In evaluating semantic similarity, word senses are crucial, and word senses vary with context. In the literature, existing word sense disambiguation methods are often expensive, limited, and may require human intervention. Moreover, identifying the correct sense of words can often be subjective even for humans. For these reasons, we propose a fuzzy approach that incorporates all possible contexts and associated senses of words by leveraging the BabelNet encyclopedic dictionary. A previous experiment using the most representative taxonomy-based similarity measures demonstrates that our proposal improves Pearson correlation with human judgment for all selected measures.

This work proposes the Named Entities Semantic Relatedness (NESR) method to compute the semantic relatedness between named entities in DBpedia. Semantic relatedness is applied in many contexts, such as entity linking and word sense disambiguation, and DBpedia is one of the largest publicly available knowledge graphs. Furthermore, named entities, which are resources that represent real-world objects, in general, in DBpedia, have richer descriptions than conceptual entities. Given two named entities, the method computes their degree of semantic relatedness considering weighted reciprocal references of adjacent resources in the paths connecting them. NESR was experimented with five datasets and compared with ten methods to compute semantic relatedness in an RDF knowledge graph. The results show that, concerning Spearman’s correlation, on average, NESR outperforms each method by at least $8 \%$, and outperforms the method that performs best by $11 \%$ on average.

Abstract In this paper, we address the problem of answering complex questions formulated by users in natural language. Since traditional information retrieval systems are not suitable for complex questions, these questions are usually run over knowledge bases, such as Wikidata or DBpedia. We propose a semi-automatic approach for transforming a natural language question into a SPARQL query that can be easily processed over a knowledge base. The approach applies classification techniques to associate a natural language question with a proper query template from a set of predefined templates. The nature of our approach is semi-automatic as the query templates are manually written by human assessors, who are the experts of the knowledge bases, whereas the classification and query processing steps are completely automatic. Our experiments on the large-scale CSQA dataset for question-answering corroborate the effectiveness of our approach.

Semantic similarity search is one of the most promising methods for improving the performance of retrieval systems. This paper presents a new probabilistic method for ontology weighting based on a Bayesian approach. In particular, this work addresses the semantic search method SemSim for evaluating the similarity among a user request and semantically annotated sources. Each resource is annotated with a vector of features (annotation vector), i.e., a set of concepts defined in a reference ontology. Analogously, a user request is represented by a collection of desired features. The paper shows, on the bases of a comparative study, that the adoption of the Bayesian weighting method improves the performance of the SemSim method.

Summary The role of ontologies in the enterprise world is constantly increasing, and they are proving even more necessary in distributed production realities, like in extended logistics, industrial districts, or virtual enterprises. In such realities, a common conceptual basis is necessary to compensate the fragmentation inherent in the underlying industrial models. This paper presents an end‐to‐end framework based on an open, collaborative approach for developing and maintaining business domain ontologies. The framework has been achieved within the European Project BIVEE and has been experimented in two industrial realities: the high‐tech sector of robotics and the more traditional one of furniture. Copyright © 2015 John Wiley & Sons, Ltd.

Ontologies are emerging as a key solution to allow different applications to exchange information in a cooperative business environment. From the technology point of view, the growth of the Internet and the Web has favoured the exchange of information and documents among people, but unfortunately the same is not true when information is exchanged among software applications. To tackle the latter problem, ontology-based approaches are among the most promising ones. This is one of the reasons of the momentum that ontologies, and consequently ontology management systems (OMSs), are witnessing. An OMS represents a powerful tool to create common and shareable knowledge repositories concerning a given application domain. The goal of this work is to present SymOntoX, a Web-based OMS conceived for the ebusiness domain. It is an open source software system supporting collaborative and distributed ontology authoring activities.