Matthew Horridge

Using SPARQL to query bioportal ontologies and metadata

BioPortal is a repository of biomedical ontologies--the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other languages, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF based serializations of all these ontologies and their metadata at sparql.bioontology.org . This dataset contains 203M triples, representing both content and metadata for the 300+ ontologies; and 9M mappings between terms. This endpoint can be queried with SPARQL which opens new usage scenarios for the biomedical domain. This paper presents lessons learned from having redesigned several applications that today use this SPARQL endpoint to consume ontological data.

WebProtégé: A Collaborative Web Based Platform for Editing Biomedical Ontologies

UNLABELLED WebProtégé is an open-source Web application for editing OWL 2 ontologies. It contains several features to aid collaboration, including support for the discussion of issues, change notification and revision-based change tracking. WebProtégé also features a simple user interface, which is geared towards editing the kinds of class descriptions and annotations that are prevalent throughout biomedical ontologies. Moreover, it is possible to configure the user interface using views that are optimized for editing Open Biomedical Ontology (OBO) class descriptions and metadata. Some of these views are shown in the Supplementary Material and can be seen in WebProtégé itself by configuring the project as an OBO project. AVAILABILITY AND IMPLEMENTATION WebProtégé is freely available for use on the Web at http://webprotege.stanford.edu. It is implemented in Java and JavaScript using the OWL API and the Google Web Toolkit. All major browsers are supported. For users who do not wish to host their ontologies on the Stanford servers, WebProtégé is available as a Web app that can be run locally using a Servlet container such as Tomcat. Binaries, source code and documentation are available under an open-source license at http://protegewiki.stanford.edu/wiki/WebProtege.

Extracting justifications from bioportal ontologies

This paper presents an evaluation of state of the art black box justification finding algorithms on the NCBO BioPortal ontology corpus. This corpus represents a set of naturally occurring ontologies that vary greatly in size and expressivity. The results paint a picture of the performance that can be expected when finding all justifications for entailments using black box justification finding techniques. The results also show that many naturally occurring ontologies exhibit a rich justificatory structure, with some ontologies having extremely high numbers of justifications per entailment.

Toward cognitive support for OWL justifications

Justifications are the dominant form of explanation for entailments of OWL ontologies, with popular OWL ontology editors, such as Protege 4, providing justification-based explanation facilities. A justification is a minimal subset of an ontology which is sufficient for an entailment to hold; they correspond to the premises of a proof. Unlike proofs, however, justifications do not articulate how their axioms support the entailment. We frequently observe that ontology developers find certain justifications difficult to work with; and while in some cases the sources of difficulty are obvious (such as a large number of axioms), we do not have a good general understanding of what makes justifications easy or difficult for ontology users. In this paper, we present an approach to determining the cognitive complexity of justifications for entailments of OWL ontologies. We describe an exploratory study which forms the basis for a cognitive complexity model that predicts the complexity of OWL justifications, and present the results of validating that model via experiments involving OWL users. This is concluded by an investigation into strategies OWL users apply to support them in understanding justifications. Our contributions include an evaluation of the cognitive complexity model, new insights into the complexity of justifications for entailments of OWL ontologies, a significant corpus with novel analyses of justifications suitable for experimentation, and an experimental protocol suitable for model validation and refinement.

Simplified OWL Ontology Editing for the Web: Is WebProtégé Enough?

Ontology engineering is a task that is notorious for its difficulty. As the group that developed Protege, the most widely used ontology editor, we are keenly aware of how difficult the users perceive this task to be. In this paper, we present the new version of WebProtege that we designed with two main goals in mind: (1) create a tool that will be easy to use while still accounting for commonly used OWL constructs; (2) support collaboration and social interaction around distributed ontology editing as part of the core tool design. We designed this new version of the WebProtege user interface empirically, by analysing the use of OWL constructs in a large corpus of publicly available ontologies. Since the beta release of this new WebProtege interface in January 2013, our users from around the world have created and uploaded 519 ontologies on our server. In this paper, we describe the key features of the new tool and our empirical design approach. We evaluate language coverage in WebProtege by assessing how well it covers the OWL constructs that are present in ontologies that users have uploaded to WebProtege. We evaluate the usability of WebProtege through a usability survey. Our analysis validates our empirical design, suggests additional language constructors to explore, and demonstrates that an easy-to-use web-based tool that covers most of the frequently used OWL constructs is sufficient for many users to start editing their ontologies.

Rendering OWL in Description Logic Syntax

As ontology engineering is inherently a multidisciplinary process, it is necessary to utilize multiple vehicles to present an ontology to a user. In order to examine the formal logical content, description logic renderings of the axioms appear to be a very helpful approach for some. This paper introduces a number of changes made to the OWLAPI’s Open image in new window rendering framework in order to improve the readability, concision, and correctness of translated OWL files, as well as increase the number of renderable OWL files.

Snap-SPARQL: A Java Framework for Working with SPARQL and OWL

We present Snap-SPARQL, which is a Java framework for working with SPARQL and OWL. The framework includes a parser, axiom template API, SPARQL algebra implementation, and graphical user interface components for reading, processing and executing SPARQL queries under the SPARQL 1.1 OWL Entailment Regime. While the framework was originally designed to support the implementation of a SPARQL teaching aid in the form of a Protege plugin, we believe that it is more generally useful and may be of interest to developers and researchers working on SPARQL 1.1 OWL entailment regime implementations and optimisations. The framework is open source and pluggable.

A Domain Specific Ontology Authoring Environment for a Clinical Documentation System

We present a domain specific ontology editor for viewing, updating and managing a clinical documentation knowledge base. The editor is designed to allow clinical content specialists, who do not have a working knowledge of OWL, Semantic Web technologies or knowledge engineering, to quickly generate ontologies that describe clinical documentation templates along with rich bi-directional mappings between these documentation template ontologies and biomedical domain ontologies. While the editor has been designed and implemented for a specific use-case, many of the novel design choices are applicable to more traditional ontology editing environments. Furthermore, we believe that the workflow that is promoted by the editing environment and the partitioning and arrangement of the ontologies that are compiled by the editor are applicable to more general scenarios where two sets of ontologies corresponding to different application sub-domains need to be edited side-by-side and mapped between using a set of binding ontologies.

RightField: scientific knowledge acquisition by stealth through ontology-enabled spreadsheets

RightField is a Java application that provides a mechanism for embedding ontology annotation support for scientific data in Microsoft Excel or Open Office spreadsheets. The result is semantic annotation by stealth, with an annotation process that is less error-prone, more efficient, and more consistent with community standards. By automatically generating RDF statements for each cell a rich, Linked Data querying environment allows scientists to search their data and other Linked Data resources interchangeably, and caters for queries across heterogeneous spreadsheets. RightField has been developed for Systems Biologists but has since adopted more widely. It is open source (BSD license) and freely available from http://www.rightfield.org.uk.

A Study on the Atomic Decomposition of Ontologies

The Atomic Decomposition of an ontology is a succinct representation of the logic-based modules in that ontology. Ultimately, it reveals the modular structure of the ontology. Atomic Decompositions appear to be useful for both user and non-user facing services. For example, they can be used for ontology comprehension and to facilitate reasoner optimisation. In this article we investigate claims about the practicality of computing Atomic Decompositions for naturally occurring ontologies. We do this by performing a replication study using an off-the-shelf Atomic Decomposition algorithm implementation on three large test corpora of OWL ontologies. Our findings indicate that (a) previously published empirical studies in this area are repeatable and verifiable; (b) computing Atomic Decompositions in the vast majority of cases is practical in that it can be performed in less than 30 seconds in 90% of cases, even for ontologies containing hundreds of thousands of axioms; (c) there are occurrences of extremely large ontologies (< 1% in our test corpora) where the polynomial runtime behaviour of the Atomic Decomposition algorithm begins to bite and computations cannot be completed within 12-hours of CPU time; (d) the distribution of number of atoms in the Atomic Decomposition for an ontology appears to be similar for distinct corpora.