Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

A dataset independent set of baselines for relation prediction in argument mining.

Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task

Detecting deceptive reviews using argumentation

The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. In this paper we use Argumentation Frameworks (AFs) extracted from reviews and explore whether the use of these AFs can improve the performance of machine learning techniques in detecting deceptive behaviour, resulting from users lying in order to mislead readers. The AFs represent how arguments from reviews relate to arguments from other reviews as well as to arguments about the goodness of the items being reviewed

The history of the development of wireless telegraphy and broadcasting in Australia to 1942, with especial reference to the Australian Broadcasting Commission : a political and administrative study

Licences are a crucial aspect of the information publishing process in the web of (linked) data. Recent work on modeling of policies with semantic web languages (RDF, ODRL) gives the opportunity to formally describe licences and reason upon them. However, choosing the right licence is still challenging. Particularly, understanding the number of features - permissions, prohibitions and obligations - constitute a steep learning process for the data provider, who has to check them individ- ually and compare the licences in order to pick the one that better fits her needs. The objective of the work presented in this paper is to reduce the effort required for licence selection. We argue that an ontology of licences, organized by their relevant features, can help providing support to the user. Developing an ontology with a bottom-up approach based on Formal Concept Analysis, we show how the process of licence selection can be simplified significantly and reduced to answering an average of three/five key questions

Argument mining: A machine learning perspective

Argument mining has recently become a hot topic, attracting the interests of several and diverse research communities, ranging from artificial intelligence, to computational linguistics, natural language processing, social and philosophical sciences. In this paper, we attempt to describe the problems and challenges of argument mining from a machine learning angle. In particular, we advocate that machine learning techniques so far have been under-exploited, and that a more proper standardization of the problem, also with regards to the underlying argument model, could provide a crucial element to develop better systems

Data Integration for Open Data on the Web

In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help