OntoMathPROOntoMath^{PRO} Ontology: A Linked Data Hub for Mathematics

In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.Comment: 15 pages, 6 images, 1 table, Knowledge Engineering and the Semantic Web - 5th International Conferenc

Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)

With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e. the missing relations in the is-a hierarchy. Previous work has dealt with the case of taxonomies. In this work we extend the scope to deal with ALC ontologies that can be represented using acyclic terminologies. We present algorithms and discuss a system

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Greenhouse gas emission factors associated with rewetting of organic soils

Drained organic soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils may reduce GHG emissions and could also create suitable conditions for return of the carbon (C) sink function characteristic of undrained organic soils. In this article we expand on the work relating to rewetted organic soils that was carried out for the 2014 Intergovernmental Panel on Climate Change (IPCC) Wetlands Supplement. We describe the methods and scientific approach used to derive the Tier 1 emission factors (the rate of emission per unit of activity) for the full suite of GHG and waterborne C fluxes associated with rewetting of organic soils. We recorded a total of 352 GHG and waterborne annual flux data points from an extensive literature search and these were disaggregated by flux type (i.e. CO2, CH4, N2O and DOC), climate zone and nutrient status. Our results showed fundamental differences between the GHG dynamics of drained and rewetted organic soils and, based on the 100 year global warming potential of each gas, indicated that rewetting of drained organic soils leads to: net annual removals of CO2 in the majority of organic soil classes; an increase in annual CH4 emissions; a decrease in N2O and DOC losses; and a lowering of net GHG emissions. Data published since the Wetlands Supplement (n = 58) generally support our derivations. Significant data gaps exist, particularly with regard to tropical organic soils, DOC and N2O. We propose that the uncertainty associated with our derivations could be significantly reduced by the development of country specific emission factors that could in turn be disaggregated by factors such as vegetation composition, water table level, time since rewetting and previous land use history

Correction of both immunodeficiency and hypoparathyroidism by thymus transplantation in complete DiGeorge Syndrome

Combined immune deficiency due to athymia in patients with complete DiGeorge syndrome can be corrected by allogeneic thymus transplantation. Hypoparathyroidism is a frequent concomitant clinical problem in these patients, which persists after thymus transplantation. Cotransplantation of allogeneic thymus and parental parathyroid tissue has been attempted but does not achieve durable correction of the patients' hypoparathyroidism due to parathyroid graft rejection. Surprisingly, we observed correction of hypoparathyroidism in one patient after thymus transplantation. Immunohistochemical analysis and fluorescence in situ hybridization confirmed the presence of allogeneic parathyroid tissue in the patient's thymus transplant biopsy. Despite a lack of HLA‐matching between thymus donor and recipient, the reconstituted immune system displays tolerance toward the thymus donor. Therefore we expect this patient's hypoparathyroidism to be permanently cured. It is recognised that ectopic parathyroid tissue is not infrequently found in the thymus. If such thymuses could be identified, we propose that their use would offer a compelling approach to achieving lasting correction of both immunodeficiency and hypoparathyroidism

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Analytic Metaphysics versus Naturalized Metaphysics: The Relevance of Applied Ontology

The relevance of analytic metaphysics has come under criticism: Ladyman & Ross, for instance, have suggested do discontinue the field. French & McKenzie have argued in defense of analytic metaphysics that it develops tools that could turn out to be useful for philosophy of physics. In this article, we show first that this heuristic defense of metaphysics can be extended to the scientific field of applied ontology, which uses constructs from analytic metaphysics. Second, we elaborate on a parallel by French & McKenzie between mathematics and metaphysics to show that the whole field of analytic metaphysics, being useful not only for philosophy but also for science, should continue to exist as a largely autonomous field

How Can Reasoner Performance of ABox Intensive Ontologies Be Predicted?

Reasoner performance prediction of ontologies in OWL 2 language has been studied so far from different dimensions. One key aspect of these studies has been the prediction of how much time a particular task for a given ontology will consume. Several approaches have adopted different machine learning techniques to predict time consumption of ontologies already. However, these studies focused on capturing general aspects of the ontologies (i.e., mainly the complexity of their TBoxes), while paying little attention to ABox intensive ontologies. To address this issue, in this paper, we propose to improve the representativeness of ontology metrics by developing new metrics which focus on the ABox features of ontologies. Our experiments show that the proposed metrics contribute to overall prediction accuracy for all ontologies in general without causing side-effects

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description