Hybrid Refining Approach of PrOnto Ontology

This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction (OKE) algorithm. To ensure robustness of the results while preserving an interdisciplinary approach, the integration of legal and technical knowledge has been carried out as follows. The set of privacy policies was first analysed by the legal experts to discover legal concepts and map the text into PrOnto. The mapping was then provided to computer scientists to perform the OKE analysis. Results were validated by the legal experts, who provided feedbacks and refinements (i.e. new classes and modules) of the ontology according to MeLOn methodology. Three iterations were performed on a set of (development) policies, and a final test using a new set of privacy policies. The results are 75,43% of detection of concepts in the policy texts and an increase of roughly 33% in the accuracy gain on the test set, using the new refined version of PrOnto enriched with SKOS-XL lexicon terms and definitions

Reification and Truthmaking Patterns

Reification is a standard technique in conceptual modeling, which consists of including in the domain of discourse entities that may otherwise be hidden or implicit. However, deciding what should be rei- fied is not always easy. Recent work on formal ontology offers us a simple answer: put in the domain of discourse those entities that are responsible for the (alleged) truth of our propositions. These are called truthmakers. Re-visiting previous work, we propose in this paper a systematic analysis of truthmaking patterns for properties and relations based on the ontolog- ical nature of their truthmakers. Truthmaking patterns will be presented as generalization of reification patterns, accounting for the fact that, in some cases, we do not reify a property or a relationship directly, but we rather reify its truthmakers

Provenance explorer: Customized provenance views using semantic inferencing

This paper presents Provenance Explorer, a secure provenance visualization tool, designed to dynamically generate customized views of scientific data provenance that depend on the viewer's requirements and/or access privileges. Using RDF and graph visualizations, it enables scientists to view the data, states and events associated with a scientific workflow in order to understand the scientific methodology and validate the results. Initially the Provenance Explorer presents a simple, coarse-grained view of the scientific process or experiment. However the GUI allows permitted users to expand links between nodes (input states, events and output states) to reveal more fine-grained information about particular sub-events and their inputs and outputs. Access control is implemented using Shibboleth to identify and authenticate users and XACML to define access control policies. The system also provides a platform for publishing scientific results. It enables users to select particular nodes within the visualized workflow and drag-and-drop them into an RDF package for publication or e-learning. The direct relationships between the individual components selected for such packages are inferred by the rule-inference engine

Making Neural Networks FAIR

Research on neural networks has gained significant momentum over the past few years. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a trend to reuse pre-trained neural networks. As such, neural networks themselves have become research data. In this paper, we first present the neural network ontology FAIRnets Ontology, an ontology to make existing neural network models findable, accessible, interoperable, and reusable according to the FAIR principles. Our ontology allows us to model neural networks on a meta-level in a structured way, including the representation of all network layers and their characteristics. Secondly, we have modeled over 18,400 neural networks from GitHub based on this ontology, which we provide to the public as a knowledge graph called FAIRnets, ready to be used for recommending suitable neural networks to data scientists

Socio-economic impact of bushfires on rural communities and local government in Gippsland and north east Victoria

This study reports on research into the significant social and economic costs of the 2003 bushfires across the Gippsland and North East Regions for both the short and medium terms. It reveals that the bushfires are expected to have considerable direct and indirect effects on these regions for the long term (i.e. the next two to five years). The loss of income and production in the Shires of Alpine, East Gippsland, Indigo and Towong from the time of the fires to May 2003 is estimated conservatively to exceed $120 million. The monograph provides an ethnographically rich assessment of the social and economic impacts of the fires and presents data for the various shires and industries. The study concludes by offering recommendations on how to address the issues identified by the research team so as to improve bushfire preparedness and emergency response management in ways which bring all stakeholders into the process of planning and implementation

Hypothermic retrograde venous perfusion with adenosine cools the spinal cord and reduces the risk of paraplegia after thoracic aortic clamping

AbstractObjective: We evaluated the utility of retrograde venous perfusion to cool the spinal cord and protect neurologic function during aortic clamping. We hypothesized that hypothermic adenosine would preserve the spinal cord during ischemia. Methods: Six swine (group I) underwent thoracic aortic occlusion for 30 minutes at normothermia. Group II animals underwent spinal cooling by retrograde perfusion of the paravertebral veins with hypothermic (4°C) saline solution during aortic occlusion. The spinal cords of group III animals were cooled with a hypothermic adenosine solution in a similar fashion. Intrathecal temperature was monitored and somatosensory evoked potentials assessed the functional status of spinal pathways. Results: Spinal cooling without systemic hypothermia significantly improved neurologic Tarlov scores in group III (4.8 ± 0.2) and group II (3.8 ± 0.4) when compared with group I scores (1.3 ± 0.6) (P < .001). Furthermore, 5 of the 6 animals in group III displayed completely normal neurologic function, whereas only one animal in group II and no animals in group I did (P = .005). Somatosensory evoked potentials were lost 10.6 ± 1.4 minutes after ischemia in group I. In contrast, spinal cooling caused rapid cessation of neural transmission with loss of somatosensory evoked potentials at 6.9 ± 1.2 minutes in group II and 7.0 ± 0.8 minutes in group III (P = .06). Somatosensory evoked potential amplitudes returned to 85% of baseline in group III and 90% of baseline in group II compared with only 10% of baseline in group I (P = .01). Conclusions: We conclude that retrograde cooling of the spinal cord is possible and protects against ischemic injury and that adenosine enhances this effect. The efficacy of this method may be at least partly attributed to a more rapid reduction in metabolic and electrical activity of the spinal cord during ischemia. (J Thorac Cardiovasc Surg 2000;119:588-95

Interoperable multimedia metadata through similarity-based semantic web service discovery

The increasing availability of multimedia (MM) resources, Web services as well as content, on the Web raises the need to automatically discover and process resources out of distributed repositories. However, the heterogeneity of applied metadata schemas and vocabularies – ranging from XML-based schemas such as MPEG-7 to formal knowledge representation approaches – raises interoperability problems. To enable MM metadata interoperability by means of automated similarity-computation, we propose a hybrid representation approach which combines symbolic MM metadata representations with a grounding in so-called Conceptual Spaces (CS). In that, we enable automatic computation of similarities across distinct metadata vocabularies and schemas in terms of spatial distances in shared CS. Moreover, such a vector-based approach is particularly well suited to represent MM metadata, given that a majority of MM parameters is provided in terms of quantified metrics. To prove the feasibility of our approach, we provide a prototypical implementation facilitating similarity-based discovery of publicly available MM services, aiming at federated MM content retrieval out of heterogeneous repositories

Using Crowdsourcing for Fine-Grained Entity Type Completion in Knowledge Bases

Recent years have witnessed the proliferation of large-scale Knowledge Bases (KBs). However, many entities in KBs have incomplete type information, and some are totally untyped. Even worse, fine-grained types (e.g., BasketballPlayer) containing rich semantic meanings are more likely to be incomplete, as they are more difficult to be obtained. Existing machine-based algorithms use predicates (e.g., birthPlace) of entities to infer their missing types, and they have limitations that the predicates may be insufficient to infer fine-grained types. In this paper, we utilize crowdsourcing to solve the problem, and address the challenge of controlling crowdsourcing cost. To this end, we propose a hybrid machine-crowdsourcing approach for fine-grained entity type completion. It firstly determines the types of some “representative” entities via crowdsourcing and then infers the types for remaining entities based on the crowdsourcing results. To support this approach, we first propose an embedding-based influence for type inference which considers not only the distance between entity embeddings but also the distances between entity and type embeddings. Second, we propose a new difficulty model for entity selection which can better capture the uncertainty of the machine algorithm when identifying the entity types. We demonstrate the effectiveness of our approach through experiments on real crowdsourcing platforms. The results show that our method outperforms the state-of-the-art algorithms by improving the effectiveness of fine-grained type completion at affordable crowdsourcing cost.Peer reviewe

Production and Development of Nutraceuticals as Alternative Crops: Implications for Certification and Branding: Part 1

Report of the development of guidelines for identifying and producing selected medicinal plants with high marker compounds; criteria to evaluate the suitability of the plants for commercial organic production; weed control protocols that do not involve use of herbicides; and quality control protocols for harvesting and storing medicinal plants. Implications for certification, branding and marketing of organic medicinal crops for growers and distributors were also discussed