Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries to produce word embeddings for rare words. For this purpose, we introduce two methods: Definition Neural Network (DefiNNet) and Define BERT (DefBERT). In our experiments, DefiNNet and DefBERT significantly outperform state-of-the-art as well as baseline methods devised for producing embeddings of unknown words. In fact, DefiNNet significantly outperforms FastText, which implements a method for the same task-based on n-grams, and DefBERT significantly outperforms the BERT method for OOV words. Then, definitions in traditional dictionaries are useful to build word embeddings for rare words.

The Semantic Search research field aims to query metadata and to identify relevant subgraphs. While in traditional search engines queries are composed by lists of keywords connected through boolean operators, Semantic Search instead, requires the submission of semantic queries that are structured as a graph of concepts, entities and relations. Submission of this graph is however not trivial as while a list of keywords of interest can be provided by any user, the formulation of semantic queries is not easy as well. One of the main challenges of RDF Browsers lies in the implementation of interfaces that allow the common user to submit semantic queries by hiding their complexity. Furthermore a good semantic search algorithm is not enough to fullfil user needs, it is worthwhile to implement visualization methods which can support users in intuitively understanding why and how the results were retrieved. In this paper we present a novel solution to query RDF datasets and to browse the results of the queries in an appealing manner.

The Semantic Web is facing the important challenge to maintain its promise of a real world-wide graph of interconnected resources. Unfortunately, while URIs almost guarantee a direct reference to entities, the relation between the two is not bijective. Many different URI references to same concepts and entities can arise when -- in such a heterogeneous setting as the WWW -- people independently build new ontologies, or populate shared ones with new arbitrarily identified individuals. The proliferation of URIs is an unwanted, though natural effect strictly bound to the same principles which characterize the Semantic Web; reducing this phenomenon will improve the recall of Semantic Search engines, which could rely on explicit links between heterogeneous information sources. To address this problem, in this paper we present an integrated environment combining the semantic annotation and ontology building features available in the Semantic Turkey web browser extension, with globally unique identifiers for entities provided by the okkam Entity Name System, thus realizing a valuable resource for preventing diffusion of multiple URIs on the (Semantic) Web.