OpenRaiser/Intern-Atlas huggingface dataset:
UWKGM: A Modular Platform for Knowledge Graph Management
Authors: Natthawut Kertkeidkachorn, Rungsiman Nararatwong, Ryutaro Ichise
Venue: CIKM
Year: 2020

A knowledge graph becomes a central data hub in the enterprise and the research communities. Nevertheless, the development of knowledge graphs is challenging due to the insufficient functionalities of knowledge graph management platforms. In this paper, we develop a knowledge graph management platform (UWKGM). This platform enables users to integrate arbitrary functionalities as RESTful API services in order to facilitate the knowledge graph development process. In the demonstration, we highlight the main features of UWKGM and its use cases on knowledge graph management tasks.
Optimization of Answer Set Programs for Consistent Query Answering by Means of First-Order Rewriting
Authors: Aziz Amezian El Khalfioui, Jonathan Joertz, Dorian Labeeuw, Gaëtan Staquet, Jef Wijsen
Venue: CIKM
Year: 2020

Consistent Query Answering (CQA) with respect to primary keys is the following problem. Given a database instance that is possibly inconsistent with respect to its primary key constraints, define a repair as an inclusion-maximal consistent subinstance. Given a Boolean query q, the problem CERTAINTY(q) takes a database instance as input, and asks whether q is true in every repair. For every Boolean conjunctive query q, the complement of CERTAINTY(q) can be straightforwardly implemented in Answer Set Programming (ASP) by means of a generate-and-test approach: first generate a repair, and then test whether it falsifies the query. Theoretical research has recently revealed that for every self-join-free Boolean conjunctive query q, the complexity class of CERTAINTY(q) is one of FO, L-complete, or coNP-complete. Faced with this complexity trichotomy, one can hypothesize that in practice, the full power of generate-and-test is a computational overkill when CERTAINTY(q) is in the low complexity classes FO or L. We investigate part of this hypothesis within the context of ASP, by asking the following question: whenever CERTAINTY(q) is in FO, does a dedicated first-order algorithm exhibit significant performance gains compared to a generic generate-and-test implementation? We first elaborate on the construction of such dedicated first-order algorithms in ASP, and then empirically address this question.
WebLens: Towards Interactive Large-scale Structured Data Profiling
Authors: Rituparna Khan, Michael N. Gubanov
Venue: CIKM
Year: 2020

Data profiling is a "set of statistical data analysis activities and processes to determine properties of a given dataset". Historically,most of the data profiling tasks were aimed at data. At scale, when a dataset has millions of tables, their meta-data (i.e. titles, attribute names and types) becomes abundant similar to data instances, and its profiling starts playing a vital role. Here we demonstrate our work on WebLens- an interactive, scalable metadata profiler for large-scale structured data. At its core is a new data structure - Metadata-profile, coupled with Machine/Deep-Learning models trained to construct it. It represents a meta-data summary of a specific real world object collected over millions of data sources. Such profiles significantly simplify access to large-scale structured datasets for both data scientists and end users. Finally, we performed a user study with 20 students and found WebLens trained models significantly outperform 20 people on the task of construction of metadata-profiles for 10 objects from different domains. For demonstration and evaluation we used a large-scale dataset of '15 Million relational English tables from the Web.
Selecting Influential Features by a Learnable Content-Aware Linear Threshold Model
Authors: Ansh Khurana, Alvis Logins, Panagiotis Karras
Venue: CIKM
Year: 2020

Consider a network in which items propagate in a manner determined by their inherent characteristics or features. How should we select such inherent content features of a message emanating from a given set of nodes, so as to engender high influence spread over the network? This influential feature set selection problem has received scarce attention, contrary to its dual, influential node set selection counterpart, which calls to select the initial adopter nodes from which a fixed message emanates, so as to reach high influence. However, the influential feature set selection problem arises in many practical settings, where initial adopters are given, while propagation depends on the perception of certain malleable message features. We study this problem for a diffusion governed by a content-aware linear threshold (CALT) model, by which, once the aggregate weight of influence on a node exceeds a randomly chosen threshold, the item goes through. We show that the influence spread function is not submodular, hence a greedy algorithm with approximation guarantees is inadmissible. We propose a method that learns the parameters of the CALT model and adapt the SimPath diffusion estimation method to build a heuristic for the influential feature selection problem. Our experimental study demonstrates the efficacy and efficiency of our technique over synthetic and real data.
Autonomous Predictive Modeling via Reinforcement Learning
Authors: Udayan Khurana, Horst Samulowitz
Venue: CIKM
Year: 2020

Building a robust predictive model requires an array of steps such as data imputation, feature transformations, estimator selection, hyper-parameter search, ensemble construction, amongst others. Due to this vast, complex and heterogeneous space of operations, off-the-shelf optimization methods offer infeasible solutions for realistic response time requirements. In practice, much of the predictive modeling process is conducted by experienced data scientists, who selectively make use of available tools. Over time, they develop an understanding of the behavior of operators, and perform serial decision making under uncertainty, colloquially referred to as educated guesswork. With an unprecedented demand for application of supervised machine learning, there is a call for solutions that automatically search for a suitable combination of operators across these tasks while minimize the modeling error. We introduce a novel system called APRL (Autonomous Predictive modeler via Reinforcement Learning), that uses past experience through reinforcement learning to optimize sequential decision making from within a set of diverse actions under a budget constraint. Our experiments demonstrate the superiority of the proposed approach over known AutoML systems that utilize Bayesian optimization or genetic algorithms.
Web Page Segmentation Revisited: Evaluation Framework and Dataset
Authors: Johannes Kiesel, Florian Kneist, Lars Meyer, Kristof Komlossy, Benno Stein, Martin Potthast
Venue: CIKM
Year: 2020

Each web page can be segmented into semantically coherent units that fulfill specific purposes. Though the task of automatic web page segmentation was introduced two decades ago, along with several applications in web content analysis, its foundations are still lacking. Specifically, the developed evaluation methods and datasets presume a certain downstream task, which led to a variety of incompatible datasets and evaluation methods. To address this shortcoming, we contribute two resources: (1) An evaluation framework which can be adjusted to downstream tasks by measuring the segmentation similarity regarding visual, structural, and textual elements, and which includes measures for annotator agreement, segmentation quality, and an algorithm for segmentation fusion. (2) The Webis-WebSeg-20 dataset, comprising 42,450~crowdsourced segmentations for 8,490~web pages, outranging existing sources by an order of magnitude. Our results help to better understand the "mental segmentation model'' of human annotators: Among other things we find that annotators mostly agree on segmentations for all kinds of web page elements (visual, structural, and textual). Disagreement exists mostly regarding the right level of granularity, indicating a general agreement on the visual structure of web pages.
A Human-in-the-Loop Approach to Malware Author Classification
Authors: Eujeanne Kim, Sung-Jun Park, Dong-Kyu Chae, Seokwoo Choi, Sang-Wook Kim
Venue: CIKM
Year: 2020

For these few decades malwares have been posing a major concern in the cyber security. Recently, a number of "author groups" have been generating lots of newmalwares by sharing source code within a group and exploiting evasive schemes such as polymorphism and metamorphism. This motivates us to study the problem of identifying the author group of a given malware, which would be able to work for not only blocking malwares but also legally punishing suspected malware authors. In this paper, we propose a human-machine collaborative approach for classifying author groups of malwares accurately. We also propose a visualization method for helping human experts to make the decision easily. We verify the superiority of our framework through extensive experiments using real-world malware data.
A Synopses Data Engine for Interactive Extreme-Scale Analytics
Authors: Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis
Venue: CIKM
Year: 2020

We detail the novel architecture of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards interactive analytics at scale. Our SDE, built on top of Apache Flink, has a unique design that supports a very wide variety of synopses, allows for dynamically adding new functionality to it at runtime, and introduces a synopsis-as-a-service paradigm to enable various types of scalability.
Magellan: A Personalized Travel Recommendation System Using Transaction Data
Authors: Konik Kothari, Dhruv Gelda, Wei Zhang, Hao Yang
Venue: CIKM
Year: 2020

We present Magellan - a personalized travel recommendation system that is built entirely from card transaction data. The data logs contain extensive metadata for each transaction between a user and a merchant. We describe the procedure employed to extract travel itineraries from such transaction data. Unlike traditional approaches, we formulate the recommendation problem into two steps: (1) predict coarse granularity information such as location and category of the next merchant; and (2) provide fine granularity individual merchant recommendations based on the predicted location and category. The breakdown helps us build a scalable recommendation system. We propose a quadtree-based algorithm that provides an adaptive spatial resolution for the location classes in our first step while also reducing the class-imbalance across various location labels. Finally, we propose a novel neural architecture, SoLEmNet, that implicitly learns the inherent class label hierarchy and achieves a higher performance on our dataset compared to previous baselines.
NASE: : Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
Authors: Xiaoyu Kou, Bingfeng Luo, Huang Hu, Yan Zhang
Venue: CIKM
Year: 2020

Link prediction is the task of predicting missing connections between entities in the knowledge graph (KG). While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets. Due to the diversity and complexity nature of the real-world KGs, it is inherently difficult to design a model that fits all datasets well. To address this issue, previous work has tried to use Automated Machine Learning (AutoML) to search for the best model for a given dataset. However, their search space is limited only to bilinear model families. In this paper, we propose a novel Neural Architecture Search (NAS) framework for the link prediction task. First, the embeddings of the input triplet are refined by the Representation Search Module. Then, the prediction score is searched within the Score Function Search Module. This framework entails a more general search space, which enables us to take advantage of several mainstream model families, and thus it can potentially achieve better performance. We relax the search space to be continuous so that the architecture can be optimized efficiently using gradient-based search strategies. Experimental results on several benchmark datasets demonstrate the effectiveness of our method compared with several state-of-the-art approaches.
Deriving Geolocations in Wikipedia
Authors: Amir Krause, Sara Cohen
Venue: CIKM
Year: 2020

We study the problem of deriving geolocations for Wikipedia pages. To this end, we introduce a general four-step process to location derivation, and consider different instantiations of this process, leveraging both textual and categorical data. Extensive experimentation shows that our methods provide good precision-recall trade-offs and improvements over text-only methods. Hence, our system can be used to augment the geographic information of Wikipedia, and to enable more effective geographic information retrieval.
Extracting N-ary Facts from Wikipedia Table Clusters
Authors: Benno Kruit, Peter Boncz, Jacopo Urbani
Venue: CIKM
Year: 2020

Tables in Wikipedia articles contain a wealth of knowledge that would be useful for many applications if it were structured in a more coherent, queryable form. An important problem is that many of such tables contain the same type of knowledge, but have different layouts and/or schemata. Moreover, some tables refer to entities that we can link to Knowledge Bases (KBs), while others do not. Finally, some tables express entity-attribute relations, while others contain more complex n-ary relations. We propose a novel knowledge extraction technique that tackles these problems. Our method first transforms and clusters similar tables into fewer unified ones to overcome the problem of table diversity. Then, the unified tables are linked to the KB so that knowledge about popular entities propagates to the unpopular ones. Finally, our method applies a technique that relies on functional dependencies to judiciously interpret the table and extract n-ary relations. Our experiments over 1.5M Wikipedia tables show that our clustering can group many semantically similar tables. This leads to the extraction of many novel n-ary relations.
Ranking Clarification Questions via Natural Language Inference
Authors: Vaibhav Kumar, Vikas Raunak, Jamie Callan
Venue: CIKM
Year: 2020

Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems. Such interactions could help in filling information gaps for better machine comprehension of the query. For the task of ranking clarification questions, we hypothesize that determining whether a clarification question pertains to a missing entry in a given post (on QA forums such as StackExchange) could be considered as a special case of Natural Language Inference (NLI), where both the post and the most relevant clarification question point to a shared latent piece of information or context. We validate this hypothesis by incorporating representations from a Siamese BERT model fine-tuned on NLI and Multi-NLI datasets into our models and demonstrate that our best performing model obtains a relative performance improvement of 40 percent and 60 percent respectively (on the key metric of Precision@1), over the state-of-the-art baseline(s) on the two evaluation sets of the StackExchange dataset, thereby, significantly surpassing the state-of-the-art.
ART (Attractive Recommendation Tailor): How the Diversity of Product Recommendations Affects Customer Purchase Preference in Fashion Industry?
Authors: Hyokmin Kwon, Jaeho Han, Kyungsik Han
Venue: CIKM
Year: 2020

This study examines the impact of the 'diversity' of product recommendations on the 'preference' of a customer, using online/offline data from a leading fashion company. First, through interviews with fashion professionals, we categorized the characteristics of customers into four types - gift, coordinator, carry-over, and trendsetter. Then, using a hybrid filtering method, we increased the accuracy and diversity of recommended products. We derived 13 salient features that reflect customer behavior based on the Purchase Funnel model and built a classification model that predicts a customer's preference rates. Second, we conducted two large-scale user tests with 20,000 real customers to verify the effectiveness of our recommendation system. Study results empirically demonstrated the importance of diversity of recommended products. The more diverse the product recommendations were, the higher the purchase rate, the average purchase amount, and the cross purchase rate were observed. In addition, we tracked the customers? purchase for two months after the user tests and found that diverse product exposure positively influenced customer retention (e.g., repurchase rate, amount).
MERL: Multi-View Edge Representation Learning in Social Networks
Authors: Yi-Yu Lai, Jennifer Neville
Venue: CIKM
Year: 2020

Network embedding models aim to learn low-dimensional representations for nodes and/or edges in graphs. For social networks, learning edge representations is especially beneficial as we need to describe or explain the relationships, activities, and interactions between users. Existing approaches that learn stand-alone node embeddings, and represent edges as pairs of node embeddings, are limited in their applicability because nodes participate in multiple relationships, which should be considered. In addition, social networks often contain multiple types of edges, which yields multi-view contexts that need to be considered in the representation. In this paper, we propose a new methodology, MERL, that (1) captures asymmetry in multiple views by learning well-defined edge representations that are responsive to the difference between the source and destination node roles, and (2) incorporates textual communications to identify multiple source of social signals (e.g. strength and affinity) that moderate the impact of different views between users. Our experiments show that MERL outperforms alternative state-of-the-art embedding models on link prediction and multilabel classification tasks across multiple views in social network datasets. We further analyze the learned embeddings of MERL and demonstrate they are more correlated with the existence of view-based edges compared to previous methods.
Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
Authors: Hsu-Chao Lai, Jui-Yi Tsai, Hong-Han Shuai, Jiun-Long Huang, Wang-Chien Lee, De-Nian Yang
Venue: CIKM
Year: 2020

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.
Rethinking Operators Placement of Stream Data Application in the Edge
Authors: Thomas Lambert, David Guyon, Shadi Ibrahim
Venue: CIKM
Year: 2020

Maximum Sustainable Throughput (MST) refers to the amount of data that a Data Stream Processing (DSP) system can ingest while keeping stable performance. It has been acknowledged as an accurate metric to evaluate the performance of stream data processing. Yet, existing operators placements continue to focus on latency and throughput, not MST, as main performance objective when deploying stream data applications in the Edge. In this paper, we argue that MST should be used as an optimization objective when placing operators. This is specially important in the Edge, where network bandwidth and data streams are highly dynamic. We demonstrate that through the design and evaluation of a MST-driven operators placement (based on constraint programming) for stream data applications. Through simulations, we show how existing placement strategies that target overall communications reduction often fail to keep up with the rate of data streams. Importantly, the constraint programming-based operators placement is able to sustain up to 5x increased data ingestion compared to baseline strategies.
An Index Advisor Using Deep Reinforcement Learning
Authors: Hai Lan, Zhifeng Bao, Yuwei Peng
Venue: CIKM
Year: 2020

We study the problem of index selection to maximize the workload performance, which is critical to database systems. In contrast to existing methods, we seamlessly integrate index recommendation rules and deep reinforcement learning, such that we can recommend single-attribute and multi-attribute indexes together for complex queries and meanwhile support multiple-index access to a table. Specifically, we first propose five heuristic rules to generate the index candidates. Then, we formulate the index selection problem as a reinforcement learning task and employ Deep Q Network (DQN) on it. Using the heuristic rules can significantly reduce the dimensions of the action space and state space in reinforcement learning. With the neural network used in DQN, we can model the interactions between indexes better than previous methods. We conduct experiments on various workloads to show its superiority.
The Newspaper Navigator Dataset: Extracting Headlines and Visual Content from 16 Million Historic Newspaper Pages in Chronicling America
Authors: Benjamin Charles Germain Lee, Jaime Mears, Eileen Jakeway, Meghan Ferriter, Chris Adams, Nathan Yarasavage, Deborah Thomas, Kate Zwaard, Daniel S. Weld
Venue: CIKM
Year: 2020

Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic American newspapers. Over 16 million pages have been digitized to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.