38,243 research outputs found
The Space Object Ontology
Achieving space domain awareness requires the
identification, characterization, and tracking of space objects.
Storing and leveraging associated space object data for purposes
such as hostile threat assessment, object identification, and
collision prediction and avoidance present further challenges.
Space objects are characterized according to a variety of
parameters including their identifiers, design specifications,
components, subsystems, capabilities, vulnerabilities, origins,
missions, orbital elements, patterns of life, processes, operational
statuses, and associated persons, organizations, or nations. The
Space Object Ontology provides a consensus-based realist
framework for formulating such characterizations in a
computable fashion. Space object data are aligned with classes
and relations in the Space Object Ontology and stored in a
dynamically updated Resource Description Framework triple
store, which can be queried to support space domain awareness
and the needs of spacecraft operators. This paper presents the
core of the Space Object Ontology, discusses its advantages over
other approaches to space object classification, and demonstrates
its ability to combine diverse sets of data from multiple sources
within an expandable framework. Finally, we show how the
ontology provides benefits for enhancing and maintaining longterm
space domain awareness
Some Issues on Ontology Integration
The word integration has been used with different
meanings in the ontology field. This article
aims at clarifying the meaning of the word âintegrationâ
and presenting some of the relevant work
done in integration. We identify three meanings of
ontology âintegrationâ: when building a new ontology
reusing (by assembling, extending, specializing
or adapting) other ontologies already available;
when building an ontology by merging several
ontologies into a single one that unifies all of
them; when building an application using one or
more ontologies. We discuss the different meanings
of âintegrationâ, identify the main characteristics
of the three different processes and proposethree words to distinguish among those meanings:integration, merge and use
Philosophy of Blockchain Technology - Ontologies
About the necessity and usefulness of developing a philosophy specific to the blockchain technology, emphasizing on the ontological aspects. After an Introduction that highlights the main philosophical directions for this emerging technology, in Blockchain Technology I explain the way the blockchain works, discussing ontological development directions of this technology in Designing and Modeling. The next section is dedicated to the main application of blockchain technology, Bitcoin, with the social implications of this cryptocurrency. There follows a section of Philosophy in which I identify the blockchain technology with the concept of heterotopia developed by Michel Foucault and I interpret it in the light of the notational technology developed by Nelson Goodman as a notational system. In the Ontology section, I present two developmental paths that I consider important: Narrative Ontology, based on the idea of order and structure of history transmitted through Paul Ricoeur's narrative history, and the Enterprise Ontology system based on concepts and models of an enterprise, specific to the semantic web, and which I consider to be the most well developed and which will probably become the formal ontological system, at least in terms of the economic and legal aspects of blockchain technology. In Conclusions I am talking about the future directions of developing the blockchain technology philosophy in general as an explanatory and robust theory from a phenomenologically consistent point of view, which allows testability and ontologies in particular, arguing for the need of a global adoption of an ontological system for develop cross-cutting solutions and to make this technology profitable.
CONTENTS:
Abstract
Introducere
Tehnologia blockchain
- Proiectare
- Modele
Bitcoin
Filosofia
Ontologii
- Ontologii narative
- Ontologii de intreprindere
Concluzii
Note
Bibliografie
DOI: 10.13140/RG.2.2.24510.3360
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Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems
There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation âchallengeâ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying âchallengeâ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings
Demo: A community based approach for managing ontology alignments
The Semantic Web is rapidly becoming a defacto distributed repository for semantically represented data, thus leveraging on the added on value of the network effect. Various ontology mapping techniques and tools have been devised to facilitate the bridging and integration of distributed data repositories. Nevertheless, ontology mapping can benefit from human supervision to increase accuracy of results. The spread of Web 2.0 approaches demonstrate the possibility of using collaborative techniques for reaching consensus. While a number of prototypes for collaborative ontology construction are being developed, collaborative ontology mapping is not yet well investigated. In this paper, we describe aprototype that combines off-the-shelf ontology mapping tools with social software techniques to enable users to collaborate on mapping ontologies. Emphasis is put on the reuse of user generated mappings to improve the accuracy of automatically generated ones
Medical 3D printing: methods to standardize terminology and report trends.
BackgroundMedical 3D printing is expanding exponentially, with tremendous potential yet to be realized in nearly all facets of medicine. Unfortunately, multiple informal subdomain-specific isolated terminological 'silos' where disparate terminology is used for similar concepts are also arising as rapidly. It is imperative to formalize the foundational terminology at this early stage to facilitate future knowledge integration, collaborative research, and appropriate reimbursement. The purpose of this work is to develop objective, literature-based consensus-building methodology for the medical 3D printing domain to support expert consensus.ResultsWe first quantitatively survey the temporal, conceptual, and geographic diversity of all existing published applications within medical 3D printing literature and establish the existence of self-isolating research clusters. We then demonstrate an automated objective methodology to aid in establishing a terminological consensus for the field based on objective analysis of the existing literature. The resultant analysis provides a rich overview of the 3D printing literature, including publication statistics and trends globally, chronologically, technologically, and within each major medical discipline. The proposed methodology is used to objectively establish the dominance of the term "3D printing" to represent a collection of technologies that produce physical models in the medical setting. We demonstrate that specific domains do not use this term in line with objective consensus and call for its universal adoption.ConclusionOur methodology can be applied to the entirety of medical 3D printing literature to obtain a complete, validated, and objective set of recommended and synonymous definitions to aid expert bodies in building ontological consensus
The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction
The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools /features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned
MultiFarm: A benchmark for multilingual ontology matching
In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual
ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different
languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages â Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish â we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism
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