OpenRaiser/Intern-Atlas huggingface dataset:
Teaching Artificial Agents to Understand Language by Modelling Reward
Authors: Edward Grefenstette
Venue: CIKM
Year: 2018

Recent progress in Deep Reinforcement Learning has shown that agents can be taught complex behaviour and solve difficult tasks, such as playing video games from pixel observations, or mastering the game of Go without observing human games, with relatively little prior information. Building on these successes, researchers such as Hermann and colleagues have sought to apply these methods to teach - in simulation - agents to complete a variety of tasks specified by combinatorially rich instruction languages. In this talk, we discuss some of these highlights and some of the limitations which inhibit scalability of such approaches to more complex instruction languages (including natural language). Following this, we introduce a new approach, inspired by recent work in adversarial reward modelling, which constitutes a first step towards scaling instruction-conditional agent training to "real world" language, unlocking the possibility of applying these techniques within a wide range of industrial applications.
Shared Embedding Based Neural Networks for Knowledge Graph Completion
Authors: Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng
Venue: CIKM
Year: 2018

Knowledge Graphs (KGs) have facilitated many real-world applications (e.g., vertical search and intelligent question answering). However, they are usually incomplete, which affects the performance of such KG based applications. To alleviate this problem, a number of Knowledge Graph Completion (KGC) methods have been developed to predict those implicit triples. Tensor/matrix based methods and translation based methods have attracted great attention for a long time. Recently, neural network has been introduced into KGC due to its extensive superiority in many fields (e.g., natural language processing and computer vision), and achieves promising results. In this paper, we propose a Shared Embedding based Neural Network (SENN) model for KGC. It integrates the prediction tasks of head entities, relations and tail entities into a neural network based framework with shared embeddings of entities and relations, while explicitly considering the differences among these prediction tasks. Moreover, we propose an adaptively weighted loss mechanism, which dynamically adjusts the weights of losses according to the mapping properties of relations, and the prediction tasks. Since relation prediction usually performs better than head and tail entity predictions, we further extend SENN to SENN+ by employing it to assist head and tail entity predictions. Experiments on benchmark datasets validate the effectiveness and merits of the proposed SENN and SENN+ methods. The shared embeddings and the adaptively weighted loss mechanism are also testified to be effective.
Rumor Detection with Hierarchical Social Attention Network
Authors: Han Guo, Juan Cao, Yazi Zhang, Junbo Guo, Jintao Li
Venue: CIKM
Year: 2018

Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.
Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information
Authors: Muhan Guo, Rui Zhang, Feiping Nie, Xuelong Li
Venue: CIKM
Year: 2018

As one of the most widely used clustering techniques, the fuzzy K-Means (also called FKM or FCM) assigns every data point to each cluster with a certain degree of membership. However, conventional FKM approach relies on the square data fitting term which is not robust to data outliers and ignores the prior information, which leads to unsatisfactory clustering results. In this paper, we present a novel and robust fuzzy K-Means clustering algorithm, namely Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information. The proposed method combines fuzzy K-Means with nonnegative spectral clustering into a unified model, and further takes the advantage of the prior knowledge of data pairs such that the quality of similarity graph is enhanced and the clustering performance is effectively improved. Besides, the ℓ2,1-norm loss function is adopted in the objective function, which achieves better robustness to outliers. Last, experimental results on benchmark datasets verify the effectiveness and superiority of the proposed clustering method.
Impact of Domain and User's Learning Phase on Task and Session Identification in Smart Speaker Intelligent Assistants
Authors: Seyyed Hadi Hashemi, Kyle Williams, Ahmed El Kholy, Imed Zitouni, Paul A. Crook
Venue: CIKM
Year: 2018

Task and session identification is a key element of system evaluation and user behavior modeling in Intelligent Assistant (IA) systems. However, identifying task and sessions for IAs is challenging due to the multi-task nature of IAs and the differences in the ways they are used on different platforms, such as smart-phones, cars, and smart speakers. Furthermore, usage behavior may differ among users depending on their expertise with the system and the tasks they are interested in performing. In this study, we investigate how to identify tasks and sessions in IAs given these differences. To do this, we analyze data based on the interaction logs of two IAs integrated with smart-speakers. We fit Gaussian Mixture Models to estimate task and session boundaries and show how a model with 3 components models user interactivity time better than a model with 2 components. We then show how session boundaries differ for users depending on whether they are in a learning-phase or not. Finally, we study how user inter-activity times differs depending on the task that the user is trying to perform. Our findings show that there is no single task or session boundary that can be used for IA evaluation. Instead, these boundaries are influenced by the experience of the user and the task they are trying to perform. Our findings have implications for the study and evaluation of Intelligent Agent Systems.
REGAL: Representation Learning-based Graph Alignment
Authors: Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
Venue: CIKM
Year: 2018

Problems involving multiple networks are prevalent in many scientific and other domains. In particular, network alignment, or the task of identifying corresponding nodes in different networks, has applications across the social and natural sciences. Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs. Within REGAL we devise xNetMF, an elegant and principled node embedding formulation that uniquely generalizes to multi-network problems. Our results demonstrate the utility and promise of unsupervised representation learning-based network alignment in terms of both speed and accuracy. REGAL runs up to 30x faster in the representation learning stage than comparable methods, outperforms existing network alignment methods by 20 to 30% accuracy on average, and scales to networks with millions of nodes each.
Knowledge Representation as Linked Data: Tutorial
Authors: Joachim Van Herwegen, Pieter Heyvaert, Ruben Taelman, Ben De Meester, Anastasia Dimou
Venue: CIKM
Year: 2018

The process of extracting, structuring, and organizing knowledge requires processing large and originally heterogeneous data sources. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on how to semantically annotate data, and generate and publish Linked Data. We introduce [R2]RML languages to generate Linked Data. We also show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish Linked Data on their own.
W2E: A Worldwide-Event Benchmark Dataset for Topic Detection and Tracking
Authors: Tuan-Anh Hoang, Khoi Duy Vo, Wolfgang Nejdl
Venue: CIKM
Year: 2018

Topic detection and tracking in document streams is a critical task in many important applications, hence has been attracting research interest in recent decades. With the large size of data streams, there have been a number of works from different approaches that propose automatic methods for the task. However, there is only a few small benchmark datasets that are publicly available for evaluating the proposed methods. The lack of large datasets with fine-grained groundtruth implicitly restrains the development of more advanced methods. In this work, we address this issue by collecting and publishing W2E - a large dataset consisting of news articles from more than 50 prominent mass media channels worldwide. The articles cover a large set of popular events within a full year. W2E is more than 15 times larger than TREC's TDT2 dataset, which is widely used in prior work. We further conduct exploratory analysis to examine the dynamics and diversity of W2E and propose potential uses of the dataset in other research.
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
Authors: Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh
Venue: CIKM
Year: 2018

With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR for improving quality of care. Disease diagnosis due to its sensitive nature, huge costs of error, and complexity has become an increasingly important focus of research in past years. Existing studies model EHR by capturing co-occurrence of clinical events to learn their latent embeddings. However, relations among clinical events carry various semantics and contribute differently to disease diagnosis which gives precedence to a more advanced modeling of heterogeneous data types and relations in EHR data than existing solutions. To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis. Our modeling approach allows for straightforward handling of missing values and heterogeneity of data. HeteroMed exploits metapaths to capture higher level and semantically important relations contributing to disease diagnosis. Furthermore, it employs a joint embedding framework to tailor clinical event representations to the disease diagnosis goal. To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis. Experimental results of our study show superior performance of HeteroMed compared to prior methods in prediction of exact diagnosis codes and general disease cohorts. Moreover, HeteroMed outperforms baseline models in capturing similarities of clinical events which are examined qualitatively through case studies.
A Fast Linear Computational Framework for User Action Prediction in Tencent MyApp
Authors: Yaochen Hu, Di Niu, Jianming Yang
Venue: CIKM
Year: 2018

User action modeling and prediction has long been a topic of importance to recommender systems and user profiling. The quality of the model or accuracy of prediction plays a vital role in related applications like recommendation, advertisement displaying, searching, etc. For large scale systems with a massive number of users, beside the pure prediction performance, there are other practical factors like training and prediction latency, memory overhead, that must be optimized to ensure smooth operation of the system. We propose a fast linear computational framework to handle a vast number of second order crossed features with dimensionality reduction. By leveraging the training and serving system architecture, we shift heavy calculation burden from online serving to offline preprocessing, at the cost of a reasonable amount of memory overhead. The experiments on a 15-day data trace from Tencent MyApp shows that our proposed framework can achieve comparable prediction performance to much complex models like the field-aware factorization machine (FFM) while being served in 2 ms with a reasonable amount of memory overhead.
Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network
Authors: Binbin Hu, Chuan Shi, Wayne Xin Zhao, Tianchi Yang
Venue: CIKM
Year: 2018

Since heterogeneous information network (HIN) is able to integrate complex information and contain rich semantics, there is a surge of HIN based recommendation in recent years. Although existing methods have achieved performance improvement to some extent, they still face the following problems: how to extensively exploit and comprehensively explore the local and global information in HIN for recommendation. To address these issues, we propose a unified model LGRec to fuse local and global information for top-N recommendation in HIN. We firstly model most informative local neighbor information for users and items respectively with a co-attention mechanism. In addition, our model learns effective relation representations between users and items to capture rich information in HIN by optimizing a multi-label classification problem. Finally, we combine the two parts into an unified model for top-N recommendation. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed model.
Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range
Authors: Jun-Gi Jang, Dongjin Choi, Jinhong Jung, U Kang
Venue: CIKM
Year: 2018

Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi-infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range. In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15x faster, and requires 15x less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.
RIN: Reformulation Inference Network for Context-Aware Query Suggestion
Authors: Jyun-Yu Jiang, Wei Wang
Venue: CIKM
Year: 2018

Search engine users always endeavor to reformulate queries during search sessions for articulating their information needs because it is not always easy to articulate the search intents. To further ameliorate the reformulation process, search engines may provide some query suggestions based on previous queries. In this paper, we propose Reformulation Inference Network (RIN) to learn how users reformulate queries, thereby benefiting context-aware query suggestion. Instead of categorizing reformulations into predefined patterns, we represent queries and reformulations in a homomorphic hidden space through heterogeneous network embedding. To capture the structure of the session context, a recurrent neural network (RNN) with the attention mechanism is employed to encode the search session by reading the homomorphic query and reformulation embeddings. It enables the model to explicitly captures the former reformulation for each query in the search session and directly learn user reformulation behaviors, from which query suggestion may benefit as shown in previous studies. To generate query suggestions, a binary classifier and an RNN-based decoder are introduced as the query discriminator and the query generator. Inspired by the intuition that model accurately predicting the next reformulation can also correctly infer the next intended query, a reformulation inferencer is then designed for inferring the next reformulation in the latent space of homomorphic embeddings. Therefore, both question suggestion and reformulation prediction can be simultaneously optimized by multi-task learning. Extensive experiments are conducted on publicly available AOL search engine logs. The experimental results demonstrate that RIN outperforms competitive baselines across various situations for both discriminative and generative tasks of context-aware query suggestion.
DEVES: Interactive Signal Analytics for Drug Safety
Authors: Tabassum Kakar, Xiao Qin, Andrew Schade, Brian McCarthy, Huy Quoc Tran, Brian Zylich, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, Suranjan De
Venue: CIKM
Year: 2018

Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.
Ontop-temporal: A Tool for Ontology-based Query Answering over Temporal Data
Authors: Elem Güzel Kalayci, Guohui Xiao, Vladislav Ryzhikov, Tahir Emre Kalayci, Diego Calvanese
Venue: CIKM
Year: 2018

We present Ontop-temporal, an extension of the ontology-based data access system Ontop for query answering with temporal data and ontologies. Ontop is a system to answer SPARQL queries over various data stores, using standard R2RML mappings and an OWL2QL domain ontology to produce high-level conceptual views over the raw data. The Ontop-temporal extension is designed to handle timestamped log data, by additionally using (i) mappings supporting validity time specification, and (ii) rules based on metric temporal logic to define temporalised concepts. In this demo we present how Ontop-temporal can be used to facilitate the access to the MIMIC-III critical care unit dataset containing log data on hospital admissions, procedures, and diagnoses. We use the ICD9CM diagnoses ontology and temporal rules formalising the selection of patients for clinical trials taken from the clinicaltrials.gov database. We demonstrate how high-level queries can be answered by Ontop-temporal to identify patients eligible for the trials.
YourDigitalSelf: A Personal Digital Trace Integration Tool
Authors: Varvara Kalokyri, Alexander Borgida, Amélie Marian
Venue: CIKM
Year: 2018

Personal information is typically fragmented across multiple, heterogeneous, distributed sources and saved as small, heterogeneous data objects, or traces. The DigitalSelf project at Rutgers University focuses on developing tools and techniques to manage (organize, search, summarize, make inferences on and personalize) such heterogeneous collections of personal digital traces. We propose to demonstrate YourDigitalSelf, a mobile phone-based personal information organization application developed as part of the DigitalSelf project. The demonstration will use a sample user data set to show how several disparate data traces can be integrated and combined to create personal narratives, or coherent episodes, of the user's activities. Conference attendees will be given the option to install YourDigitalSelf on their own devices to interact with their own data.
X-Rank: Explainable Ranking in Complex Multi-Layered Networks
Authors: Jian Kang, Scott Freitas, Haichao Yu, Yinglong Xia, Nan Cao, Hanghang Tong
Venue: CIKM
Year: 2018

In this paper we present a web-based prototype for an explainable ranking algorithm in multi-layered networks, incorporating both network topology and knowledge information. While traditional ranking algorithms such as PageRank and HITS are important tools for exploring the underlying structure of networks, they have two fundamental limitations in their efforts to generate high accuracy rankings. First, they are primarily focused on network topology, leaving out additional sources of information (e.g. attributes, knowledge). Secondly, most algorithms do not provide explanations to the end-users on why the algorithm gives the specific ranking results, hindering the usability of the ranking information. We developed Xrank, an explainable ranking tool, to address these drawbacks. Empirical results indicate that our explainable ranking method not only improves ranking accuracy, but facilitates user understanding of the ranking by exploring the top influential elements in multi-layered networks. The web-based prototype (Xrank: http://www.x-rank.net) is currently online - we believe it will assist both researchers and practitioners looking to explore and exploit multi-layered network data.