Semantic Web-Based Information Systems: State-of-the-Art ...

0 Naeve
• Metadata is not always objective; it must also allow subjective expressions.
• Metadata is not produced once and for all; it is the result of an ongoing annotation
process that ideally should produce an ecosystem of annotations that
highlight resources with high quality.
• Metadata is not a collection of documents but a global network of information.
• Metadata is not only for machines; we need conceptual metadata for people.
An important feature of the HSW is the ability to collect metadata from various
sources into suitable contexts. These contexts can then be presented in various
graphical user interfaces, such as adorned forms, through diagrammatic languages
such as UML and so forth. Such contexts are also usable for the human management
of evolving metadata (Nilsson, Palmér, & Naeve, 2002).
The different contexts of the HSW are described by context maps that are constructed
by conceptual modeling and connected through the KM architecture. This provides
a conceptual information atlas of connected context maps with human-understandable
semantics for both abstract ideas and concrete resources. As discussed in a
subsequent section, for the conceptual modeling, we make use of the ULM (unified
language modeling) technique, 11 which is tailored to support the visualization of how
we speak about things. UML (n.d.) provides a well proven, standardized modeling
vocabulary with clearly defined visual semantics of the relationships between the
occurring concepts.
Combining the human semantics of UML with the machine semantics of RDF (n.d.)
enables more efficient and user-friendly forms of human-computer interaction. The
HSW supports the mixture of human and machine semantics that is needed for efficient
construction and use of modular and personalized learning and knowledge
management environments based on retrieval and reuse of relevant knowledge
resources.
In general, the HSW is designed to support the ongoing paradigm shift of social
interaction patterns away from knowledge push and towards knowledge pull, such
as the following shifts:
• from teacher-centric to learner-centric education;
• from doctor-centric to patient-centric health care;
• from bureaucrat-centric to citizen-centric administration;
• from government-centric to citizen-centric democracy; and
• from producer-centric to consumer-centric business models.
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