Alessandro Solimando

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration

A major challenge in information management today is the integration of huge amounts of data distributed across multiple data sources. A suggested approach to this problem is ontology-based data integration where legacy data systems are integrated via a common ontology that represents a unified global view over all data sources. However, data is often not natively born using these ontologies. Instead, much data resides in legacy relational databases. Therefore, mappings that relate the legacy relational data sources to the ontology need to be constructed. Recent techniques and systems that automatically construct such mappings have been developed. The quality metrics of these systems are, however, often only based on self-designed benchmarks. This paper introduces a new publicly available benchmarking suite called RODI, which is designed to cover a wide range of mapping challenges in Relational-to-Ontology Data Integration scenarios. RODI provides a set of different relational data sources and ontologies representing a wide range of mapping challenges as well as a scoring function with which the performance of relational-to-ontology mapping construction systems may be evaluated.

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.

Detecting and Correcting Conservativity Principle Violations in Ontology Mappings

Ontologies play a key role in the development of the Semantic Web and are being used in many diverse application domains such as biomedicine and energy industry. An application domain may have been modeled according to different points of view and purposes. This situation usually leads to the development of different ontologies that intuitively overlap, but that use different naming and modeling conventions.

Pushing the limits of OWL 2 reasoners in ontology alignment repair problems

Ontologies play a key role in the development of the Semantic Web and are being used in many diverse application domains such as biomedicine and e-commerce. An application domain may have been modeled according to different points of view and purposes. This situation usually leads to the development of different ontologies that intuitively overlap, but that use different naming and modeling conventions. The problem of (semi-)automatically integrating independently developed ontologies through mappings, is usually referred to as the ontology matching problem. Ontology matching systems, however, rely on lexical and structural heuristics, and the integration of the input ontologies and the mappings may lead to many undesired logical consequences, which could sensibly diminish their usefulness. The present paper, on the one hand aims at veryfing the hypothesis that classification of large ontologies via mappings still poses a challenge to OWL 2 reasoners. On the other it also explores the applicability of OWL 2 reasoning for the repair of unintended entailments (namely, unsatisfiable concepts or violations of the conservativity principle). In this paper we provide an update on the feasibility of using OWL 2 reasoners to repair the integration of ontologies via mappings, providing a more accurate evaluation of the feasibility of extracting all the justifications. Additionally, the current evaluation also encompasses the analysis of the use of OWL 2 reasoners for solving the violations of the so-called conservativity principle.

Static analysis of XML document adaptations

In this paper we propose a framework for XML data and schema co-evolution that allows to check whether a user-proposed document adaptation (i.e., a sequence of document update operations intended to adapt the documents valid for a schema to a new schema) is guaranteed to produce a document valid for the updated schema. The proposed framework can statically determine, working only with the automata related to the original and modified schema, if the document update operation sequence will re-establish document validity, thus avoiding the very expensive run-time revalidation of the set of involved documents that is usually performed upon schema update.

RODI: Benchmarking relational-to-ontology mapping generation quality

Accessing and utilizing enterprise or Web data that is scattered across multiple data sources is an important task for both applications and users. Ontology-based data integration, where an ontology mediates between the raw data and its consumers, is a promising approach to facilitate such scenarios. This approach crucially relies on useful mappings to relate the ontology and the data, the latter being typically stored in relational databases. A number of systems to support the construction of such mappings have recently been developed. A generic and effective benchmark for reliable and comparable evaluation of the practical utility of such systems would make an important contribution to the development of ontology-based data integration systems and their application in practice. We have proposed such a benchmark, called RODI. In this paper, we present a new version of RODI, which significantly extends our previous benchmark, and we evaluate various systems with it. RODI includes test scenarios from the domains of scientific conferences, geographical data, and oil and gas exploration. Scenarios are constituted of databases, ontologies, and queries to test expected results. Systems that compute relational-to-ontology mappings can be evaluated using RODI by checking how well they can handle various features of relational schemas and ontologies, and how well the computed mappings work for query answering. Using RODI, we conducted a comprehensive evaluation of seven systems.

Synthetising changes in XML documents as PULs

The ability of efficiently detecting changes in XML documents is crucial in many application contexts. If such changes are represented as XQuery Update Pending Update Lists (PULs), they can then be applied on documents using XQuery Update engines, and document management can take advantage of existing composition, inversion, reconciliation approaches developed in the update processing context. The paper presents an XML edit-script generator with the unique characteristic of using PULs as edit-script language and improving the state of the art from both the performance and the generated edit-script quality perspectives.

Supporting shared hypothesis testing in the biomedical domain

BackgroundPathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses.ResultsIn this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences.ConclusionsWe evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.

Ontology Adaptation upon Updates

Ontologies, like any other model, change over time due to modifications in the modeled domain, deeper understanding of the domain by the modeler, error corrections, simple refactoring or shift of modeling granularity level. Local changes usually impact the remainder of the ontology as well as any other data and metadata defined over it. The massive size of ontologies and their possible fast update rate requires automatic adaptation methods for relieving ontology engineers from a manual intervention, in order to allow them to focus mainly on high-level inspection. This paper, in spirit of the Principle of minimal change, proposes a fully automatic ontology adaptation approach that reacts to ontology updates and computes sound reformulations of ontological axioms triggered by the presence of certain preconditions. The rule-based adaptation algorithm covers up to \(\mathcal{SROIQ}\) DL.