OpenRaiser/Intern-Atlas huggingface dataset:
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Authors: Miao Fan, Yeqi Bai, Mingming Sun, Ping Li
Venue: CIKM
Year: 2019

Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest appearing in a free-text sentence. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation leaving a large proportion of unrecognizable long-tail relations due to insufficient labeled instances for training. In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. Specifically, we adopt the large-margin ProtoNet with fine-grained features, expecting they can generalize well on long-tail relations. Extensive experiments were conducted by FewRel, a large-scale supervised few-shot RC dataset, to evaluate our framework: LM-ProtoNet (FGF). The results demonstrate that it can achieve substantial improvements over many baseline approaches.
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
Authors: Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu
Venue: CIKM
Year: 2019

Betweenness centrality (BC) is a widely used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that pass through them. It is key to many valuable applications, including community detection and network dismantling. Computing BC scores on large networks is computationally challenging due to its high time complexity. Many sampling-based approximation algorithms have been proposed to speed up the estimation of BC. However, these methods still need considerable long running time on large-scale networks, and their results are sensitive to even small perturbation to the networks. In this paper, we focus on the efficient identification of top-k nodes with highest BC in a graph, which is an essential task to many network applications. Different from previous heuristic methods, we turn this task into a learning problem and design an encoder-decoder based framework as a solution. Specifically, the encoder leverages the network structure to represent each node as an embedding vector, which captures the important structural information of the node. The decoder transforms each embedding vector into a scalar, which identifies the relative rank of a node in terms of its BC. We use the pairwise ranking loss to train the model to identify the orders of nodes regarding their BC. By training on small-scale networks, the model is capable of assigning relative BC scores to nodes for much larger networks, and thus identifying the highly-ranked nodes. Experiments on both synthetic and real-world networks demonstrate that, compared to existing baselines, our model drastically speeds up the prediction without noticeable sacrifice in accuracy, and even outperforms the state-of-the-arts in terms of accuracy on several large real-world networks.
Fine-Grained Fuel Consumption Prediction
Authors: Chenguang Fang, Shaoxu Song, Zhiwei Chen, Acan Gui
Venue: CIKM
Year: 2019

The high costs and pollutant emissions of vehicles have raised the demand for reducing fuel consumption globally. The idea is to improve the operations of vehicles without losing the output power such that the engine speed and torque work with the minimum fuel consumption rate. It relies on the complete map of engine speed and torque to fuel consumption rate, known as the engine universal characteristic map. Unfortunately, such a map is often incomplete (fuel consumption rate not observed under most engine speed and torque combinations) and inconsistent (different fuel consumption rates observed under the same engine speed and torque combination). In this paper, we propose to predict the fine-grained fuel consumption rate of each engine speed and torque combination, by learning a model from the incomplete and inconsistent observation data. A novel FuelNet is designed based on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Deconvolution is employed to predict the incomplete fuel consumption rates, while the discriminator can successfully tolerate the inconsistent fuel consumption rate observations. Experiments show that our FuelNet outperforms the existing approaches in both imputing the incomplete and repairing the inconsistent fuel consumption rates. Remarkably, we deploy the predicted fine-grained fuel consumption rates in a mobile application to assist driving, and show that the fuel consumption can be reduced up to 12.8%.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Authors: Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan
Venue: CIKM
Year: 2019

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.
DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation
Authors: Tao-Yang Fu, Wang-Chien Lee
Venue: CIKM
Year: 2019

Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects:1) we propose to plot a path as a sequence of -generalized images"which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN)on image processing; 2) we design a novel two-dimensional CNN, namely PathCNN, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37% to 25.64% of mean absolute error (MAE) in multiple large-scale real-world datasets
Fast Random Forest Algorithm via Incremental Upper Bound
Authors: Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda
Venue: CIKM
Year: 2019

Random forest is an ensemble approach based on decision trees. It computes the best split in each node in terms of impurity reduction. However, the impurity computations incur high computation cost in its training process. This paper proposes F-forest, an efficient variant of random forest. It incrementally estimates upper bounds for scores that correspond to impurity reductions to find the best split. Since we can safely skip unnecessary computations, it can guarantee the same training result as the original approach. Experiments show that our approach is faster than state-of-the-art approaches.
On a Chatbot Conducting Virtual Dialogues
Authors: Boris Galitsky, Dmitry Ilvovsky
Venue: CIKM
Year: 2019

We present a demo of the chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts extracted and selected from the documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions are automatically generated for these answers.
Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition
Authors: Yunze Gao, Yingying Chen, Jinqiao Wang, Hanqing Lu
Venue: CIKM
Year: 2019

Scene text recognition has attracted rapidly increasing attention from the research community. Recent dominant approaches typically follow an attention-based encoder-decoder framework that uses a unidirectional decoder to perform decoding in a left-to-right manner, but ignoring equally important right-to-left grammar information. In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition. Firstly, the backward decoder performs decoding from right to left and generates the reverse language context. After that, the forward decoder simultaneously utilizes the visual context from image encoder and the reverse language context from backward decoder through two attention modules. In this way, the bidirectional decoders perform effective interaction to fully fuse the bidirectional grammar information and further improve the decoding quality. Besides, in order to relieve the adverse effect of noises, we devise a gated context mechanism to adaptively make use of the visual context and reverse language context. Extensive experiments on various challenging benchmarks demonstrate the effectiveness of our method.
Active Entity Recognition in Low Resource Settings
Authors: Ning Gao, Nikos Karampatziakis, Rahul Potharaju, Silviu Cucerzan
Venue: CIKM
Year: 2019

The task of Named Entity Recognition (NER) has been well studied under high-resource conditions (e.g., extracting named mentions of PERSON, ORGANIZATION and LOCATION from news articles). However, there are very few studies of the NER task for open-domain collections and in low-resource settings. We focus on NER for low-resource collections, in which any entity types of practical interest to the users of the system must be supported. We try to achieve this with a low cost of annotation of data from the target domain/collection. We propose an entity recognition framework that combines active learning and conditional random fields (CRF), and which provides the flexibility to define new entity types as needed by the users. Our experiments on a help & support corpus show that the system can achieve F1 measure of 0.77 by relying on only 100 manually-annotated sentences.
A Lossy Compression Method on Positional Index for Efficient and Effective Retrieval
Authors: Shuni Gao, Jipeng Liu, Xiaoguang Liu, Gang Wang
Venue: CIKM
Year: 2019

In query processing, incorporating proximity between query terms is beneficial for effective retrieval. However, it brings inevitable storage and computing costs by using positional data in inverted indexes. In this paper, we propose a lossy method for compressing term position data in the case of utilizing term proximity. Our method exploits clustering property of term occurrences, adaptively clusters the nearby occurrences, and replaces the clustered positions with a centralized value. Experimental results show that our adaptive method is competitive with respect to index size, ranking efficiency and effectiveness.
Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model
Authors: Congjie Gao, Yongjun Li, Jiaqi Yang
Venue: CIKM
Year: 2019

Recently, the fine-grained geolocalization of User-Generated Short Text (UGST) has become increasingly important. Existing methods can not make full use of the location information in the UGSTs. Besides, existing works only consider the importance of terms for all locations, but do not distinguish the importance of the same term in different locations. To solve these problems, we propose a fine-grained geolocalization method based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of non-geotagged UGST posted from each location and selecting k locations according to the top-k probabilities. Experiments on ground-truth datasets prove the superior performance of FGST-WP.
ReducE-Comm: Effective Inventory Reduction System for E-Commerce
Authors: Shay Gershtein, Tova Milo, Slava Novgorodov
Venue: CIKM
Year: 2019

Many e-commerce platforms serve as an intermediary between companies/manufacturers and consumers, receiving a commission per purchase. To increase revenue, such sites tend to offer a wide variety of items. However, in many situations a smaller subset of the items should be selected and offered for sale, e.g., when opening an express branch or expanding to a new region, or when maintenance costs become prohibitive and redundant items should be disposed of. In all these cases selecting a reduced inventory which covers most consumer needs is an important goal. In this demo we introduce ReducE-Comm - a highly parallelizable and scalable system that given a large set of items, a bound on the number of items that can be supported and information about consumer preferences/items relationships, allows to select a subset of the items which maximizes the likelihood of a purchase. Our system is interactive and facilitates real-time analysis, by providing detailed per-item impact statistics. We demonstrate the effectiveness of ReducE-Comm on real-world data and scenarios taken from a large e-commerce system, by interacting with the CIKM'19 audience who act as analysts aiming to intelligently reduce the inventory.
Session-based Search Behavior in Naturalistic Settings for Learning-related Tasks
Authors: Souvick Ghosh, Chirag Shah
Venue: CIKM
Year: 2019

In this research, we investigate the behavioral patterns exhibited in different search sessions as users attempt to complete search tasks of increasing cognitive complexity. The search tasks, which are exploratory in nature, have been designed using the Taxonomy of Educational Objectives, and are presented to the users hierarchically. We capture naturalistic search behavior of the users in real world (non-lab) setting using a Chrome browser plugin. The research analyzes the web log data of the users to assess if and how the web search behavior of the users changes over different search sessions. We also look at the different demographic factors like age and gender, educational factors like the academic background, read and write proficiency in English, and search skills to determine if these factors influence the web search behavior of the users. Our results indicate that search sessions have significant effects on the web search behavior of the users. Most of the web search behaviors differed significantly across search sessions. Of the secondary factors, gender showed significant effect on the query reformulations (measured using average edit distance between queries) and query length (measured using number of words per query) while year of study affected only the average query length. Search experience had significant effect on all the web search behaviors.
Detecting Malicious Accounts in Online Developer Communities Using Deep Learning
Authors: Qingyuan Gong, Jiayun Zhang, Yang Chen, Qi Li, Yu Xiao, Xin Wang, Pan Hui
Venue: CIKM
Year: 2019

Online developer communities like GitHub provide services such as distributed version control and task management, which allow a massive number of developers to collaborate online. However, the openness of the communities makes themselves vulnerable to different types of malicious attacks, since the attackers can easily join and interact with legitimate users. In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles as well as dynamic activity characteristics. On one hand, GitSec makes use of users' descriptive features from the profiles. On the other hand, GitSec processes users' dynamic behavioral data by constructing two user activity sequences and applying a parallel neural network design to deal with each of them, respectively. An attention mechanism is used to integrate the information generated by the parallel neural networks. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our extensive evaluations show that GitSec is an accurate detection system, with an F1-score of 0.922 and an AUC value of 0.940.
Unsupervised Concept Drift Detection with a Discriminative Classifier
Authors: Ömer Gözüaçik, Alican Büyükçakir, Hamed R. Bonab, Fazli Can
Venue: CIKM
Year: 2019

In data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature. Detecting and subsequently adapting to concept drifts yield more robust and better performing models. In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space. It is a simple method that can be used along with any existing classifier that does not intrinsically have a drift adaptation mechanism. We experiment on the most prevalent concept drift detectors using 8 datasets. The results demonstrate that D3 outperforms the baselines, yielding models with higher performances on both real-world and synthetic datasets.
LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning
Authors: Diandian Gu, Ziniu Hu, Shangchen Du, Yun Ma
Venue: CIKM
Year: 2019

Analyzing links among pages from different mobile apps is an important task of app analysis. Currently, most efforts of analyzing inter-app page links rely on static program analysis, which produces a lot of false positives, requiring significant manual effort to verify the links. To address the issue, in this paper, we propose LinkRadar, a data-driven approach to assisting the analysis of inter-app page links. Our key idea is to use dynamic program analysis to gather a set of actual inter-app page links, based on which we train a model to predict whether there exist links among pages from different apps to help verify the results of static program analysis. The challenge is that inter-app page links are hard to be triggered by dynamic program analysis, making it difficult to collect enough inter-app page links to train the model. Considering the similarity between intra-app page links and inter-app page links, we use transfer learning to deal with the data scarcity problem. Evaluation results show that LinkRadar is able to infer the inter-app page links with high accuracy.
ExplIQuE: Interactive Databases Exploration with SQL
Authors: Marie Le Guilly, Jean-Marc Petit, Vasile-Marian Scuturici, Ihab F. Ilyas
Venue: CIKM
Year: 2019

To help databases users who have just started learning SQL or are not familiar with their database, we propose ExplIQuE, an exploration interface with query extensions. Its purpose is to assist users to smoothly dive into data exploration, and to be able to express imprecise questions over their data. Indeed, such situations are more and more current with the increasing desire for users to get value out of their data. In this configuration, in addition to classic SQL querying possibilities, ExplIQuE offers the possibility to extend a given SQL query, by suggesting a set of possible selection predicates to add to the query, that aim at dividing the initial answer set to identify interesting exploration zones. In addition, ExplIQuE proposes some indicators to help the user in choosing its desire extension and in understanding her data, as well as interactive visualizations of the result set, in two dimensions revealed by PCA techniques. In this demonstration, we offer the audience the possibility to try the various functionalities of ExplIQuE by trying to express an imprecise question over a scientific database on bacterial colonies, through an iterative process. A video of the proposed demonstration is available at \urlhttps://youtu.be/oK8xWGCWj_A.
Learning-Based Methods with Human-in-the-Loop for Entity Resolution
Authors: Sairam Gurajada, Lucian Popa, Kun Qian, Prithviraj Sen
Venue: CIKM
Year: 2019

This tutorial is intended for researchers and practitioners working in the data integration area and, in particular, entity resolution (ER), which is a sub-area focused on linking entities across heterogeneous datasets. We outline the ideal requirements of modern ER systems: (1) capture domain knowledge via (minimal) human interaction, (2) provide as much automation as possible via machine learning techniques, and (3) achieve high explainability. We describe recent research trends towards bringing such ideal ER systems closer to reality. We begin with an overview of human-in-the-loop methods that are based on techniques such as crowdsourcing and active learning. We then dive into recent trends that involve deep learning techniques such as representation learning to automate feature engineering, and combinations of transfer and active learning to reduce the amount of user labels required. We also discuss how explainable AI relates to ER, and outline some of the recent advances towards explainable ER.
Tensor Decomposition-based Node Embedding
Authors: Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Rafal A. Angryk
Venue: CIKM
Year: 2019

In recent years, node embedding algorithms, which learn low dimensional vector representations for nodes in a graph, have been one of the key research interests of the graph mining community. The existing algorithms either rely on computationally expensive eigendecomposition of the large matrices, or require tuning of the word embedding-based hyperparameters as a result of representing the graph as a node sequence similar to the sentences in a document. Moreover, the latent features produced by these algorithms are hard to interpret. In this paper, we present Tensor Decomposition-based Node Embedding (TDNE), a novel model for learning node representations for arbitrary types of graphs: undirected, directed, and/or weighted. Our model preserves the local and global structural properties of a graph by constructing a third-order tensor using the k-step transition probability matrices and decomposing the tensor through CANDECOMP/PARAFAC (CP) decomposition in order to produce an interpretable, low dimensional vector space for the nodes. Our experimental evaluation using two well-known social network datasets proves TDNE to be interpretable with respect to the understandability of the feature space, and precise with respect to the network reconstruction.