This paper summarises work where we combined semantic web technologies with deep learning systems to obtain state-of-the art explainable misinformation detection. We proposed a conceptual and computational model to describe a wide range of misinformation detection systems based around the concepts of credibility and reviews. We described how Credibility Reviews (CRs) can be used to build networks of distributed bots that collaborate for misinformation detection which we evaluated by building a prototype based on publicly available datasets and deep learning models.

Deep learning currently dominates the benchmarks for various NLP tasks and, at the basis of such systems, words are frequently represented as embeddings --vectors in a low dimensional space-- learned from large text corpora and various algorithms have been proposed to learn both word and concept embeddings. One of the claimed benefits of such embeddings is that they capture knowledge about semantic relations. Such embeddings are most often evaluated through tasks such as predicting human-rated similarity and analogy which only test a few, often ill-defined, relations. In this paper, we propose a method for (i) reliably generating word and concept pair datasets for a wide number of relations by using a knowledge graph and (ii) evaluating to what extent pre-trained embeddings capture those relations. We evaluate the approach against a proprietary and a public knowledge graph and analyze the results, showing which lexico-semantic relational knowledge is captured by current embedding learning approaches.

Intercultural competence is an essential 21st Century skill. A key issue for developers of cross-cultural training simulators is the need to provide relevant learning experience adapted to the learnerfis abilities. This paper presents a dialogic approach for a quick assessment of the depth of a learner's current intercultural awareness as part of the EU ImREAL project. To support the dialogue, Linked Data is seen as a rich knowledge base for a diverse range of resources on cultural aspects. This paper investigates how semantic technologies could be used to: (a) extract a pool of concrete culturally-relevant facts from DBpedia that can be linked to various cultural groups and to the learner, (b) model a learner's knowledge on a selected set of cultural themes and (c) provide a novel, adaptive and user-friendly, user modelling dialogue for cultural awareness. The usability and usefulness of the approach is evaluated by CrowdFlower and Expert Inspection.

The complexity of ontology authoring and the difficulty to master the use of existing ontology authoring tools, put significant constraints on the involvement of both domain experts and knowledge engineers in ontology authoring. This often requires substantial effort for fixing ontologies defects (e.g. inconsistency, unsatisfiability, missing or unintended implications, redundancy, isolated entities). The paper argues that ontology authoring tools should provide immediate semantic feedback upon entering ontological constructs. We present a framework to analyse input axioms and provide meaningful feedback at a semantic level. The framework has been used to augment an existing Controlled Natural Language-based ontology authoring tool – ROO. An experimental study with ROO has been conducted to examine users' reactions to the semantic feedback and the effect on their ontology authoring behaviour. The study strongly supported responsive intuitive ontology authoring tools, and identified future directions to extend and integrate semantic feedback.