OpenRaiser/Intern-Atlas huggingface dataset:
First Workshop on Knowledge Base Construction, Mining and Reasoning
Authors: Xiang Ren, Craig A. Knoblock, William Yang Wang, Yu Su
Venue: WSDM
Year: 2018

1. Motivation and Goals. e success of data mining and search technologies is largely aributed to the ecient and eective analysis of structured data. e construction of a well-structured, machine-actionable database from raw data sources is oen the premise of consequent applications. Meanwhile, the ability of mining and reasoning over such constructed databases is at the core of powering various downstream applications on web and mobile devices. Recently, we have witnessed a signicant amount of interests in building large-scale knowledge bases (KBs) from massive, unstructured data sources (e.g., Wikipedia-based methods such as DBpedia [9], YAGO [19], Wikidata [22], automated systems like Snowball [1], KnowItAll [5], NELL [4] and DeepDive [15], and opendomain approaches like Open IE [2] and Universal Schema [14]); as well as mining and reasoning over such knowledge bases to empower a wide variety of intelligent services, including question answering [6], recommender systems [3] and semantic search [8]. Automated construction, mining and reasoning of the knowledge bases have become possible as research advances in many related areas such as information extraction, natural language processing, data mining, search, machine learning, databases and data integration. However, there are still substantial scientic and engineering challenges in advancing and integrating such relevant methodologies. e goal of this proposed workshop is to gather together leading experts from industry and academia to share their visions about the eld, discuss latest research results, and exchange exciting ideas. With a focus on invited talks and position papers, the workshop aims to provide a vivid forum of discussion about knowledge base-related research. 2. Relevance to WSDM. Knowledge base construction, mining and reasoning is closely related to a wide variety of applications in WSDM, including web search, question answering, and recommender systems. Building a high-quality knowledge base from
FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks
Authors: Mojtaba Rezvani, Qing Wang, Weifa Liang
Venue: WSDM
Year: 2018

Vertices in a real-world social network can be grouped into densely connected communities that are sparsely connected to other groups. Moreover, these communities can be partitioned into successively more cohesive communities. Despite an ever-growing pile of research on hierarchical community detection, existing methods suffer from either inefficiency or inappropriate modeling. Yet, some cut-based approaches have shown to be effective in finding communities without hierarchies. In this paper, we study the hierarchical community detection problem in large networks and show that it is NP-hard. We then propose an efficient algorithm based on edge-cuts to identify the hierarchy of communities. Since communities at lower levels of the hierarchy are denser than the higher levels, we leverage a fast network sparsification technique to enhance the running time of the algorithm. We further propose a randomized approximation algorithm for information centrality of networks. We finally evaluate the performance of the proposed algorithms by conducting extensive experiments using real datasets. Our experimental results show that the proposed algorithms are promising and outperform the state-of-the-art algorithms by several orders of magnitude.
From Search to Research: Direct Answers, Perspectives and Dialog
Authors: Harry Shum
Venue: WSDM
Year: 2018

Advances in artificial intelligence have improved machine understanding of speech, images, and natural language. This in turn has allowed us to greatly enhance the intelligence of products such as Bing and Cortana. This keynote describes our continuing journey beyond keyword-driven systems, into dialog and intelligent agent functionality, helping our users "research more, search less".
Modeling Time to Open of Emails with a Latent State for User Engagement Level
Authors: Moumita Sinha, Vishwa Vinay, Harvineet Singh
Venue: WSDM
Year: 2018

Email messages have been an important mode of communication, not only for work, but also for social interactions and marketing. When messages have time sensitive information, it becomes relevant for the sender to know what is the expected time within which the email will be read by the recipient. In this paper we use a survival analysis framework to predict the time to open an email once it has been received. We use the Cox Proportional Hazards (CoxPH) model that offers a way to combine various features that might affect the event of opening an email. As an extension, we also apply a mixture model (MM) approach to CoxPH that distinguishes between recipients, based on a latent state of how prone to opening the messages each individual is. We compare our approach with standard classification and regression models. While the classification model provides predictions on the likelihood of an email being opened, the regression model provides prediction of the real-valued time to open. The use of survival analysis based methods allows us to jointly model both the open event as well as the time-to-open. We experimented on a large real-world dataset of marketing emails sent in a 3-month time duration. The mixture model achieves the best accuracy on our data where a high proportion of email messages go unopened.
Shortcutting Label Propagation for Distributed Connected Components
Authors: Stergios Stergiou, Dipen Rughwani, Kostas Tsioutsiouliklis
Venue: WSDM
Year: 2018

Connected Components is a fundamental graph mining problem that has been studied for the PRAM, MapReduce and BSP models. We present a simple CC algorithm for BSP that does not mutate the graph, converges in O(log n) supersteps and scales to graphs of trillions of edges.
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
Authors: Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li
Venue: WSDM
Year: 2018

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.
sSketch: A Scalable Sketching Technique for PCA in the Cloud
Authors: Md. Mehrab Tanjim, Muhammad Abdullah Adnan
Venue: WSDM
Year: 2018

Multidimensional data appear frequently in many web-related applications, e.g., product ratings, the bag-of-words representation of web pages, etc. Principal Component Analysis (PCA) has been widely used for discovering patterns in relationships among entities in multidimensional data. However, existing algorithms for PCA have limited scalability since they explicitly materialize intermediate data, whose size rapidly grows as the dimension increases. To avoid scalability issues, we propose sSketch, a scalable sketching technique for PCA that employs several optimization ideas, such as mean propagation, efficient sparse matrix operations, and effective job consolidation to minimize intermediate data. Using sSketch, we also provide two other scalable methods for deriving singular value and 2-norm of reconstruction error, both of which are used for data analysis purpose. We provide our implementation on popular Spark framework for distributed platform. We compare our method against state-of-the-art library functions available for distributed settings, namely MLlib-PCA and Mahout-PCA with real big datasets. Our experiments show that our method outperforms both of them by a wide margin. To encourage reproducibility, the source code of sSketch is made publicly available at \hrefhttps://github.com/DataMiningResearch/sSketch https://github.com/DataMiningResearch/sSketch.
Scalable Algorithms in the Age of Big Data and Network Sciences: Characterization, Primitives, and Techniques
Authors: Shang-Hua Teng
Venue: WSDM
Year: 2018

In the age of network sciences and machine learning, efficient algorithms are now in higher demand more than ever before. Big Data fundamentally challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today»s problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. In this talk, I will highlight a family of fundamental algorithmic techniques for designing provably-good scalable algorithms: (1) scalable primitives and scalable reduction, (2) spectral approximation of graphs and matrices, (3) sparsification by multilevel structures, (4) advanced sampling, (5) local network exploration. For the first, I will focus on the emerging Laplacian Paradigm, that has led to breakthroughs in scalable algorithms for several fundamental problems in network analysis, machine learning, and scientific computing. I will then illustrate these algorithmic techniques with four recent applications: (1) sampling from graphic models, (2) network centrality approximation, (3) social-influence analysis (4) local clustering. Mathematical and algorithmic solution to these problems exemplify the fusion of combinatorial, numerical, and statistical thinking in data and network analysis.
Collabot: Personalized Group Chat Summarization
Authors: Naama Tepper, Anat Hashavit, Maya Barnea, Inbal Ronen, Lior Leiba
Venue: WSDM
Year: 2018

In recent years, enterprise group chat collaboration tools, such as Slack, IBM»s Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools - productivity increase and improved group communication - come significant challenges. Specifically, the 'always on' feature that makes it hard for users to cope with the load of conversational content and get up to speed after logging off for a while. In this demo, we present Collabot - a chat assistant service that implicitly learns users interests and social ties within a chat group and provides a personalized digest of missed content. Collabot assists users in coping with chat information overload by helping them understand the main topics discussed, collaborators, links and resources. This demo has two main contributions. First, we present a novel personalized group chat summarization algorithm; second the demonstration depicts a working implementation applied on different chat groups from different domains within IBM. A video, describing the demo can be found at https://www.youtube.com/watch?v=6cVsstiJ9vk.
A Unified Processing Paradigm for Interactive Location-based Web Search
Authors: Sheng Wang, Zhifeng Bao, Shixun Huang, Rui Zhang
Venue: WSDM
Year: 2018

This paper studies the location-based web search and aims to build a unified processing paradigm for two purposes: (1) efficiently support each of the various types of location-based queries (kNN query, top-k spatial-textual query, etc.) on two major forms of geo-tagged data, i.e., spatial point data such as geo-tagged web documents, and spatial trajectory data such as a sequence of geo-tagged travel blogs by a user; (2) support interactive search to provide quick response for a query session, within which a user usually keeps refining her query by either issuing different query types or specifying different constraints (e.g., adding a keyword and/or location, changing the choice of k, etc.) until she finds the desired results. To achieve this goal, we first propose a general Top-k query called Monotone Aggregate Spatial Keyword query-MASK, which is able to cover most types of location-based web search. Next, we develop a unified indexing (called Textual-Grid-Point Inverted Index) and query processing paradigm (called ETAIL Algorithm) to answer a single MASK query efficiently. Furthermore, we extend ETAIL to provide interactive search for multiple queries within one query session, by exploiting the commonality of textual and/or spatial dimension among queries. Last, extensive experiments on four real datasets verify the robustness and efficiency of our approach.
Position Bias Estimation for Unbiased Learning to Rank in Personal Search
Authors: Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, Marc Najork
Venue: WSDM
Year: 2018

A well-known challenge in learning from click data is its inherent bias and most notably position bias. Traditional click models aim to extract the ‹query, document› relevance and the estimated bias is usually discarded after relevance is extracted. In contrast, the most recent work on unbiased learning-to-rank can effectively leverage the bias and thus focuses on estimating bias rather than relevance [20, 31]. Existing approaches use search result randomization over a small percentage of production traffic to estimate the position bias. This is not desired because result randomization can negatively impact users' search experience. In this paper, we compare different schemes for result randomization (i.e., RandTopN and RandPair) and show their negative effect in personal search. Then we study how to infer such bias from regular click data without relying on randomization. We propose a regression-based Expectation-Maximization (EM) algorithm that is based on a position bias click model and that can handle highly sparse clicks in personal search. We evaluate our EM algorithm and the extracted bias in the learning-to-rank setting. Our results show that it is promising to extract position bias from regular clicks without result randomization. The extracted bias can improve the learning-to-rank algorithms significantly. In addition, we compare the pointwise and pairwise learning-to-rank models. Our results show that pairwise models are more effective in leveraging the estimated bias.
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
Authors: Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu
Venue: WSDM
Year: 2018

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg»» about users» true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users» profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users» sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users» latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.
Customer Purchase Behavior Prediction from Payment Datasets
Authors: Yu Ting Wen, Pei-Wen Yeh, Tzu-Hao Tsai, Wen-Chih Peng, Hong-Han Shuai
Venue: WSDM
Year: 2018

With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified.
Indirect Supervision for Relation Extraction using Question-Answer Pairs
Authors: Zeqiu Wu, Xiang Ren, Frank F. Xu, Ji Li, Jiawei Han
Venue: WSDM
Year: 2018

Automatic relation extraction (E)for types of interest is of great importance for interpreting massive text corpora in an efficient manner. For example, we want to identify the relationship "president_of" between entities "Donald Trump" and "United States" in a sentence expressing such a relation. Traditional RE models have heavily relied on human-annotated corpus for training, which can be costly in generating labeled data and become obstacles when dealing with more relation types. Thus, more RE extraction systems have shifted to be built upon training data automatically acquired by linking to knowledge bases (distant supervision). However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy. In recent years, as increasing attention has been brought to tackling question-answering (QA) tasks, user feedback or datasets of such tasks become more accessible. In this paper, we propose a novel framework, ReQuest, to leverage question-answer pairs as an indirect source of supervision for relation extraction, and study how to use such supervision to reduce noise induced from DS. Our model jointly embeds relation mentions, types, QA entity mention pairs and text features in two low-dimensional spaces (RE and QA), where objects with same relation types or semantically similar question-answer pairs have similar representations. Shared features connect these two spaces, carrying clearer semantic knowledge from both sources. ReQuest, then use these learned embeddings to estimate the types of test relation mentions. We formulate a global objective function and adopt a novel margin-based QA loss to reduce noise in DS by exploiting semantic evidence from the QA dataset. Our experimental results achieve an average of 11% improvement in F1 score on two public RE datasets combined with TREC QA dataset. Codes and datasets can be downloaded at https://github.com/ellenmellon/ReQuest.
GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics
Authors: Jiejun Xu, Hanghang Tong, Tsai-Ching Lu, Jingrui He, Nadya Bliss
Venue: WSDM
Year: 2018

Networks are natural analytic tools in modeling adversarial activities(e.g., human trafficking, illicit drug production, terrorist financial transaction) using different intelligence data sources. However, such activities are often covert and embedded across multiple domains and contexts. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a joint manner. Thus, one of the main research topics in modeling adversarial activities is to develop effective techniques to align and fuse information from different networks into a unified representation for global analysis. Based on the combined network representation, an equally important research topic is on detecting and matching indicating patterns to recognize the underlining adversarial activities in the integrated network. The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on graph mining techniques in the era of big data, with emphasis on two fundamental problems - "Connecting the dots" and "finding a needle in a haystack", in the context of graph-based adversarial activity analytics.
Differential Privacy for Information Retrieval
Authors: Grace Hui Yang, Sicong Zhang
Venue: WSDM
Year: 2018

The concern for privacy is real for any research that uses user data. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information.
Dynamic Word Embeddings for Evolving Semantic Discovery
Authors: Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, Hui Xiong
Venue: WSDM
Year: 2018

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting alignment problem. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.
IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization
Authors: Feida Zhu, Yongfeng Zhang, Neil Yorke-Smith, Guibing Guo, Xu Chen
Venue: WSDM
Year: 2018

Recommendation system has became an important component in many real applications, ranging from e-commerce, music app to video-sharing site and on-line book store. The key of a successful recommendation system lies in the accurate user/item profiling. With the advent of web 2.0, quite a lot of multimodal information has been accumulated, which provides us with the opportunity to profile users in a more comprehensive manner. However, directly integrating multimodal information into recommendation system is not a trivial task, because they may be either homogenous or heterogeneous, which requires more advanced method for both fusion and alignment.