3.2 The Role of Ontologies in DLs
Before we describe the semantic negotiation strategy
between two heterogeneous agents, it is necessary
for us to re-visit the role of ontologies in DLs.
According to aforementioned discussion, we believe
that in the development of future digital libraries, the
deployment of careful generated ontologies or
thesauri will offer higher reliability and quality for
the DL services. Furthermore, based on the adoption
of ontologies, it will also help make mapping among
related schema or integrate various schema into a
repository to support the content-based retrieval. In
fact, DL researchers have implicitly applied the idea
of ontologies in DLs, for example, the process of
classification on digital records. But there is still a
long way to go to realize the ontology-based
harvesting, searching and browsing, etc in DLs.
As concerning Ontology itself alone, James Hendler
states that the Semantic Web will contain a great
number of small possibly mutually inconsistent
ontological components that consist largely of
pointers to each other instead of few large and
consistent ontologies (James, 2001). Currently, the
most promising approach for the comparably ‘large’
standard ontologies is the effort to clean-up, refine,
validate and merge the existing resources, e.g.
WordNet (http://www.cogsci.princeton.edu/wn),
HowNet(http://www.keenage.com/zhiwang/ezhiwan
g.html),CoreLex(http://www.cs.brandies.edu/~paulb
/CoreLex/overview.html), the publicly accessible
part of Cyc (http://www.cyc.com/), etc., for the
practical application, like ontology/metadata
mapping in DLs. There is available program for
helping validating designed ontologies (Nicola,
2002).
According to the well-know ‘5 papers on Wordnet’
(Miller, 1990), the essential part of concepts are:
z Synonymy(similar concept): <creator,
maker>
z Hyponymy(narrower-broader/ISA):
<designer is a creator>, <creator is person>
z Meronymy(part-of/HASA): <creator has
personality>
z Derivationally related terms/concepts:
<creator RELATEDTO create(verb)>
A number of papers in the DL and IR communities
have described the considerable improvement
obtained by adopting synonymy and hyponymy. For
example, in the application of query expansion. This
paper is yet not another endeavour to propose new
approaches for performance improvement. Rather, it
concentrates on how we can incorporate them into
distributed DLs and alleviate the problems brought
by schema heterogeneity. The following section will
concentrate on the semantic negotiation strategy.
3.3 Semantic Negotiation Strategy
Semantic Negotiation is a general purpose
mechanism that can be used in many different
contexts for exchanging schemas information and
description. In the procedure of negotiation, the
agent on the Service Provider (SP, the same meaning
as that in OAI-PMH) is expected to
interpret/understand the schema formats on the
heterogeneous Data Provider (DP, also from OAI-
PMH). The process is as follows:
1). When agent
sp(i)
asks agent
dp(j)
for the schema
format information, agent
dp(j)
sends agent
sp(i)
a list of
terms, using the description based on a lexical base,
for example, Wordnet. And the latter should also
support such a kind of lexical base. The reason for
doing so is that it is almost impossible for two
agents to mutually comprehend and exchange data
without any shared vocabulary or thesauri.
2). if agent
sp(i)
does not understand the description, it
responds with an error code indicating that the
description can not be understood. In this case, it
lists the particular terms not understood. Based on
this feedback, agent
dp(j)
can try to provide a
description that the server is more likely to
understand.
3). if the agent
sp(i)
partially understands the
description, that is, there are some mismatching
terms, it returns an error code saying so. It can
optionally also tell the agent
dp(j)
which part of the
description was not satisfied by any of the terms.
4). if the agent
sp(i)
understands the description, it
returns the confirmation to agent
dp(j)
. In the case
where the answer is a list of resources, the answer
may include additional data about each resource,
which the agent
sp(i)
may cache, in anticipation of
future queries about these resources.
The sequence diagram is illustrated in Figure 2.
Let us take a simple example, if agent
sp(1)
on Library
A queries agent
dp(2)
on Library B for the metadata
schema, agent
dp(2)
then responds his metadata format
in which there is one term – ‘author’ that agent
sp(1)
does not understand. Thus agent
sp(1)
sends a
feedback to agent
dp(2)
, claiming that unknown term.
Based on the feedback, agent
dp(2)
provides a
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