Introduction to statistical semantics

Human beings create meaning from the fact that concepts tend to occur together in a predictable way. When we see a dog, we also see a tail, paws, eyes, legs, fur, and etcetera. In this context, we would see an owner who goes for a walk in the park with the dog that is attached to a leach, and the dog might bark. Thus, the concept of a dog is connected with these other concepts, and when these concepts reliable co-occur with each other, then the meaning of what a dog is, is then created. In other words, the meaning of a concept is generated when there are reliable relationships between different concepts related to the one we want to define. Thus, the probability of occurrences of one concept is increased by the presence of another concept. Nevertheless, despite the fact that the world is filled with reliable co-occurrences that provide opportunities to the creation of meaning, it is not sufficient in itself to create meaning. Meaning creation also requires a mental representation that reflects the co-occurrences that are present in the world. This is what happens in the brain when we apprehend our environment. Sadly, we do not have a direct access to the brain’s representation of meaning. In this book, different authors outline scientific research that use statistical semantics as a method to create models for describing semantic representations of human’s meaning-making through natural language

OSCAR: A Customisable Tool for Free-Text Search over SPARQL Endpoints

SPARQL is a very powerful query language for RDF data, which can be used to retrieve data following specific patterns. In order to foster the availability of scholarly data on the Web, several project and institutions make available Web interfaces to SPARQL endpoints so as to enable a user to search for information in the RDF datasets they expose using SPARQL. However, SPARQL is quite complex to learn, and usually it is fully accessible only to experts in Semantic Web technologies, remaining completely obscure to ordinary Web users. In this paper we introduce OSCAR, the OpenCitations RDF Search Application, which is a user-friendly search platform that can be used to search any RDF triplestore providing a SPARQL endpoint, while hiding the complexities of SPARQL. We present its main features and demonstrate how it can be adapted to work with different SPARQL endpoints containing scholarly data, vis those provided by OpenCitations, ScholarlyData and Wikidata. We conclude by discussing the results of a user testing session that reveal the usability of the OSCAR search interface when employed to access information within the OpenCitations Corpus