Semantic technology for open data publishing

After years of focus on technologies for big data storing and processing, many observers are pointing out that making sense of big data cannot be done without suitable tools for conceptualizing, preparing, and integrating data (see http://www.dbta.com/). Research in the last years has shown that taking into account the semantics of data is crucial for devising powerful data integration solutions. In this work we focus on a specific paradigm for semantic data integration, named "Ontology-Based Data Access" (OBDA), proposed in [1-4]. According to such paradigm, the client of the information system is freed from being aware of how data and processes are structured in concrete resources (databases, software programs, services, etc.), and interacts with the system by expressing her queries and goals in terms of a conceptual representation of the domain of interest, called ontology. More precisely, a system realizing the vision of OBDA is constituted by three components: The ontology, whose goal is to provide a formal, clean and high level representation of the domain of interest, and constitutes the component with which the clients of the system (both humans and software programs) interact. fiedata source layer, representing the existing data sources in the information system, which are managed by the processes and services operating on their data. e mapping between the two layers, which is an explicit representation of the relationship between the data sources and the ontology, and is used to translate the operations on the ontology (e.g., query answering) in terms of concrete actions on the data sources.

Non-Monotonic Ontology-based Abstractions of Data Services

In Ontology-Based Data Access (OBDA), a domain ontology is linked to the data sources of an organization in order to query, integrate and manage data through the concepts and relations of the domain of interest, thus abstracting from the structure and the implementation details of the data layer. While the great majority of contributions in OBDA in the last decade have been concerned with the issue of computing the answers of queries expressed over the ontology, recent papers address a different problem, namely the one of providing suitable abstractions of data services, i.e., characterizing or explaining the semantics of queries over the sources in terms of queries over the domain ontology. Current works on this subject are based on expressing abstractions in terms of unions of conjunctive queries (UCQs). In this paper we advocate the use of a non-monotonic language for this task. As a first contribution, we present a simple extension of UCQs with nonmonotonic features, and show that non-monotonicity provides more expressive power in characterizing the semantics of data services. A second contribution is to prove that, similarly to the case of monotonic abstractions, depending on the expressive power of the languages used to specify the various components of the OBDA system, there are cases where neither perfect nor approximated abstractions exist for a given data service. As a third contribution, we single out interesting special cases where the existence of abstractions is guaranteed, and we present algorithms for computing such abstractions in these cases

Monotone Abstractions in Ontology-Based Data Management

In Ontology-Based Data Management (OBDM), an abstraction of a source query q is a query over the ontology capturing the semantics of q in terms of the concepts and the relations available in the ontology. Since a perfect characterization of a source query may not exist, the notions of best sound and complete approximations of an abstraction have been introduced and studied in the typical OBDM context, i.e., in the case where the ontology is expressed in DL-Lite, and source queries are expressed as unions of conjunctive queries (UCQs). Interestingly, if we restrict our attention to abstractions expressed as UCQs, even best approximations of abstractions are not guaranteed to exist. Thus, a natural question to ask is whether such limitations affect even larger classes of queries. In this paper, we answer this fundamental question for an essential class of queries, namely the class of monotone queries. We define a monotone query language based on disjunctive Datalog enriched with an epistemic operator, and show that its expressive power suffices for expressing the best approximations of monotone abstractions of UCQs

Semantic characterization of data services through ontologies

We study the problem of associating formal semantic descriptions to data services. We base our proposal on the Ontology-Based Data Access paradigm, where a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on the notion of source-to- ontology (s-to-o) rewriting, which comes in three variants, called sound, complete and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an s-to-o rewriting of a given data service), and computation (computing an s-to-o rewriting of a data service)

Answering conjunctive queries with inequalities in DL-liteℛ

In the context of the Description Logic DL-Liteℛ≠, i.e., DL-Liteℛ without UNA and with inequality axioms, we address the problem of adding to unions of conjunctive queries (UCQs) one of the simplest forms of negation, namely, inequality. It is well known that answering conjunctive queries with unrestricted inequalities over DL-Liteℛ ontologies is in general undecidable. Therefore, we explore two strategies for recovering decidability, and, hopefully, tractability. Firstly, we weaken the ontology language, and consider the variant of DL-Liteℛ≠ corresponding to RDFS enriched with both inequality and disjointness axioms. Secondly, we weaken the query language, by preventing inequalities to be applied to existentially quantified variables, thus obtaining the class of queries named UCQ≠,bs. We prove that in the two cases, query answering is decidable, and we provide tight complexity bounds for the problem, both for data and combined complexity. Notably, the results show that answering UCQ≠,bs over DL-Liteℛ≠ ontologies is still in AC0 in data complexity

Epistemic Disjunctive Datalog for Querying Knowledge Bases

The Datalog query language can express several powerful recursive properties, often crucial in real-world scenarios. While answering such queries is feasible over relational databases, the picture changes dramatically when data is enriched with intensional knowledge. It is indeed well-known that answering Datalog queries is undecidable already over lightweight knowledge bases (KBs) of the DL-Lite family. To overcome this issue, we propose a new query language based on Disjunctive Datalog rules combined with a modal epistemic operator. Rules in this language interact with the queried KB exclusively via the epistemic operator, thus extracting only the information true in every model of the KB. This form of interaction is crucial for not falling into undecidability. The contribution provided by this paper is threefold. First, we illustrate the syntax and the semantics of the novel query language. Second, we study the expressive power of different fragments of our new language and compare it with Disjunctive Datalog and its variants. Third, we outline the precise data complexity of answering queries in our new language over KBs expressed in various well-known formalisms

Evaluation of SARS-CoV-2 viral RNA in fecal samples

The need for timely establishment of a complete diagnostic protocol of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is demanded worldwide. We selected 15 positive novel coronavirus disease 19 (COVID-19) patients with mild or no symptom. Initially, fecal samples were negative in the 67% (10/15) of the cases, while 33% (5/10) of the cases were positive. After serial virus RNA testing, 73% (11/15) of the cases resulted positive to fecal specimens. In particular, 15 days after the first positive respiratory specimens test, 6 fecal specimens became positive for SARS-CoV-2 RNA, while 13 respiratory test returned negative result. In conclusion, qRT-PCR assays of fecal specimens, is an important step to control infection, suggesting that samples remained positive for SARS-CoV-2 RNA longer time then respiratory tract samples. Our results enhance the recent knowledge on this emerging infectious disease and offer suggestions for a more complete diagnostic strategy

Querying OWL 2 QL ontologies under the SPARQL Metamodeling Semantics Entailment Regime

OWL2QL is the profile of OWL2 targeted to Ontology-Based Data Access (OBDA) scenarios, where large amount of data are to be accessed, and thus answering conjunctive queries over data is the main task. However, this task is quite restrained wrt the classical KR Ask-and-Tell framework based on query- ing the whole theory, not only facts (data). If we use SPARQL as query language, we get much closer to this ideal. Indeed, SPARQL queries over OWL 2 QL, under the so-called Direct Semantics Entailment Regime, may comprise any assertion expressible in the language, i.e., both ABox atoms and TBox atoms, including inequalities expressed by means of DifferentIndividuals. Nevertheless this regime is hampered by the assumption that variables in queries need to be typed, meaning that the same variable cannot occur in positions of different types, e.g., both in class and individual position (punning). In this paper we dismiss this limiting assumption by resorting to a recent meta modeling semantics and show that query answering in the resulting entailment regime is polynomially compilable into Datalog (and hence PTIME wrt both TBox and ABox)

Dynamics of SARS-CoV-2-Specific B Cell Memory Responses in Infected and Vaccinated Individuals

Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly resulted in a pandemic constituting a global health emergency. As an indicator of long-term immune protection from reinfection with the SARS-CoV-2 virus, the presence of memory B cells (MBCs) should be evaluated. Since the beginning of COVID-19 pandemic, several variants of concerns have been detected, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1/B.1.1.28.1), Delta (B.1.617.2), and Omicron (BA.1) variants with several different mutations, causing serious concern regarding the increased frequency of reinfection, and limiting the effectiveness of the vaccine response. At this regard, we investigated SARS-CoV-2-specific cellular immune responses in four different cohorts: COVID-19, COVID-19 infected and vaccinated, vaccinated, and negative subjects. We found that MBC response to SARS-CoV-2 at more than 11 months postinfection was higher in the peripheral blood of all COVID-19 infected and vaccinated subjects respect to all the other groups. Moreover, to better characterize the differences of SARS-CoV-2 variants immune responses, we genotyped SARS-CoV-2-positive samples from the patients' cohort. We found a higher level of immunoglobulin M+ (IgM+) and IgG+ spike MBCs in SARS-CoV-2-positive patients (5-8 months after symptoms onset) infected with the SARS-CoV-2-Delta variant compared with the SARS-CoV-2-Omicron variant implying a higher immune memory response. Our findings showed that MBCs persist more than 11 months after primary infection indicating a different involvement of the immune system according to the different SARS-CoV-2 variant that infected the host