model comfortably outperforms several state-of-the-art zero-shot text to image retrieval models, as well as zero-shot classification and hashing models suitably used for retrieval.
Little Ball of Fur: A Python Library for Graph Sampling
Authors: Benedek Rozemberczki, Oliver Kiss, Rik Sarkar
Venue: CIKM
Year: 2020

Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploration-based network sampling techniques accessible to a large number of professionals, researchers, and students in a single streamlined framework. We created this framework with a focus on a coherent application public interface which has a convenient design, generic input data requirements, and reasonable baseline settings of algorithms. Here we overview these design foundations of the framework in detail with illustrative code snippets. We show the practical usability of the library by estimating various global statistics of social networks and web graphs. Experiments demonstrate that Little Ball of Fur can speed up node and whole graph embedding techniques considerably with mildly deteriorating the predictive value of distilled features.