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1990-The Intelligent Database Interface: Integrating AI And From: AAAI-90 Proceedings. Copyright ©1990, AAAI (www.aaai.org). All rights reserved. The Intelligent Database Interface: Integrating AI and Database Systems Donald P. McKay and Timothy W. Finin and Anthony O’Hare* Unisys Center for Advanced Information Technology Paoli, Pennsylvania [email protected] and [email protected] Abstract its tuple-at-a-time inference mechanisms. The ID1 has also been used to implement a query server supporting The Intelligent Database Interface (IDI) is a cache-based interface that is designed to provide Artificial Intelligence a database used for an Air Trorvel Information System systems with efficient access to one or more databases on one which is accessed by a spoken language system imple- or more remote database management systems (DBMSs). mented in Prolog [Dahl, et. al., 19901. It can be used to interface with a wide variety of different In addition to providing efficient access to remote DBMSs with little or no modification since SQL is used to DBMSs, the ID1 offers several other distinct advan- communicate with remote DBMSs and the implementation tages. It can be used to interface with a wide vari- of the ID1 provides a high degree of portability. The query ety of different DBMSs with little or no modification language of the ID1 is a restricted subset of function-free since SQL is used to communicate with the remote Horn clauses which is translated into SQL. Results from the DBMS. Also, several connections to the same or differ- ID1 are returned one tuple at a time and the ID1 manages a cache of result relations to improve efficiency. The ID1 is ent DBMSs can exist simultaneously and can be kept one of the key components of the Intelligent System Server active across any number of queries because connec- (ISS) knowledge representation and reasoning system and is tions to remote DBMSs are abstract objects that are also being used to provide database services for the Unisys managed as resources by the IDI. Finally, accessing spoken language systems program. schema information is handled automatically by the IDI, i.e., the application is not required to maintain Introduction up-to-date schema information for the IDI. This signif- icantly reduces the potential for errors introduced by The Intelligent Database Interface (IDI) is a portable, stale schema information or by hand entered data. cache-based interface designed to provide artificial intel- The ID1 can be viewed as a stand-alone DBMS inter- ligence systems in general and expert systems in par- ticular with efficient access to one or more databases face which accepts queries in the form of IDIL clauses on one or more remote database management systems and returns the result relation as a set of tuples (i.e., (DBMS) which support SQL [Chamberlm, et. al., a list of Lisp atoms and/or strings). IDIL queries are 19761. The query language of the ID1 is the Intelligent translated into SQL and sent to the appropriate DBMS Database Interface Language (IDIL) [O’Hare, 19891 and for execution. The results from the DBMS are then is based on a restricted subset of function-free Horn transformed by the ID1 into tuples of Lisp objects. Al- clauses where the head of a clause represents the tar- though the IDI was not designed to be used directly get list (i.e., the form of the result relation) and the by a user, the following descriptions will be couched body is a conjunction of literals which denote database in terms of using the ID1 as a stand-alone system so relations or operations on the relations and/or their at- that we may avoid complicating our discussions with tributes (e.g., negation, aggregation, and arithmetic op- the details of an AI system such as the ISS. erations). The design of the ID1 was heavily influenced by pre- The ID1 is one of the key components of the In- vious research in the area of AI/DB integration [Kellog, telligent System Server (ES) [Finin, et. al., 19891 et. al., 1986, O’Hare, 1987, O’Hare and Travis, 1989, which is based on Protem [Fritzson and Finin, 19881 O’Hare and Sheth, 19891. One of the more significant and provides a combined logic-based and frame-based design criteria that this lead to is the support of non- knowledge representation system and supports forward- trivial queries in IDIL. That is, to allow for queries in- chaining, backward-chaining, and truth maintenance. volving more than just a single database relation. This The ID1 was designed to be compatible with the logic- capability allows the AI system to off-load computa- based knowledge representation scheme of the ISS and tions that are more efficiently processed by the DBMS instead of the AI system (e.g., join operations). In many *current address: IBM, Research Triangle Park, North cases, this also has the effect of reducing the size of data Carolina set that is returned by the DBMS. MCKAY ET AL. 677 the AI system is extended with DBMS capabilities to provide efficient access to, and management of, large amounts of stored data. In general, such systems do Ertmdhg Re Al system ldxse coupling not incorporate full DBMS technology. Rather, the emphasis is on the AI system and the DBMS capabil- ities are added in an ad hoc and limited manner, e.g., [Ceri, et. al., 19861 implements only the data access layer. Alternatively, a new generation knowledge-based system such as LDL [Chimenti, et. al., 19871 may be constructed. In either ease, this approach effectively Figure 1: Of the four alternative approaches to AI/D5 inte- involves “re-inventing” some or all of DBMS technol- gration, the Intelligent Database Interface is an example of ogy. While such systems typically provide sophisticated an enhanced AI/DB interface. tools and environments for the development of applica- tions such as expert systems, they can not readily make While the ID1 is to some small degree system depen- use of existing databases. Thus, the development of AI dent, it does offer a high degree of portability because applications which must access existing databases will it is implemented in Common Lisp, communicates with be exceedingly difficult if not impossible (e.g., when the remote DBMSs using SQL and standard UNIX pipes, database is routinely accessed and updated via more and represents IDIL queries and their results as Com- traditional kinds of applications). mon Lisp objects. Extending the DBMS System: This approach In the following sections we present a brief overview extends a DBMS to provide knowledge representation of the area of AI/DB integration which represents a and reasoning capabilities, e.g., POSTGRES [Stone- large part of the motivation for the IDI, a discussion breaker, et. al., 19871. Here, the DBMS capabilities are of some of the more significant features of the IDI, the the central concern and the AI capabilities are added in organization and major components of the IDI, and fi- an ad hoc manner. The knowledge representation and nally an example of how the ID1 is being used in two reasoning capabilities are generally quite limited and applications. - they lack the sophisticated tools and environments of most AI systems. Such systems do not directly sup- AI/DB Integration port the use of existing DBMSs nor can they directly support existing AI applications (e.g., expert systems) The integration of AI and DBMS technologies promises without substantial effort on the part of the applica- to play a significant role in shaping the future of com- tion developer. In some sense, this is the opposite of puting. As noted in [Brodie, 19881, AI/DB integration the previous approach. is crucial not only for next generation computing but also for the continued development of DBMS technology Loose Coupling: The loose coupling approach to and for the effective application of much of AI technol- AI/DB integration uses a simple interface between the ogy* two types of systems to provide the AI system with ac- While both DBMS and AI systems, particularly ex- cess to existing databases, e.g., KEE-connection [Abar- pert systems, represent well established technologies, bane1 and Williams, 19861. While this approach has research and development in the area of AI/DB inte- the distinct advantage of integrating existing AI sys- gration is comparatively new. The motivations driv- tems and existing DBMSs, the relatively low level of ing the integration of these two technologies include integration results in poor performance and limited use the need for (a) access to large amounts of shared of the DBMS by the AI system. In addition, access data for knowledge processing, (b) efficient manage- to data from the database, as well as the data itself, ment of data as well as knowledge, and (c) intelli- is poorly integrated into the representational scheme of gent processing of data. In addition to these moti- the AI system. The highly divergent methods repre- vations, the design of ID1 was also motivated by the senting data (e.g., relational data models vs. frames) is desire to preserve the substantial investment repre- generally left to the application developer or knowledge sented by most existing databases. To that end, a engineer with only minimal support from the AI/DB key design criterion for ID1 was that it support the interface. use of existing DBMSs as independent system compo- Enhanced AI/DB Interface: The last approach nents. As illustrated in Figure 1 and described below, to AI/DB integration represents a substantial enhance- several general approaches to AI/DB integration have ment of the loosely coupled approach and provides a been investigated and reported in the literature (e.g., more powerful and efficient interface between the two [Bocca, 1986, Chakravarthy, et.
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