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A Formal Dialogue Model for Ontology Authoring
Several research teams have proposed controlled fragments of English suitable for building ontologies for the Semantic Web. These Controlled Languages are typically employed in applications that allow users to compose texts through guided authoring; the sentences in the text are parsed and interpreted to obtain axioms in OWL. We investigate here a variant of this approach in which the user enters sentences one at a time through an instant messaging interface, and receives an immediate response in English from the authoring system before typing in the next sentence. Such an application can support a variety of dialogue moves by the user—not only assertions, but also retractions, questions, requests for explanation, and so forth—and for each, we need to define an appropriate set of responses for the authoring system. To illustrate the interest and complexity of this task, we develop a formal model for just one case: responses to an assertion
Goal Directed Conflict Resolution and Policy Refinement
Peer reviewedPostprin
Representing and coding the knowledge embedded in texts of Health Science Web published articles
Despite the fact that electronic publishing is a common activity to scholars electronic journals are still based in the print model and do not take full advantage of the facilities offered by the Semantic Web environment. This is a report of the results of a research project with the aim of investigating the possibilities of electronic publishing journal articles both as text for human reading and in machine readable format recording the new knowledge contained in the article. This knowledge is identified with the scientific methodology elements such as problem, methodology, hypothesis, results, and conclusions. A model integrating all those elements is proposed which makes explicit and records the knowledge embedded in the text of scientific articles as an ontology. Knowledge thus represented enables its processing by intelligent software agents The proposed model aims to take advantage of these facilities enabling semantic retrieval and validation of the knowledge contained in articles. To validate and enhance the model a set of electronic journal articles were analyzed
Populous: A tool for populating ontology templates
We present Populous, a tool for gathering content with which to populate an
ontology. Domain experts need to add content, that is often repetitive in its
form, but without having to tackle the underlying ontological representation.
Populous presents users with a table based form in which columns are
constrained to take values from particular ontologies; the user can select a
concept from an ontology via its meaningful label to give a value for a given
entity attribute. Populated tables are mapped to patterns that can then be used
to automatically generate the ontology's content. Populous's contribution is in
the knowledge gathering stage of ontology development. It separates knowledge
gathering from the conceptualisation and also separates the user from the
standard ontology authoring environments. As a result, Populous can allow
knowledge to be gathered in a straight-forward manner that can then be used to
do mass production of ontology content.Comment: in Adrian Paschke, Albert Burger begin_of_the_skype_highlighting
end_of_the_skype_highlighting, Andrea Splendiani, M. Scott Marshall, Paolo
Romano: Proceedings of the 3rd International Workshop on Semantic Web
Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10,
201
Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains
Biomedical taxonomies, thesauri and ontologies in the form of the
International Classification of Diseases (ICD) as a taxonomy or the National
Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in
acquiring, representing and processing information about human health. With
increasing adoption and relevance, biomedical ontologies have also
significantly increased in size. For example, the 11th revision of the ICD,
which is currently under active development by the WHO contains nearly 50,000
classes representing a vast variety of different diseases and causes of death.
This evolution in terms of size was accompanied by an evolution in the way
ontologies are engineered. Because no single individual has the expertise to
develop such large-scale ontologies, ontology-engineering projects have evolved
from small-scale efforts involving just a few domain experts to large-scale
projects that require effective collaboration between dozens or even hundreds
of experts, practitioners and other stakeholders. Understanding how these
stakeholders collaborate will enable us to improve editing environments that
support such collaborations. We uncover how large ontology-engineering
projects, such as the ICD in its 11th revision, unfold by analyzing usage logs
of five different biomedical ontology-engineering projects of varying sizes and
scopes using Markov chains. We discover intriguing interaction patterns (e.g.,
which properties users subsequently change) that suggest that large
collaborative ontology-engineering projects are governed by a few general
principles that determine and drive development. From our analysis, we identify
commonalities and differences between different projects that have implications
for project managers, ontology editors, developers and contributors working on
collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic
How to Evaluate Controlled Natural Languages
This paper presents a general framework how controlled natural languages can
be evaluated and compared on the basis of user experiments. The subjects are
asked to classify given statements (in the language to be tested) as either
true or false with respect to a certain situation that is shown in a graphical
notation called "ontographs". A first experiment has been conducted that
applies this framework to the language Attempto Controlled English (ACE)
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