Serena Villata

Bio: Serena Villata is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Argumentation theory & Semantic Web. The author has an hindex of 28, co-authored 196 publications receiving 2814 citations. Previous affiliations of Serena Villata include French Institute for Research in Computer Science and Automation & University of Turin.

Towards Artificial Argumentation.

Abstract: The field of computational models of argument is emerging as an important aspect of artificial intelligence research. The reason for this is based on the recognition that if we are to develop robust intelligent systems, then it is imperative that they can handle incomplete and inconsistent information in a way that somehow emulates the way humans tackle such a complex task. And one of the key ways that humans do this is to use argumentation either internally, by evaluating arguments and counterarguments‚ or externally, by for instance entering into a discussion or debate where arguments are exchanged. As we report in this review, recent developments in the field are leading to technology for artificial argumentation, in the legal, medical, and e-government domains, and interesting tools for argument mining, for debating technologies, and for argumentation solvers are emerging.

Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions

Abstract: Blogs and forums are widely adopted by online communities to debate about various issues. However, a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications. In our paper, we combine textual entailment with argumentation theory to automatically extract the arguments from debates and to evaluate their acceptability.

Five Years of Argument Mining: a Data-driven Analysis

Abstract: Argument mining is the research area aiming at extracting natural language arguments and their relations from text, with the final goal of providing machine-processable structured data for computational models of argument. This research topic has started to attract the attention of a small community of researchers around 2014, and it is nowadays counted as one of the most promising research areas in Artificial Intelligence in terms of growing of the community, funded projects, and involvement of companies. In this paper, we present the argument mining tasks, and we discuss the obtained results in the area from a data-driven perspective. An open discussion highlights the main weaknesses suffered by the existing work in the literature, and proposes open challenges to be faced in the future.

Support in Abstract Argumentation

Abstract: In this paper, we consider two drawbacks of Cayrol and Lagasque-Schiex's meta-argumentation theory to model bipolar argumentation frameworks. We consider first the “lost of admissibility” in Dung's sense and second, the definition of notions of attack in the context of a support relation. We show how to prevent these drawbacks by introducing support meta-arguments. Like the model of Cayrol and Lagasque-Schiex, our formalization confirms the use of meta-argumentation to reuse Dung's properties. We do not take a stance towards the usefulness of a support relation among arguments, though we show that if one would like to introduce them, it can be done without extending Dung's theory. Finally, we show how to use meta-argumentation to instantiate an argumentation framework to represent defeasible support. In this model of support, the support relation itself can be attacked.

Enabling linked data publication with the Datalift platform

Abstract: As many cities around the world provide access to raw public data along the Open Data movement, many questions arise concerning the accessibility of these data. Various data formats, duplicate identifiers, heterogeneous metadata schema descriptions, and diverse means to access or query the data exist. These factors make it difficult for consumers to reuse and integrate data sources to develop innovative applications. The Semantic Web provides a global solution to these problems by providing languages and protocols for describing and accessing datasets. This paper presents Datalift, a framework and a platform helping to lift raw data sources to semantic interlinked data sources.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

The Uses Of Argument

Abstract: Thank you very much for downloading the uses of argument. Maybe you have knowledge that, people have search numerous times for their chosen novels like this the uses of argument, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer. the uses of argument is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the the uses of argument is universally compatible with any devices to read.

Knowledge Graphs

Abstract: In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.