9 research outputs found
OntoMaven: Maven-based Ontology Development and Management of Distributed Ontology Repositories
In collaborative agile ontology development projects support for modular
reuse of ontologies from large existing remote repositories, ontology project
life cycle management, and transitive dependency management are important
needs. The Apache Maven approach has proven its success in distributed
collaborative Software Engineering by its widespread adoption. The contribution
of this paper is a new design artifact called OntoMaven. OntoMaven adopts the
Maven-based development methodology and adapts its concepts to knowledge
engineering for Maven-based ontology development and management of ontology
artifacts in distributed ontology repositories.Comment: Pre-print submission to 9th International Workshop on Semantic Web
Enabled Software Engineering (SWESE2013). Berlin, Germany, December 2-5, 201
Provalets: Component-Based Mobile Agents as Microservices for Rule-Based Data Access, Processing and Analytics
Provalets are mobile rule agents for rule-based data access, semantic processing, and inference analytics. They can be dynamically deployed as microservices from Maven repositories into standardized container environments such as OSGi, where they can be used via simple REST calls. The programming model supports rapid prototyping and reuse of Provalets components to build Linked Enterprise Data applications where the sensible corporate data is not transmitted outside the enterprise, but instead the Provalets providing data processing and knowledge inference capabilities are moved closer to the data
Validation and Evaluation
In this technical report, we present prototypical implementations of
innovative tools and methods for personalized and contextualized (multimedia)
search, collaborative ontology evolution, ontology evaluation and cost models,
and dynamic access and trends in distributed (semantic) knowledge, developed
according to the working plan outlined in Technical Report TR-B-12-04. The
prototypes complete the next milestone on the path to an integral Corporate
Semantic Web architecture based on the three pillars Corporate Ontology
Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search,
as envisioned in TR-B-08-09
Semantic Systems. In the Era of Knowledge Graphs
This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic
The consistent representation of scientific knowledge : investigations into the ontology of karyotypes and mitochondria
PhD ThesisOntologies are widely used in life sciences to model scienti c knowledge. The engineering
of these ontologies is well-studied and there are a variety of methodologies
and techniques, some of which have been re-purposed from software engineering
methodologies and techniques. However, due to the complex nature of bio-ontologies,
they are not resistant to errors and mistakes. This is especially true for more expressive
and/or larger ontologies.
In order to improve on this issue, we explore a variety of software engineering techniques
that were re-purposed in order to aid ontology engineering. This exploration
is driven by the construction of two light-weight ontologies, The Mitochondrial Disease
Ontology and The Karyotype Ontology. These ontologies have speci c and
useful computational goals, as well as providing exemplars for our methodology.
This thesis discusses the modelling decisions undertaken as well as the overall success
of each ontological model. Due to the added knowledge capture steps required
for the mitochondrial knowledge, The Karyotype Ontology is further developed than
The Mitochondrial Disease Ontology.
Speci cally, this thesis explores the use of a pattern-driven and programmatic approach
to bio-medical ontology engineering. During the engineering of our biomedical
ontologies, we found many of the components of each model were similar
in logical and textual de nitions. This was especially true for The Karyotype Ontology.
In software engineering a common technique to avoid replication is to abstract
through the use of patterns. Therefore we utilised localised patterns to model
these highly repetitive models. There are a variety of possible tools for the encoding
of these patterns, but we found ontology development using Graphical User
Interface (GUI) tools to be time-consuming due to the necessity of manual GUI
interaction when the ontology needed updating. With the development of Tawny-
OWL, a programmatic tool for ontology construction, we are able to overcome this
issue, with the added bene t of using a single syntax to express both simple and
- i -
patternised parts of the ontology.
Lastly, we brie
y discuss how other methodologies and tools from software engineering,
namely unit tests, di ng, version control and Continuous Integration (CI) were
re-purposed and how they aided the engineering of our two domain ontologies.
Together, this knowledge increases our understanding in ontology engineering techniques.
By re-purposing software engineering methodologies, we have aided construction,
quality and maintainability of two novel ontologies, and have demonstrated
their applicability more generally
Supporting requirement elicitation and ontology testing in knowledge graph engineering
Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising