1,395 research outputs found
Git4Voc: Git-based Versioning for Collaborative Vocabulary Development
Collaborative vocabulary development in the context of data integration is
the process of finding consensus between the experts of the different systems
and domains. The complexity of this process is increased with the number of
involved people, the variety of the systems to be integrated and the dynamics
of their domain. In this paper we advocate that the realization of a powerful
version control system is the heart of the problem. Driven by this idea and the
success of Git in the context of software development, we investigate the
applicability of Git for collaborative vocabulary development. Even though
vocabulary development and software development have much more similarities
than differences there are still important differences. These need to be
considered within the development of a successful versioning and collaboration
system for vocabulary development. Therefore, this paper starts by presenting
the challenges we were faced with during the creation of vocabularies
collaboratively and discusses its distinction to software development. Based on
these insights we propose Git4Voc which comprises guidelines how Git can be
adopted to vocabulary development. Finally, we demonstrate how Git hooks can be
implemented to go beyond the plain functionality of Git by realizing
vocabulary-specific features like syntactic validation and semantic diffs
A Query Integrator and Manager for the Query Web
We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions
prototypical implementations
In this technical report, we present prototypical implementations of
innovative tools and methods developed according to the working plan outlined
in Technical Report TR-B-09-05 [23]. We present an ontology modularization and
integration framework and the SVoNt server, the server-side end of an SVN-
based versioning system for ontologies in the Corporate Ontology Engineering
pillar. For the Corporate Semantic Collaboration pillar, we present the
prototypical implementation of a light-weight ontology editor for non-experts
and an ontology based expert finder system. For the Corporate Semantic Search
pillar, we present a prototype for algorithmic extraction of relations in
folksonomies, a tool for trend detection using a semantic analyzer, a tool for
automatic classification of web documents using Hidden Markov models, a
personalized semantic recommender for multimedia content, and a semantic
search assistant developed in co-operation with the Museumsportal Berlin. The
prototypes complete the next milestone on the path to an integral Cor- porate
Semantic Web architecture based on the three pillars Corporate Ontol- ogy
Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search,
as envisioned in [23]
requirements and use cases
In this report, we introduce our initial vision of the Corporate Semantic Web
as the next step in the broad field of Semantic Web research. We identify
requirements of the corporate environment and gaps between current approaches
to tackle problems facing ontology engineering, semantic collaboration, and
semantic search. Each of these pillars will yield innovative methods and tools
during the project runtime until 2013. Corporate ontology engineering will
improve the facilitation of agile ontology engineering to lessen the costs of
ontology development and, especially, maintenance. Corporate semantic
collaboration focuses the human-centered aspects of knowledge management in
corporate contexts. Corporate semantic search is settled on the highest
application level of the three research areas and at that point it is a
representative for applications working on and with the appropriately
represented and delivered background knowledge. We propose an initial layout
for an integrative architecture of a Corporate Semantic Web provided by these
three core pillars
Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents
The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications
An Automated System for the Assessment and Ranking of Domain Ontologies
As the number of intelligent software applications and the number of semantic websites continue to expand, ontologies are needed to formalize shared terms. Often it is necessary to either find a previously used ontology for a particular purpose, or to develop a new one to meet a specific need. Because of the challenge involved in creating a new ontology from scratch, the latter option is often preferable. The ability of a user to select an appropriate, high-quality domain ontology from a set of available options would be most useful in knowledge engineering and in developing intelligent applications. Being able to assess an ontology\u27s quality and suitability is also important when an ontology is developed from the beginning. These capabilities, however, require good quality assessment mechanisms as well as automated support when there are a large number of ontologies from which to make a selection.
This thesis provides an in-depth analysis of the current research in domain ontology evaluation, including the development of a taxonomy to categorize the numerous directions the research has taken. Based on the lessons learned from the literature review, an approach to the automatic assessment of domain ontologies is selected and a suite of ontology quality assessment metrics grounded in semiotic theory is presented. The metrics are implemented in a Domain Ontology Rating System (DoORS), which is made available as an open source web application. An additional framework is developed that would incorporate this rating system as part of a larger system to find ontology libraries on the web, retrieve ontologies from them, and assess them to select the best ontology for a particular task. An empirical evaluation in four phases shows the usefulness of the work, including a more stringent evaluation of the metrics that assess how well an ontology fits its domain and how well an ontology is regarded within its community of users
Insights from an OTTR-centric Ontology Engineering Methodology
OTTR is a language for representing ontology modeling patterns, which enables
to build ontologies or knowledge bases by instantiating templates. Thereby,
particularities of the ontological representation language are hidden from the
domain experts, and it enables ontology engineers to, to some extent, separate
the processes of deciding about what information to model from deciding about
how to model the information, e.g., which design patterns to use. Certain
decisions can thus be postponed for the benefit of focusing on one of these
processes. To date, only few works on ontology engineering where ontology
templates are applied are described in the literature.
In this paper, we outline our methodology and report findings from our
ontology engineering activities in the domain of Material Science. In these
activities, OTTR templates play a key role. Our ontology engineering process is
bottom-up, as we begin modeling activities from existing data that is then, via
templates, fed into a knowledge graph, and it is top-down, as we first focus on
which data to model and postpone the decision of how to model the data.
We find, among other things, that OTTR templates are especially useful as a
means of communication with domain experts. Furthermore, we find that because
OTTR templates encapsulate modeling decisions, the engineering process becomes
flexible, meaning that design decisions can be changed at little cost.Comment: Paper accepted at the 14th Workshop on Ontology Design and Patterns
(WOP 2023
OntoVIP: An ontology for the annotation of object models used for medical image simulation.
International audienceThis paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository
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