In this paper, we introduce the problem of rewriting finite formal languages using syntactic macros such that the rewriting is minimal in size. We present polynomial-time algorithms to solve variants of this problem and show their correctness. To demonstrate the practical relevance of the proposed problems and the feasibility and effectiveness of our algorithms in practice, we apply these to biomedical ontologies authored in OWL. We find that such rewritings can significantly reduce the size of ontologies by capturing repeated expressions with macros. This approach not only offers valuable assistance in enhancing ontology quality and comprehension but can also be seen as a general methodology for evaluating features of rewriting systems (including syntactic macros, templates, or other forms of rewriting rules), which can be analyzed in terms of their influence on computational problems.

Parkinson's disease (PD) is a chronic neurological disease that negatively influences patients' quality of life (QoL). Reduced social interactions are a result of both PD symptoms and impairment in QoL, which is known as social withdrawal. Smartphones are suitable for monitoring social behaviour under the notion of digital phenotyping, as people spend significant time socialising on them. We implement year-long longitudinal research using smartphones to study social withdrawal in PD. In addition to an entire year of 24/7 continuous monitoring on smartphones, weekly social ratings and QoL are provided as self-report ground truth. Twenty-three features are extracted from more than 10 million raw data entries collected from eight participants. These features are then used to build models to reflect participants' self-report ratings. We consider the interactions that happen on smartphones, such as calls, messages and social media, and use smartphone sensors to infer face-to-face interactions. By applying multiple linear regression and Naive Bayes, our model is significantly better than just assigning mean and random guesses. The results suggest that our approach provides a more granular method for tracking social behaviour in people with PD via smartphones, and it could benefit wider communities where the social impact on patients needs attention.

Artificial modelling of tumours can provide insights in cancer biology and offer a powerful complement to laboratory research. A common approach is to simulate tumour growth in a two-dimensional environment and to then generalize results to a three-dimensional one. Literature suggests this strategy fails to adequately capture the underlying biology and may provide misleading results. To establish whether 2D models may form a viable alternative to 3D ones, we developed a model comprising cancer cell growth and proliferation and soluble diffusion to replicate experiments previously performed in a laboratory. We made use of established parametrization techniques to configure our simulations and novel error estimation strategies to evaluate them. Our results suggest that the same simulation in 2D and 3D yields significantly different results. Further, that the cause of this discrepancy lies in the spatial geometry of 2D simulations which does not allow for the formation of hypoxic regions in the tumour mass. We conclude with a recommendation that due to the limitations of 2D simulations, and the negligible difference in cost between the two approaches, 3D simulations should be employed over 2D ones.

With the growth of ontologies used in diverse application areas, the need for module extraction and modularisation techniques has risen. The notion of the modular structure of an ontology, which comprises a suitable set of base modules together with their logical dependencies, has the potential to help users and developers in comprehending, sharing, and maintaining an ontology. We have developed a new modular structure, called atomic decomposition (AD), which is based on modules that provide strong logical properties, such as locality-based modules. In this article, we present the theoretical foundations of AD, review its logical and computational properties, discuss its suitability as a modular structure, and report on an experimental evaluation of AD. In addition, we discuss the concept of a modular structure in ontology engineering and provide a survey of existing decomposition approaches.

Ecological Momentary Assessment (EMA) tools are used to monitor the thoughts and feelings of people in their everyday lives over time. In this paper we examine the feasibility of multi-item, multi-subject Hidden Markov Models (HMMs) to identify response clusters in people with schizophrenia. Data comprise 49 participants from two randomised clinical trials using the mobile app ClinTouch, an EMA tool for daily monitoring of schizophrenia symptoms. The app was used for up to 12 weeks (median follow-up 83 days, 78% response rate). We find that a 3-cluster model with 3 states per cluster performs best amongst the configurations tested, and the feasibility of HMMs as applied to multi-item EMA data is demonstrated. However, there is substantial heterogeneity between participants within each hidden state for which sampling error due to short observation periods is a likely contributor. More data are needed to validate and refine the modelling approach taken here.

, the logic that underpins the popular Web Ontology Language (OWL), has a high worst case complexity (N2Exptime). Decomposing the ontology into modules prior to classification, and then classifying the composites one-by-one, has been suggested as a way to mitigate this complexity in practice. Modular reasoning is currently motivated by the potential for reducing the hardness of subsumption tests, reducing the number of necessary subsumption tests and integrating efficient delegate reasoners. To date, we have only a limited idea of what we can expect from modularity as an optimisation technique. We present sound evidence that, while the impact of subsumption testing is significant only for a small number of ontologies across a popular collection of 330 ontologies (BioPortal), modularity has a generally positive effect on subsumption test hardness (2-fold mean reduction in our sample). More than 50% of the tests did not change in hardness at all, however, and we observed large differences across reasoners. We conclude (1) that, in general, optimisations targeting subsumption test hardness need to be well motivated because of their comparatively modest overall impact on classification time and (2) that employing modularity for optimisation should not be motivated by beneficial effects on subsumption test hardness alone.

The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that 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 discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.

Clinical assessment scales, such as the Glasgow coma scale, are a core part of Electronic Health Records (EHRs). However, fully representing them in an OWL ontology is challenging: In particular, the determination of a score from patient's observations and clinical findings requires forms of aggregation and addition which are either tedious in OWL 2 or merely impractical due to combinatorial explosion. To solve this problem, we propose to separate the representation of the structure and content of an assessment scale from its enactment with the former being captured in OWL 2 and the latter being determined by a SPARQL query. The paper reports the results of a systematic review of 104 well-established clinical assessment scales along with the performance of the SPARQL queries proposed when executed with the query engine ARQ for Jena over HL7 CDA level three documents.

Medical vocabulary is complex, and convoluted not least because of large numbers of compound terms. Formalized medical terminologies such as SNOMED-CT and ICD-10 take one of two strategies when representing medical language: so-called pre-coordination where valid compound terms are included explicitly and post-coordination where the terminology consists of a basis and a generative function from which the compound terms may be derived. However, these notions are not used with particular precision. Here we provide a formalization of the notion of coordination, a technique for estimating the degree of coordination in a given system, and an examination, based on our technique, of the coordination level of a number of terminologies.

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.

Given the high expressivity of the Web Ontology Language OWL 2, there is a potential for great diversity in the logical content of OWL ontologies. The fact that many naturally occurring entailments of such ontologies have multiple justifications indicates that ontologies often overdetermine their consequences, suggesting a diversity in supporting reasons. On closer inspection, however, we often find that justifications---even for multiple entailments---appear to be structurally similar, suggesting that their multiplicity might be due to diverse material, not formal grounds for an entailment.

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 OWL's 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.

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.

Extracting a subset of a given ontology that cap-tures all the ontology’s knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based mod-ules. However, a single module does not allow us to understand neither topicality, connectedness, struc-ture, or superfluous parts of an ontology, nor agree-ment between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such mod-ules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of locality-based modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that en-ables us to efficiently extract a polynomial repres-entation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experi-ments carried out and results obtained. 1

XACML has emerged as a popular access control language on the Web, but because of its rich expressiveness, it has proved difficult to analyze in an automated fashion. In this paper, we present a formalization of XACML using description logics (DL), which are a decidable fragment of First-Order logic. This formalization allows us to cover a more expressive subset of XACML than propositional logic-based analysis tools, and in addition we provide a new analysis service (policy redundancy). Also, mapping XACML to description logics allows us to use off-the-shelf DL reasoners for analysis tasks such as policy comparison, verification and querying. We provide empirical evaluation of a policy analysis tool that was implemented on top of open source DL reasoner Pellet.

As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. Existing ontology development environments provide some limited support, in conjunction with a reasoner, for detecting and diagnosing errors in OWL ontologies. Typically these are restricted to the mere detection of, for example, unsatisfiable concepts. We have integrated a number of simple debugging cues generated from our description logic reasoner, Pellet, in our hypertextual ontology development environment, Swoop. These cues, in conjunction with extensive undo/redo and Annotea based collaboration support in Swoop, significantly improve the OWL debugging experience, and point the way to more general improvements in the presentation of an ontology to new users.

The task of building an open and scalable ontology browsing and editing tool based on OWL, the first standardized Web-oriented ontology language, requires the rethinking of critical user interface and ontological engineering issues. In this article, we describe Swoop, a browser and editor specifically tailored to OWL ontologies. Taking a “Web view” of things has proven quite instructive, and we discuss some insights into Web ontologies that we gained through our experience with Swoop, including issues related to the display, navigation, editing, and collaborative annotation of OWL ontological data.