DOOR: towards a formalization of ontology relations

In this paper, we describe our ongoing effort in describing and formalizing semantic relations that link ontolo- gies with each others on the Semantic Web in order to create an ontology, DOOR, to represent, manipulate and reason upon these relations. DOOR is a Descriptive Ontology of Ontology Relations which intends to define relations such as inclusion, versioning, similarity and agreement using ontological primitives as well as rules. Here, we provide a detailed description of the methodology used to design the DOOR ontology, as well as an overview of its content. We also describe how DOOR is used in a complete framework (called KANNEL) for detecting and managing semantic relations between ontologies in large ontology repositories. Applied in the context of a large collection of automatically crawled ontologies, DOOR and KANNEL provide a starting point for analyzing the underlying structure of the network of ontologies that is the Semantic Web

A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web

SPARQL Query Recommendations by Example

In this demo paper, a SPARQL Query Recommendation Tool (called SQUIRE) based on query reformulation is presented. Based on three steps, Generalization, Specialization and Evaluation, SQUIRE implements the logic of reformulating a SPARQL query that is satisfiable w.r.t a source RDF dataset, into others that are satisfiable w.r.t a target RDF dataset. In contrast with existing approaches, SQUIRE aims at rec- ommending queries whose reformulations: i) reflect as much as possible the same intended meaning, structure, type of results and result size as the original query and ii) do not require to have a mapping between the two datasets. Based on a set of criteria to measure the similarity between the initial query and the recommended ones, SQUIRE demonstrates the feasibility of the underlying query reformulation process, ranks appropriately the recommended queries, and offers a valuable support for query recommendations over an unknown and unmapped target RDF dataset, not only assisting the user in learning the data model and content of an RDF dataset, but also supporting its use without requiring the user to have intrinsic knowledge of the data

Applicability of Emotion to Intelligent Systems

We propose to investigate the connection between emotions and cognition in intelligent systems through the dynamic concept of language, which links context to logic in both human and machine language. For this, our approach is inspired on aspects of the information theory of Abraham Moles. We analyze emotions under the semantic dimension, linked to a subjective context, which gives rise or not to decisions. We demonstrate that intelligent systems can, on the one hand, work with previously categorized emotions (say in a frozen context); or, on the other hand, process information under a dynamic aspect. This is possible when considering that the algorithm, as the core of the system’s language, must be adapted to functions that reflect an updated context. Thus, adapting emotions to AI means working with time-dependent communication-interpretation, in an optimized way, uniting syntax and semantics in the intended behavior of the machine. We conclude that misinterpretations can be avoided by inserting a contextual appreciation together with a categorized appreciation of emotions at the heart of the system. This allows it to absorb pre-established values in a unified way with the fluid values of emotions, making the system more intuitive. It is believed that, in this way, Computational Linguistics is focused on the characteristics of Cognitive Computing, teaching the system to interpret the appropriate context of the emotion at stake

The concept of care complexity: a qualitative study

Background: Hospital organisations based on the level of care intensity have clearly revealed a concept, that of care complexity, which has been widely used for decades in the healthcare field. Despite its wide use, this concept is still poorly defined and it is often confused with and replaced by similar concepts such as care intensity or workload. This study aims to describe the meaning of care complexity as perceived by nurses in their day-to-day experience of hospital clinical care, rehabilitation, home care, and organisation. Design and methods: Fifteen interviews were conducted with nurses belonging to clinical-care areas and to heterogeneous organisational areas. The interview was of an unstructured type. The participants were selected using a propositional methodology. Colaizzi’s descriptive phenomenological method was chosen for the analysis of the interviews. Results: The nurses who were interviewed predominantly perceive the definition of care complexity as coinciding with that of workload. Nevertheless, the managerial perspective does not appear to be exclusive, as from the in-depth interviews three fundamental themes emerge that are associated with the concept of care complexity: the patient, the nurse and the organisation. Conclusions: The study highlights that care complexity consists of both quantitative and qualitative aspects that do not refer only to the organisational dimension. The use of the terminology employed today should be reconsidered: it appears to be inappropriate to talk of measurement of care complexity, as this concept also consists of qualitative – thus not entirely quantifiable – aspects referring to the person being cared for. In this sense, reference should instead be made to the evaluation of care complexity, which would also constitute a better and more complete basis for defining the nursing skills required in professional nursing practice

SPARQL Query Recommendation by Example: Assessing the Impact of Structural Analysis on Star-Shaped Queries

One of the existing query recommendation strategies for unknown datasets is "by example", i.e. based on a query that the user already knows how to formulate on another dataset within a similar domain. In this paper we measure what contribution a structural analysis of the query and the datasets can bring to a recommendation strategy, to go alongside approaches that provide a semantic analysis. Here we concentrate on the case of star-shaped SPARQL queries over RDF datasets. The illustrated strategy performs a least general generalization on the given query, computes the specializations of it that are satisfiable by the target dataset, and organizes them into a graph. It then visits the graph to recommend first the reformulated queries that reflect the original query as closely as possible. This approach does not rely upon a semantic mapping between the two datasets. An implementation as part of the SQUIRE query recommendation library is discussed

Recommendations for coronavirus infection in rheumatic diseases treated with biologic therapy.

The Coronavirus-associated disease, that was first identified in 2019 in China (CoViD-19), is a pandemic caused by a bat-derived beta-coronavirus, named SARS-CoV2. It shares homology with SARS and MERS-CoV, responsible for past outbreaks in China and in Middle East. SARS-CoV2 spread from China where the first infections were described in December 2019 and is responsible for the respiratory symptoms that can lead to acute respiratory distress syndrome. A cytokine storm has been shown in patients who develop fatal complications, as observed in past coronavirus infections. The management includes ventilatory support and broad-spectrum antiviral drugs, empirically utilized, as a targeted therapy and vaccines have not been developed. Based upon our limited knowledge on the pathogenesis of CoViD-19, a potential role of some anti-rheumatic drugs may be hypothesized, acting as direct antivirals or targeting host immune response. Antimalarial drugs, commonly used in rheumatology, may alter the lysosomal proteases that mediates the viral entry into the cell and have demonstrated efficacy in improving the infection. Anti-IL-1 and anti-IL-6 may interfere with the cytokine storm in severe cases and use of tocilizumab has shown good outcomes in a small cohort. Baricitinib has both antiviral and anti-inflammatory properties. Checkpoints inhibitors such as anti-CD200 and anti-PD1 could have a role in the treatment of CoViD-19. Rheumatic disease patients taking immunosuppressive drugs should be recommended to maintain the chronic therapy, prevent infection by avoiding social contacts and pausing immunosuppressants in case of infection. National and international registries are being created to collect data on rheumatic patients with CoViD-19