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Integrating Information and Knowledge with Software Agents UDC 519.681:681.3.068 Integrating Information and Knowledge with Software Agents VHironobu Kitajima VRyusuke Masuoka VFumihiro Maruyama (Manuscript received June 9, 2000) This paper describes research and development activities for integrating distributed information and knowledge with software agent technology. First, we give an over- view of our software agent architecture. Then, we describe the application of our software agent technology in a system that integrates distributed databases and in a document-oriented information integration system which integrates search engines. Our agent-based approach is suitable for dynamic information sources. 1. Introduction for users to retrieve information and knowledge The advent of the Internet has brought about simultaneously from distributed sources and to what is called an “information flood.” There is an make consecutive retrievals of associated infor- enormous amount of information and knowledge mation and knowledge. digitally available through networks. Although Distributed and disparate information sourc- such information and knowledge can be accessed es need to be integrated. This is where software on an individual basis, it is not an easy task for agent technology comes into play. In this paper, a users to acquire appropriate information and “software agent” is a computer system that is sit- knowledge. uated in an environment and is capable of The sources of information and knowledge autonomous action in this environment to meet have platform-level, system-level, and represen- its design objectives.1) A system of software agents tation-level differences. Platform-level differences hides platform-level, system-level, and represen- include the differences between hardware and tation-level differences and realizes a virtual operating systems, for example, differences be- integration. The areas where virtual integration tween Solaris, Windows NT, and mainframe can be applied include Supply Chain Management computers. System-level differences include the (SCM), Enterprise Application Integration (EAI), differences between database management sys- Enterprise Information Portals (EIPs), and Knowl- tems such as Oracle, SQL Server, DB2 and edge Management (KM). proprietary applications on mainframe comput- This paper describes our activities in this ers. Representation-level differences come from area under the Smart AGent Environment (SAGE) the different database structures and different project at Fujitsu Laboratories. Chapter 2 gives vocabularies used in databases, which include dif- a general overview of our software agent archi- ferent field names for corresponding fields and tecture and introduces its application to the different field values with the same meaning. integration of distributed databases for SCM. These three types of differences make it difficult Chapter 3 describes a system for document- 162 FUJITSU Sci. Tech. J.,36,2,pp.162-174(December 2000) H. Kitajima et al.: Integrating Information and Knowledge with Software Agents oriented information integration which has been realize virtual catalogs. SAGE: Anthony, details in practical use by Fujitsu’s systems engineers of which are given in Chapter 3, is an application since October 1998. Chapter 4 describes the stan- of the agent system to a virtual integration of doc- dardization efforts and future directions of our ument search engines. In both applications, the project. Chapter 5 concludes the paper. agent system enables the system integrators to give end users a virtually integrated view of dis- 2. Integrating distributed databases tributed and disparate information sources In this chapter, we first introduce the Smart without changing the way in which information AGent Environment (SAGE) project and its intel- sources are operated. ligent agent systems, which enable users to This chapter mainly describes SAGE: Fran- virtually integrate distributed information sourc- cis. (SAGE: Francis lead to the development of an es. In the rest of this chapter, we mainly discuss agent system software product called AGENT- SAGE: Francis, which is an Electronic Commerce PRO.4),5) Fujitsu released AGENTPRO in August (EC) application of the SAGE project in which dis- 1999.) tributed and disparate Relational Databases (RDBs) are virtually integrated. Section 2.2 de- 2.2 Architecture scribes the architecture of SAGE: Francis, Section Figure 1 shows the architecture of SAGE: 2.3 describes its main features, and Section 2.4 Francis. It consists of user agents (UAs), facilita- describes our mediator agents called “facilitators.” tors (FAs), and database agents (DBAs).note 1) All Then, in Section 2.5, we explain how SAGE: Francis agents communicate with each other via Agent works by describing an example application in a Communication Language (ACL) messages, the Supply Chain Management (SCM) prototype sys- syntax of which is defined by the Knowledge Que- tem. Finally, in Section 2.6, we discuss ry and Manipulation Language (KQML)6) and centralized-type and decentralized-type solutions Knowledge Interchange Format (KIF).7), note 2) for the integration of distributed information sources. Browser Browser Browser Browser 2.1 SAGE HTML, etc. We are conducting research on intelligent User User User User agent agent agent agent agent systems under the SAGE project. Under Facilitator Facilitator this project, we have developed our agent system ACL and several agent system applications, which in- clude SAGE: Francis2),3) and SAGE: Anthony. In Database Database Database Database agent agent agent agent both SAGE: Francis and SAGE: Anthony, the agent SQL, etc. system is used for virtual integration of distrib- uted databases and other disparate information Database Database Database Database sources. SAGE: Francis is an application of our agent system to Electronic Commerce (EC) set- Figure 1 tings where RDBs are virtually integrated to Architecture of SAGE. note 1) There are also application agents (AAs) in AGENTPRO, which are created as wrappers for general appli- cations. AAs are very close to DBAs. Essentially, they are DBAs in which database information sources are replaced with general applications. note 2) KQML and KIF are often called DARPA ACL since they are outcomes of Knowledge Sharing Efforts (KSE), which is a DARPA project. Future versions of AGENTPRO will be FIPA-compliant.8) FUJITSU Sci. Tech. J.,36, 2,(December 2000) 163 H. Kitajima et al.: Integrating Information and Knowledge with Software Agents Every ACL message in SAGE is sent and re- and which ontology they use. There are also “un- ceived asynchronously. Agents can automatically advertise” messages, which enable DBAs to deny match ACL messages such as a query message partially or entirely the knowledge sent by previ- and its resultant message. This is important be- ous advertise messages.note 3) cause agents can send the messages when Based on such knowledge, FAs can route and appropriate and operate freely. translate ACL messages appropriately. Hence, the The two main functions of UAs are to pro- system configuration is changed according to the cess conversions between ACL messages and advertise messages from DBAs. These advertise expressions on user interfaces such as Web pages messages enable a decentralized method of sys- and to offer a support service for users. One of tem configuration, which allows DBAs to have the main functions of DBAs is to process conver- autonomy over their operations. This advertise- sions between ACL messages and SQL queries: ment mechanism insulates UAs, and therefore the SQL is the query language of database manage- users, from the locations and other details of in- ment systems (DBMSs) such as Oracle and formation sources. The transparency provided by Microsoft Access. Two other key functions include this mechanism is essential for virtual informa- converting DB data into virtual knowledge to be tion integration. used in ACL messages from DBAs and informing 3) Ontology and ontology translation FAs of the capabilities of DBAs (a function called SAGE is based on ontologies. An ontology is “advertising”). The main functions of FAs, which a set of definitions of terms and the relationships act as mediators between UAs and DBAs, are for- between those terms. An ontology can be roughly warding messages to suitable DBAs, merging and defined as a vocabulary used by communicating sorting (if specified) results from multiple DBAs, agents. replying accordingly if there are no suitable Since distributed and disparate information agents, and translating terms in ACL messages sources are developed independently, they usual- as necessary. ly use different sets of terms. Even though agents use the same syntax for ACL messages, for exam- 2.3 Main features ple, KQML and KIF, ontologies used in ACL As a complete agent system, SAGE: Francis messages can be different. Therefore, FAs pro- has the following main features: vide an ontology translation service. With this 1) Agentification of legacy information sources service, the agent system hides vocabulary-level SAGE: Francis provides DBAs to agentify leg- difference from users. This is another essential acy information sources such as databases. After element for virtual information integration. the agentification, these information sources are FAs also provide flexible routing of ACL mes- made into agents which communicate via ACL sages based on the messages’ ontology, which uses messages using CORBA Object Request Broker its own category information. This is one of the (ORB). This hides platform-level
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