The emergence of linked data in the form of knowledge graphs in RDF has been one of the most recent evolutions of the Semantic Web. This led to the development of natural language question answering systems that automatically translates a question into SPARQL based on these RDF knowledge graphs. In particular, hybrid question answering, the task of question answering by combining both structured (RDF) and unstructured knowledge sources (text) has emerged as an important challenge. This paper tackles hybrid question answering based on natural language questions. We present HAWK_R, a question answering system that improves an open source system called HAWK. We identify its limitations and propose enhancements using heuristic-based methods based on RDF and text search. Our results show a clear improvement of the F-score.

This paper explores the possibility of using neural embedding features for enhancing the effectiveness of ad hoc document ranking based on learning to rank models. We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents. We employ several learning to rank methods for document ranking using embedding-based features, keyword-based features as well as the interpolation of the embedding-based features with keyword-based features. The results show that embedding features have a synergistic impact on keyword based features and are able to provide statistically significant improvement on harder queries.

One of the most important prerequisites for achieving the Semantic Web vision is semantic annotation of data/resources. Semantic annotation enriches unstructured and/or semistructured content with a context that is further linked to the structured domain-specific knowledge. In particular, ontologybased semantic annotators enable the selection of a specific ontology to annotate content. This paper presents results of an empirical study of recent ontology-based annotators, namely Stanbol, KIM, and SDArch. Specifically, we evaluated the robustness of these annotators with respect to specific features of ontology concepts such as the length of concepts? labels and their linguistic categories (e.g., prepositions and conjunctions). Our results show that although significantly correlated according to most of the conducted evaluations, tools still exhibit their unique features that could be a topic of new research.

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.

One of the goals of the knowledge puzzle project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the knowledge puzzle ontology learning tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.