https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation.Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

Evaluating Learning Algorithms: A Classification Perspective

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

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Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

The Nature Of Emotion Fundamental Questions

Background: Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it.
Objective: The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation.
Methods: This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined.
Results: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. 

This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter.
Conclusions: The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

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COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.

Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search.

Semantically Enhanced Entity Ranking

Entity Ranking (ER) is a recently emerging search task in Information Retrieval, where the goal is not finding documents matching the query words, but instead finding entities which match types and attributes mentioned in the query. In this paper we propose a formal model to define entities as well as a complete ER system, providing examples of its application to enterprise, Web, and Wikipedia scenarios. Since searching for entities on Web scale repositories is an open challenge as the effectiveness of ranking is usually not satisfactory, we present a set of algorithms based on our model and evaluate their retrieval effectiveness. The results show that combining simple Link Analysis, Natural Language Processing, and Named Entity Recognition methods improves retrieval performance of entity search by over 53% for P@10 and 35% for MAP.

A Model for Ranking Entities and Its Application to Wikipedia

This paper presents an in-depth qualitative analysis of n=13,373 tweets that relate to the peak of the Swine Flu outbreak of 2009, and the Ebola outbreak of 2014. Tweets were analysed using thematic analysis and a number of themes and sub-themes were identified. The results were brought together in an abstraction phase and the commonalities between the cases were studied. An interesting similarity which emerged was the rate at which Twitter users expressed intense fear and panic akin to that of the sociological concept of "moral panic". Moreover, a number of discussions were found to emerge which were not reported in previous literature. Our study is the largest in-depth analysis of tweets on infectious diseases. Our results will inform public health strategies for future infectious disease outbreaks. Future work will seek to conduct further comparisons and explore relevant health theory.

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Moral Panic through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks

Scientific documents often adopt a well-defined vocabulary and avoid the use of ambiguous terms. However, as soon as documents from different research sub-communities are considered in combination, many scientific terms become ambiguous as the same term can refer to different concepts from different sub-communities. The ability to correctly identify the right sense of a given term can considerably improve the effectiveness of retrieval models, and can also support additional features such as search diversification. This is even more critical when applied to explorative search systems within the scientific domain.

In this paper, we propose novel semi-supervised methods to term disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. Experimental evidence over two different test collections from the physics and biomedical domains shows that the proposed method is effective and outperforms state-of-the-art approaches based on feature vectors constructed out of term co-occurrences as well as standard supervised approaches.

/pdf/ontology-based-word-sense-disambiguation-for-scientific-342abd7mlf.pdf

Ontology-Based word sense disambiguation for scientific literature

Requesters on crowdsourcing platforms typically exercise the power to decide the fate of tasks completed by crowd workers. Rejecting work has a direct impact on workers; (i) they may not be rewarded for the work completed and for their effort that has been exerted, and (ii) rejection affects worker reputation and may limit their access to future work opportunities. This paper presents a comprehensive study that aims to understand worker moods and how workers react to rejections in microtask crowdsourcing. We experimentally investigate the effect of the mood of workers on their performance, and the interaction of their moods with their reactions to rejection. Finally, we explore techniques such as presenting social comparative explanations to foster positive reactions to rejection. We found that workers in pleasant moods significantly outperform those in unpleasant moods. Workers whose work is rejected due to narrowly failing pre-screening tests exhibited the most negative emotional responses.

Gianluca Demartini

Papers

Semantically Enhanced Entity Ranking

A Model for Ranking Entities and Its Application to Wikipedia

Moral Panic through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks

Ontology-Based word sense disambiguation for scientific literature

Understanding Worker Moods and Reactions to Rejection in Crowdsourcing