Rafael S. Gonçalves

Analysing the evolution of the NCI Thesaurus

The National Cancer Institute (NCI) Thesaurus (NCIt) is a biomedical ontology which has been developed for over a decade. Nearly every month from 2003 through 2011, the NCI has published an updated version of the NCIt to the Web as an OWL ontology (as well as in other formats). We collected all 88 OWL versions of the NCIt available and conducted a cross-sectional study on this corpus to investigate and characterize the evolution of the NCIt. In particular, we gathered and analysed various axiom and entity statistics, and carried out a reasoner performance test over the corpus. Additionally, we extracted two complete sets of pairwise, consecutive diffs: the first set was generated by a purely syntactic difference analysis (based on OWLs notion of “structural equivalence”); for the second set, we also checked whether the additions or removals changed the set of entailments between versions. We discovered a high level of “merely syntactic” removals and additions. We develop a categorization of such changes based on a heuristic inference of the impact of the change. As a result, not only do we get a rich, purely analytic characterization of the change history of the NCIt, but also we generate a realistic test corpus for incremental classification.

Categorising logical differences between OWL ontologies

The analysis of changes between OWL ontologies (in the form of a diff ) is an important service for ontology engineering. A purely syntactic analysis of changes is insufficient to distinguish between changes that have logical impact and those that do not. The current state of the art in semantic diffing ignores logically ineffectual changes and lacks any further characterisation of even significant changes. We present a diff method based on an exhaustive categorisation of effectual and ineffectual changes between ontologies. In order to verify the applicability of our approach we apply it to 88 versions of the National Cancer Institute (NCI) Thesaurus (NCIt), and demonstrate that all categories are realized throughout the corpus. Based on the outcome of the NCIt study we argue that the devised categorisation of changes is helpful for ontology engineers and their understanding of changes carried out between ontologies.

Finding Fault: Detecting Issues in a Versioned Ontology

Understanding ontology evolution is becoming an active topic of interest for ontology engineers, e.g., there exist large collaboratively-developed ontologies but, unlike in software engineering, comparatively little is understood about the dynamics of historical changes, especially at a fine level of granularity. Only recently has there been a systematic analysis of changes across ontology versions, but still at a coarse-grained level. The National Cancer Institute (NCI) Thesaurus (NCIt) is a large, collaboratively-developed ontology, used for various Web and research-related purposes, e.g., as a medical research controlled vocabulary. The NCI has published ten years worth of monthly versions of the NCIt as Web Ontology Language (OWL) documents, and has also published reports on the content of, development methodology for, and applications of the NCIt. In this paper, we carry out a fine-grained analysis of asserted axiom dynamics throughout the evolution of the NCIt from 2003 to 2012. From this, we are able to identify axiomatic editing patterns that suggest significant regression editing events in the development history of the NCIt.

The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments

The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed-the CEDAR Workbench-is a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.

An ontology-driven tool for structured data acquisition using Web forms

BackgroundStructured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be automatically employed in decision making (e.g., in our example application domain of clinical functional assessment, for determining eligibility for disability benefits) based on conclusions derived from acquired data (e.g., assessment of impaired motor function). To use data in these settings, we need it structured in a way that can be exploited by automated reasoning systems, for instance, in the Web Ontology Language (OWL); the de facto ontology language for the Web.ResultsWe tackle the problem of generating Web-based assessment forms from OWL ontologies, and aggregating input gathered through these forms as an ontology of “semantically-enriched” form data that can be queried using an RDF query language, such as SPARQL. We developed an ontology-based structured data acquisition system, which we present through its specific application to the clinical functional assessment domain. We found that data gathered through our system is highly amenable to automatic analysis using queries.ConclusionsWe demonstrated how ontologies can be used to help structuring Web-based forms and to semantically enrich the data elements of the acquired structured data. The ontologies associated with the enriched data elements enable automated inferences and provide a rich vocabulary for performing queries.

The OWL Reasoner Evaluation (ORE) 2015 Resources

The OWL Reasoner Evaluation (ORE) Competition is an annual competition (with an associated workshop) which pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we outline the design of the competition and present the infrastructure used for its execution: the corpora of ontologies, the competition framework, and the submitted systems. All resources are publicly available on the Web, allowing users to easily re-run the 2015 competition, or reuse any of the ORE infrastructure for reasoner experiments or ontology analysis.