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transformation of the proposed search plan to individual queries towards Internet search engines. 5)
Information Retrieval level: Here the system reaches the pre-selected search engines (from third level), and
reports their results back to the user (second level) in HTML form.
Implementation Decisions
The implementation of MENTOR system can be separated in two stages: a) Implementation and development
of the interactive component (levels 1,2,4 and 5), in HTML3 and CGI (Common Gateway Interface) scripts. b)
Implementation of the Advisor component (level 3), in Common LISP.
MENTOR' s flowchart is as follows: STEP 1: User accesses MENTOR via WWW and inserts his inputs to the
system (levels 1,2). STEP 2: User inputs are transferred to the Advisor Component so that the search strategy
is determined, (level 3). STEP 3: The results of MENTOR' s Advisor Component are reported back to the user,
(level 2). At this point, the user can modify the suggested search plan and proceed either to next step
(information retrieval, levels 4,5) or to access again the Advisor Component (level 3), pursuing a new search
plan. STEP 4: MENTOR reaches the selected search Internet engines (level 4) and reports their results (level
5) back to the user (level 2). The most important step, is step 2 where MENTOR' s Advisor Component is
reached. Due to the complexity of this component a detailed description of its functionality is given in section.
Mentor's Advisor Component analysis
The Advisor Component of MENTOR system comprises of: 1. An automated Knowledge Acquisition
component: This will be responsible for the knowledge elicitation from a domain expert and for the
transformation of the acquired knowledge to a Knowledge Base System (KBS) as a side-effect of a manmachine
dialogue. The stage of knowledge elicitation requires three different kinds of domain experts in order
three different kinds of KBS to be constructed. 2. Three different Knowledge Base Systems (LKBS, IKBS,
DKBS): The first type of knowledge base system, named LKBS (Librarian Knowledge Base System), is going
to be constructed based on the acquired knowledge from a Librarian expert (a person specialised in subject or
word-related search). This KBS will include the top level rules, tricks and tips that the expert usually follows,
in order to locate the information that he is interested in. Similarly, the second KBS, named IKBS (Internet
Knowledge Base System), will be based on the acquired knowledge from an Internet expert (a person
specialised in Internet information location) and will include again the top level rules, tricks and tips that the
expert usually follows in order to retrieve a specific information from Internet repositories. Both LKBS and
IKBS will include rules and knowledge which will be domain independent and furthermore can be used and
reused independently of the user defined search term. The third kind of knowledge base, named DKBS
(Domain Knowledge Base System), will include information provided by the domain expert, in the sense of
related concepts or synonyms to the user defined search term, in order the potentials of the users search to be
enhanced. All the information stored in DKBS will be organised in distinct domain dependent sub-knowledge
bases. Every time a new knowledge elicitation happens, the acquired domain specific knowledge will be
added to the DKBS. Consequently the information stored in DKBS is not static but on the contrary is increased
gradually each time a new knowledge elicitation happens. 3. A Knowledge Integrity tool: The Knowledge
Integrity tool will be able to detect inconsistencies in the domain supplied by each domain expert and
report them back to them so that they can be removed. 4. A forward chaining Inference Engine: This
Inference Engine is going to be initialised using the user's inputs. Therefore based on these, and in
combination with the information stored in the three pre-constructed knowledge bases LKBS, IKBS and
DKBS, it will apply forward chaining to find all the rules and the related concepts that contribute in order a
search plan to be reached.
Further Work
In the past several years, the number and variety of resources available on the Internet have increased
dramatically. With this increase, many new systems have been developed that allow users to search for and
access these resources. This paper outlines the overall description of a propose-and-revise system which can
be used as an intelligent agent in the construction of search strategies for information retrieval in a specific
domain applied on Internet information repositories. Further research topics include:- Method of knowledge