OpenRaiser/Intern-Atlas huggingface dataset:
Fake news: Fundamental theories, detection strategies and challenges
Authors: Xinyi Zhou, Reza Zafarani, Kai Shu, Huan Liu
Venue: Conference WSDM 2019
Year: 2019

The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumors); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.
Multiple view semantic segmentation for street view images
Authors: Jianxiong Xiao, Long Quan
Venue: Conference ICCV 2009
Year: 2009

We propose a simple but powerful multi-view semantic segmentation framework for images captured by a camera mounted on a car driving along streets. In our approach, a pair-wise Markov Random Field (MRF) is laid out across multiple views. Both 2D and 3D features are extracted at a super-pixel level to train classifiers for the unary data terms of MRF. For smoothness terms, our approach makes use of color differences in the same image to identify accurate segmentation boundaries, and dense pixel-to-pixel correspondences to enforce consistency across different views. To speed up training and to improve the recognition quality, our approach adaptively selects the most similar training data for each scene from the label pool. Furthermore, we also propose a powerful approach within the same framework to enable large-scale labeling in both the 3D space and 2D images. We demonstrate our approach on more than 10,000 images from Google Maps Street View.
Understanding reading attention distribution during relevance judgement
Authors: Xiangsheng Li, Yiqun Liu, Jiaxin Mao, Zexue He, Min Zhang, Shaoping Ma
Venue: Conference CIKM 2018
Year: 2018

Reading is a complex cognitive activity in many information retrieval related scenarios, such as relevance judgement and question answering. There exists plenty of works which model these processes as a matching problem, which focuses on how to estimate the relevance score between a document and a query. However, little is known about what happened during the reading process, i.e., how users allocate their attention while reading a document during a specific information retrieval task. We believe that a better understanding of this process can help us design better weighting functions inside the document and contributes to the improvement of ranking performance. In this paper, we focus on the reading process during relevance judgement task. We designed a lab-based user study to investigate human reading patterns in assessing a document, where users' eye movements and their labeled relevant text were collected, respectively. Through a systematic analysis into the collected data, we propose a two-stage reading model which consists of a preliminary relevance judgement stage (Stage 1) and a reading with preliminary relevance stage (Stage 2). In addition, we investigate how different behavior biases affect users' reading behaviors in these two stages. Taking these biases into consideration, we further build prediction models for user's reading attention. Experiment results show that query independent features outperform query dependent features, which indicates that users allocate attentions based on many signals other than query terms in this process. Our study sheds light on the understanding of users' attention allocation during relevance judgement and provides implications for improving the design of existing ranking models.
Generating illustrative snippets for open data on the Web
Authors: Gong Cheng, Cheng Jin, Wentao Ding, Danyun Xu, Yuzhong Qu
Venue: Conference WSDM 2017
Year: 2017

To embrace the open data movement, increasingly many datasets have been published on the Web to be reused. Users, when assessing the usefulness of an unfamiliar dataset, need means to quickly inspect its contents. To satisfy the needs, we propose to automatically extract an optimal small portion from a dataset, called a snippet, to concisely illustrate the contents of the dataset. We consider the quality of a snippet from three aspects: coverage, familiarity, and cohesion, which are jointly formulated in a new combinatorial optimization problem called the maximum-weight-and-coverage connected graph problem (MwcCG). We give a constant-factor approximation algorithm for this NP-hard problem, and experiment with our solution on real-world datasets. Our quantitative analysis and user study show that our approach outperforms a baseline approach.
Question answering on interlinked data
Authors: Saeedeh Shekarpour, Axel-Cyrille Ngonga Ngomo, Sören Auer
Venue: Conference WWW 2013
Year: 2013

The Data Web contains a wealth of knowledge on a large number of domains. Question answering over interlinked data sources is challenging due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present a question answering system, which transforms user supplied queries (i.e. natural language sentences or keywords) into conjunctive SPARQL queries over a set of interlinked data sources. The contribution of this paper is two-fold: Firstly, we introduce a novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation). We employ a hidden Markov model, whose parameters were bootstrapped with different distribution functions. Secondly, we present a novel method for constructing a federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph which ultimately renders a corresponding SPARQL query. The results of our evaluation with three life-science datasets and 25 benchmark queries demonstrate the effectiveness of our approach.
Keyword search over knowledge graphs via static and dynamic hub labelings
Authors: Yuxuan Shi, Gong Cheng, Evgeny Kharlamov
Venue: Conference WWW 2020
Year: 2020

Keyword search is a prominent approach to querying Web data. For graph-structured data, a widely accepted semantics for keywords is based on group Steiner trees. For this NP-hard problem, existing algorithms with provable quality guarantees have prohibitive run time on large graphs. In this paper, we propose practical approximation algorithms with a guaranteed quality of computed answers and very low run time. Our algorithms rely on Hub Labeling (HL), a structure that labels each vertex in a graph with a list of vertices reachable from it, which we use to compute distances and shortest paths. We devise two HLs: a conventional static HL that uses a new heuristic to improve pruned landmark labeling, and a novel dynamic HL that inverts and aggregates query-relevant static labels to more efficiently process vertex sets. Our approach allows to compute a reasonably good approximation of answers to keyword queries in milliseconds on million-scale knowledge graphs.
GREASE: A generative model for relevance search over knowledge graphs
Authors: Tianshuo Zhou, Ziyang Li, Gong Cheng, Jun Wang, Yuang Wei
Venue: Conference WSDM 2020
Year: 2020

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.