Semantic Annotation for Process Models: - Department of Computer ...

18 CHAPTER 2. PROBLEM SETTING
The concepts in domain ontologies and task ontologies are specialized from the
ones in the top-level ontology. Application ontologies are often specializations of both
domain ontologies and task ontologies. Such classification can be reflected into the
four layer meta-data architecture mentioned previously, i.e. top-level ontologies are at
M2 and domain and task ontologies are at M1 and application ontologies are at M0.
Domain, task and application ontologies about a certain domain usually construct the
general context of the systems in that domain.
Three main uses of ontology are identified in [45]: 1) For communication between
implemented computational systems, between humans, between humans and implemented
computational systems; 2) For computational inference, e.g. for internally
representing and manipulating plans and planning information, and for analyzing the
internal structures, algorithms, inputs and outputs of implemented systems in theoretical
and conceptual terms; 3) For reuse (and organization) of knowledge e.g. structuring
or organizing libraries or repositories of plans and planning and domain information.
The beneficial applications of ontology could locate in the following areas [178]:
• Semantic Web. The Semantic Web relies heavily on formal ontologies that
structure underlying data for the purpose of comprehensive and transportable
machine understanding. Ontologies are used to define the proper meaning of
data and metadata for the Semantic Web [173].
• Knowledge Management. The technology of the Semantic Web brings out
the knowledge pieces oriented view of knowledge management. Ontologies enable
intelligent push service, the integration of knowledge management and business
process to support the vision of ubiquitous knowledge. For the applications of
knowledge management, ontologies are employed to annotate unstructured information
with semantic information, to integrate information and to generate user
specific views that make knowledge access easier [180] [25].
• Interoperability. Interoperability is an important issue in the applications of
integration and reusing existing systems. As inter-lingua, ontology provides a
common and machine-interpretable format for data interchange [177] [188].
• Information Retrieval. Conventional information retrieval approaches suffer
from problems of the inconsistency between the query and the vocabulary of the
documents, which reduces recall of search. Ontologies help to decouple description
and query vocabulary and increase retrieval performance [46].
• Service Retrieval As the development of the research activities and applications
of Web services, locating online services is increasingly critical in many
domains. Online services include software applications, software components,
process models, or service organizations. The approaches of using ontologies in
querying those services are exploited and evaluated to improve the retrieval precision
[10] [182] [131].
Those areas are usually overlapping in an application. For example, knowledge
management is conducted under a Semantic Web environment; information retrieval or
services retrieval may be one of required services in knowledge management; the proper
knowledge management facilitates interoperability.