Query: CIKM 2020

title: ESSTER at the EYRE 2020 Entity Summarization Task
abstract: Entity summaries provide human users with the key information about an entity. In this system paper, we present the implementation of our entity summarizer ESSTER. It aims at generating entity summaries that contain structurally important triples and exhibit high readability and low redundancy. For structural importance, we exploit the global and local characteristics of properties and values in RDF data. For readability, we learn the familarity of properties from a text corpus. To reduce redundancy, we perform logical reasoning and compute textual and numerical similarity between triples. ESSTER solves a combinatorial optimization problem to integrate these features. It achieves state of the art results on the ESBM v1.2 dataset.

title: SKG4J 2020: 1st International Workshop on Semantic and Knowledge Graph Advances for Journalism
abstract: SKG4J targeted contributions at the interface between Artificial Intelligence, Data Management and its implications for journalistic practice. The first version of the workshop accepted three submissions with topics emphasising the complementary requirements for delivering realistic journalistic knowledge extraction/management platforms.

title: ReCOVery: A Multimodal Repository for COVID 19 News Credibility Research
abstract: First identified in Wuhan, China, in December 2019, the outbreak of COVID 19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO) Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID 19. We first broadly search and investigate ~2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http:/coronavirus fakenews.com.

title: TweetsCOV19 A Knowledge Base of Semantically Annotated Tweets about the COVID 19 Pandemic
abstract: Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods. With respect to the recent outbreak of the Coronavirus disease 2019 (COVID 19) online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution, and interdependencies with real world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection, or entity recognition. However, obtaining, archiving, and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning October 2019 April 2020. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis and use cases of the corpus.

title: AGATHA: Automatic Graph Mining And Transformer based Hypothesis Generation Approach
abstract: Medical research is risky and expensive. Drug discovery requires researchers to efficiently winnow thousands of potential targets to a small candidate set. However, scientists spend significant time and money long before seeing the intermediate results that ultimately determine this smaller set. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep learning hypothesis generation system that learns a data driven ranking criteria to recommend new biomedical connections. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. Furthermore, we perform an ablation study to examine the aspects of our semantic network that most contribute to recommendation quality. Overall, AGATHA achieves best in class recommendation quality when compared to other hypothesis generation systems built to predict across all available biomedical literature.  All code, experimental data, and pre trained models are available online: sybrandt.com/2020/agatha.

title: The Enslaved Dataset: A Real world Complex Ontology Alignment Benchmark using Wikibase
abstract: Ontology alignment has taken a critical place for helping heterogeneous resources to interoperate. It has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers to find simple 1:1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. Even if the complex alignment systems are developed, the performance of finding complex relations still has a lot of room for improvement. One reason for this limitation may be that there are still few applicable alignment benchmarks that contain such complex relationships that can raise researchers' interests. In this paper, we propose a real world dataset from the Enslaved project as a potential complex alignment benchmark. The benchmark consists of two resources, the Enslaved Ontology along with a Wikibase repository holding a large number of instance data from the Enslaved project, as well as a manually created reference alignment between them. The alignment was developed in consultation with domain experts in the digital humanities. The alignment not only includes simple 1:1 equivalence correspondences, but also more complex m:n equivalence and subsumption correspondences and are provided in both Expressive and Declarative Ontology Alignment Language (EDOAL) format and rule syntax. The Enslaved benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) 2020 and is completely free for public use to assist the researchers in developing and evaluating their complex alignment algorithms.

title: Graph Analytics on Proximity Data to Fight Contagion
abstract: As social distancing has become the new normal, it is eminent for enterprises and administrations to make decisions based on analytics on human proximity data Graph Analytics can serve as an essential technology to understand human network behaviors and contact tracing inferences in pandemic and post pandemic scenarios A large amount of work has happened on epidemiology of pandemics However, a formal application of graph analytics backed by visualization and mapping of graph algorithms has remained neglected in pandemic scenarios This paper has addressed this important need backed by graph modeling, methods and analytics (c) 2020 CEUR WS All rights reserved

title: DataMod2020: 9th International Symposium "From Data to Models and Back"
abstract: DataMod 2020 aims to bring together practitioners and researchers from academia, industry and research institutions interested in the combined application of computational modelling methods with data driven techniques from the areas of knowledge management, data mining and machine learning. Modelling methodologies of interest include automata, agents, Petri nets, process algebras and rewriting systems. Application domains include social systems, ecology, biology, medicine, smart cities, governance, security, education, software engineering, and any other field that deals with complex systems and large amounts of data. Papers can present research results in any of the themes of interest for the symposium as well as application experiences, tools and promising preliminary ideas. Papers dealing with synergistic approaches that integrate modelling and knowledge management/discovery or that exploit knowledge management/discovery to develop/syntesise system models are especially welcome.

title: A Large Test Collection for Entity Aspect Linking
abstract: Given a text with entity links, the task of entity aspect linking is to identify which aspect of an entity is referred to in the context. For example, if a text passage mentions the entity "USA' is USA mentioned in the context of the 2008 financial crisis, American cuisine, or else? Complementing efforts of Nanni et al (2018) we provide a large scale test collection which is derived from Wikipedia hyperlinks in a dump from 01/01/2020. Furthermore, we offer strong baselines with results and broken out feature sets to stimulate more research in this area. Data, code, feature sets, runfiles and results are released under a CC SA license and offered on our aspect linking resource web page http:/www.cs.unh.edu/~dietz/eal dataset 2020/

title: The Ebb and Flow of the COVID 19 Misinformation Themes
abstract: The COVID 19 pandemic has seen the emergence of unique misinformation narratives in various outlets, through social media, blogs, etc This online misinformation has been proven to spread in a viral manner and has a direct impact on public safety In an effort to improve public understanding, we curated a corpus of 543 misinformation pieces whittled down to 243 unique misinformation narratives along with third party proofs debunking these stories Building upon previous applications of topic modeling to COVID 19 related material, we developed a tool leveraging topic modeling to create a chronological visualization of these stories From our corpus of misinformation stories, this tool has shown to accurately represent the ground truth reported by our curator team This highlights some of the misinformation narratives unique to the COVID 19 pandemic and provides a quick method to monitor and assess misinformation diffusion, enabling policymakers to identify themes to focus on for communication campaigns (c) 2020 CEUR WS All rights reserved