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.

This paper addresses the notion of semantic similarity between concepts organized according to a taxonomy, based on the well-known information content approach. This approach has been widely experimented in the literature over the years and, in general, outperforms other proposals which do not originate from it. However, it shows some limitations related to the notion of generic sense of a concept. In this paper we illustrate the problem arising by using the traditional approach, and a novel information-theoretic definition of semantic similarity in a taxonomy is proposed which also takes into account the intended sense of a concept in a given context. This proposal has been applied to some among the most representative state-of-the-art similarity measures based on the information content approach, and the experiment shows that it achieves very high correlation values with human judgment.

Business Process (BP) modeling is constantly acquiring attention in modern enterprises. Today, BP editor tools support modelers in building correct diagrams only from the syntactic point of view. Enriching them with ontologies can bring many advantages as the possibility of applying advanced reasoning techniques, aimed at the identification of contradictions and mistakes in the model specification, and the possibility of organizing BP models repositories, with advanced search and retrieval facilities. Furthermore, semantic technologies can substantially support business/IT alignment. Semantic enrichment of BPs can be achieved by representing a BP, or part of it, with an ontology-oriented formalism (semantic lifting) and mapping it to a reference ontology. In this paper, we present the basic elements of a business process ontology framework (OPAL+BPAL) and its concrete representation according to OWL syntax. Finally, we show how it is possible to generate an OWL representation of a BPMN diagram.

Background: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being “standardized” so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer’s Disease (AD) in whose context specialized data collections such as the one by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are maintained. Methods: Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner. Results: We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer’s disease. Conclusions: The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

This paper presents SemSim^e, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSim^e is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSim^e has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as $SemSi{m}_{RV}$ .

The evaluation of the semantic similarity of concepts organized according to taxonomies is a long-standing problem in computer science and has attracted great attention from researchers over the decades. In this regard, the notion of information content plays a key role, and semantic similarity measures based on it are still on the rise. In this review, we address the methods for evaluating the semantic similarity between either concepts or sets of concepts belonging to a taxonomy that, often, in the literature, adopt different notations and formalisms. The results of this systematic literature review provide researchers and academics with insight into the notions that the methods discussed have in common through the use of the same notation, as well as their differences, overlaps, and dependencies, and, in particular, the role of the notion of information content in the evaluation of semantic similarity. Furthermore, in this review, a comparative analysis of the methods for evaluating the semantic similarity between sets of concepts is provided.