ABSTRACT
Internet and World Wide Web have experienced an enormous explosion recently. With the explosive growth in the availability of on line information it has become increasingly difficult for most of Internet users to locate and exploit the information avail able. The development of Intelligent Agents seems to be the most promising answer to confront this problem. Autonomous Agents allow a radically new approach which allows for easy and efficient information retrieval. This paper outlines the use of Intelligent Agents developed on World Wide Web towards user training and information retrieval tasks. Additionally describes a system called "SEARCH ADVISOR", which provides help towards Internet users as far as Internet b ased information retrieval is concerned, using expert searchers knowledge. As it is a propose-and-revise system, can be used as an Intelligent Agent in the construction of search strategies for information retrieval .
Keywords: Information retrieval, Internet Learning and Teaching, Artificial Intelligence in Education, Intelligent Agents, Internet, Search .
The rapid growth of data volume and diversity in Internet and World Wide Web has created significant problems related to the efficiency and accuracy of the information retrieval. Additionally, information in existing Internet repositories is heterogeneou s, inconsistent and sometimes incomplete [1]. This fact increases the difficulty of the above mentioned problem. To make effective use of this wealth of information, user needs means to locate information. In the past few years, a number of such resource discovery tools have been created such as:
The development of Agent software has brought a new approach to information retrieval. Broadly defined, an agent is a program that performs unique tasks without direct human supervision. Agents are programs that have some special skill and are able to en gage and help users in complex actions. As such, agents transforms the user from a worker into a manager who delegates tasks to that agent.[2] (Fig 1). An agent is the carrier of will, the entity that chooses between possible actions. Agents cannot be s eeing, but we can only see what they are performing.
Figure 1,
User Agent interaction.
SEARCH ADVISOR, the system we are going to describe, provides help towards Internet users as far as Internet based information retrieval is concerned, using expert searchers knowledge. By meeting the pre-mentioned criteria, SEARCH ADVISOR can be consider as an intelligent agent.
Even though there is a variety of search engines available on the Net , there is a lack of a mechanism that will be able to construct a global search strategy. SEARCH ADVISOR in the sense of an Intelligent Agent is a propose-and-revise system which automates the construction of a search strategy (in a specific domain) for Internet based information retrieval, in order to help Internet users to access and retr ieve information using a variety of Internet meta-search engines and information resources. SEARCH ADVISOR can help and train "novice searchers" towards a search task, by providing additional information regarding the decision tree that the system construct during the search session. Using and combing the meta-knowledge that has been acquired from expert searchers and user's defined search term, can be used as a trainer for novices searchers. Providing, justification reasons, regarding each step of the proposed search strategy, SEARCH ADVISOR enables them to identify the criteria that an exp ert searcher uses during a search task, and allow him/her to monitories expert' thoughts. The main goal of SEARCH ADVISOR is not only to accomplish accurately the retrieval session, but simultaneously to enrich and improve users search skills during this search session. SEARCH ADVISOR's system can be analysed in the following levels (Fig 2). 1) User interaction - Data Input-Output level: The system can be accessed by the user via front - end interface of the system. The user is allowed as first step, to insert a search term in a dialogue box and additionally to receive the results both o f the proposed search strategy and the Internet search.
2) SEARCH ADVISOR' s Advisor level: At this level the system implements and combines user inputs with the expert suggestions (using the pre-stored Librarian, Internet and Domain expert knowledge) in order to report the preferable search strategy, that is suggested to be followed, back to the user. A detailed description of this level is given in the following section.
Figure 2,
Overview of Search Advisor system.
4) Information Retrieval level: Here the system reaches Internet repositories, and reports the results back to the user (via the second level interface). For the implementation of the interactive components of SEARCH ADVISOR system (levels 1, 3 and 4), HTML3 and CGI (Common Gateway Interface) scripts are used and for the implementation of the Advisor Component (level 2), is used Common LISP.
SEARCH ADVISOR's flowchart is as follows (Fig 3) :
STEP 1: System is intialised using user input and the meta-knowledge stored in the three different KBS. User accesses SEARCH ADVISOR via WWW and defines the search term.
STEP 2: User inputs are transferred to the Advisor Component so that the search strategy is determined .
Figure 3,
Flowchart of a search session using Search Advisor.
The Advisor Component of SEARCH ADVISOR system comprises of:
a. 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 man-machine dialog
ue. The stage of knowledge elicitation requires three different kinds of domain experts in order three different kinds of KBS to be constructed.
b. 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, that an Librarian expert searcher usually follows, in order to locate the information that he is interested in. Additionally the meta-knowledge used by the expert for the refi
nement of a search task is also included in the LKBS.
The second knowledge base system, 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 t
ips 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.
Finally the third 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.
Further research topics include the investigation regarding the appropriate model of knowledge representation of the acquired knowledge and the concept piling. The isolation and identification of the meta-knowledge used by expert searchers during a sear ch session is another crucial point. The mismatches between user defined search term and subject headings is one of the most usual reasons that a search task fails. To improve the performance of an information retrieval session we have to eliminate those mismatches by developing a mechanism in order search terms defined by the user to be corresponded to the appropriate subject heading . Finally it is necessary SEARCH ADVISOR to be updated and compatible towards new search mechanisms that will rise o n the Net.
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Avgoustos. A. Tsinakos and Konstandinos. G. Margaritis
Department of Informatics
University of Macedonia
54006 Thessaloniki
GREECE
Tel : +30-31- 891 891
E-mail: tsinakos/kmarg@macedonia.uom.gr
©, 1997. The authors, Avgoustos A. Tsinakos and Konstandinos G. Margaritis assign to University of New Brunswick and other educational and non-profit institutions a non-exclusive license to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. The authors also grant a non-exclusive license to the University of New Brunswick to publish this document in full on the World Wide Web and on CD-ROM and in printed form with the conference papers, and for the document to be published on mirrors on the World Wide Web. Any other usage is prohibited without the express permission of the authors.