KNOWLEDGE REPRESENTATION MEETS DATABASES

In this last section of Chapter 4, we examine the issue of combining requirements of knowledge bases and databases. This discussion takes a logic- based perspective. A continued discussion on combining knowledge bases and databases will be continued in the next chapter (as well as in the remaining part of this book), where more pragmatic concerns will be addressed. The focus of our discussion is on intelligent access to heterogeneous information sources. According to [Baader, Jeusfeld and Nutt 1997, Borgida, Chaudhri and Staudt 1998], researchers seeking logic-based approaches have studied using Datalog (or another languague called DL) to achieve integration between computational intelligence and DBMS. Although their approaches are logic-based, the discussion may shed significant insight on the nature of this kind of integration. Both databases and knowledge bases are used to represent the relevant parts of an application domain, and to allow convenient access to the stored information. Research in KR originally concentrated on expressive formalisms with sophisticated reasoning services, usually under the assumption that the size of the knowledge base (KB) is relatively small and resides in main memory. In contrast, DB research was concerned with efficiently storing, retrieving, and sharing large amounts of simple data (usually in secondary memory), but the languages for describing schema information were rather simple, and reasoning about the schema played only a minor role. This difference reminds us the importance of dealing with scaling up problems (which was briefly discussed in Chapter 2). However, the distinction between the requirements and problems in KB and DB are vanishing rapidly. This is because a modern KR system must be able to handle large data values if it is to be employed in realistic applications. This means that techniques developed in the DB area can and should be incorporated. The boundary between KB and DB is diminishing, also because the information stored in DBs is becoming more complex and comes from heterogeneous sources, thus requiring more intelligent construction and retrieval techniques, especially the use of meta-data, which is really knowledge about data. (For a discussion on meta-data, see Chapter 14.) In principle, as long as descriptions of database schemas are expressed as formal concept definitions in a suitable description logic, a computational intelligence tool can reason about them to

detect inconsistent descriptions and containment of these schemas. Note that the reasoning is done independently of the specific content of a database. Research work has been carried out to implement KR systems on the top of relational databases or the access to a database through a KR system. Techniques to be introduced in Chapter 5 and Chapter 6 will make important contribution to this integration.

SUMMARY

In this chapter we have extended our discussion of predicates to relations. We discussed relational algebra and relational calculus. Logically, relational databases are just predicates; however, the practical issues considered in DBMS make them deserve special treatment.

We have also discussed relational database design. The basic normal forms (based on functional dependencies and multivalued dependencies) discussed in this chapter can be summarized as follows:

4NF ⊂ BCNF ⊂ 3NF ⊂ 2NF ⊂ 1NF

Decomposition algorithms to these normal forms were also introduced. More details on the relational databases, as well as many other basic issues related to database management systems can be found in [Silberschatz, Korth and Sudarshan 1987, Ramakrishnan 1998].

Note that the relational database design theory has a very close relationship with logic-based reasoning. A volume consisting of historically important discussion on deductive databases can be found in [Minker 1987]. The integration between Prolog and DBMS, which was an enthusiastic topic in the 1980’s, has been considered as dead. However, this does not indicate that integration itself is a bad idea. The key point here is how to integrate them. Datalog is a useful language for this kind of integration. In this chapter we briefly introduced magic set method for efficient deductive query processing. Other methods also exist. For example, [Lee and Leung 1993] introduced a query-processing method using V graph and SARP techniques. It is based on the analysis of a recursive rule's structure that cuts through the complexity often associated with queries in deductive databases.

Another important development in integrated database design is concerned with combining deductive databases techniques with object-oriented databases, sometimes under the title of deductive object-oriented databases (DOOD). Some basic issues related to deductive and object-oriented bases are discussed in [Gardarin and Valduriez 1989]. A collection of recent papers along with this research direction can be found in [Bry, Ramakrishnan and Ramamohanarao 1997]. A recent survey on deductive database languages (including different Datalog extensions LDL, COL, Hilog and Relationlog) as well object-oriented deductive languages (including O-logic, F-logic, ROL and IQL), can be found in [Liu 1999].

SELF-EXAMINATION QUESTIONS

1 . Explain the meaning of safety in relational calculus. Why don't we discuss safety in RA?

2. What is the meaning of the following query in RA? How to re-write it using RC (you don't have to worry about the exact syntax).

σA1= 'a'(r) ×πA2(s)

3. Verify FDs as a special case of MVDs. Consider R(ABC), with F: {A → B}. Use two tuples (a1, b1, c1) and (a1, b1, c2) and restate in terms of MVD.

4 . Consider Table 4.11. Does the following MVD hold in R(ABCDE): C→→BE? If not, add the smallest number of tuples to make the MVD hold.

Table 4.11 Another example of MVD

A B C D E a1 a2 a1 a2 a3 a4 a4 a3 b1 b2 b2 b1 b3 b4 b3 b4 c1 c1 c1 c1 c2 c2 c2 c2 d1 d2 d1 d2 d3 d4 d4 d3 e1 e1 e1 e1 e2 e3 e2 e3

REFERENCES

Baader, F., Jeusfeld, M. A. and Nutt, W., Intelligent access to heterogeneous information sources: Report on the 4th workshop on knowledge representation meets databases, SIGMOD Record, 26(4), 44-48, 1997.

Borgida, A., Chaudhri, V. K., and Staudt, M., Report on the 5th workshop on knowledge representation meets databases (KRDB'98), SIGMOD Record, 27(3), 10-15, 1998.

Bry, F., Ramakrishnan, R. and Ramamohanarao, K. (eds.), Proceedings of 1997 Deductive and Object-Oriented Databases Conference (DOOD '97), Springer, Berlin, 1997.

Elmasri R. and Navathe, S. B., Fundamentals of Database Systems (2nd ed.), Benjamin Cummings, Redwood City, CA, 1994.

Gardarin G., and Valduriez, P., Relational Databases and Knowledge Bases, Addison Wesley, Reading, MA, 1989.

Lee, D.L. and Leung, Y. Y., Fast Query processing in Deductive Databases. IEEE software, 10(6), 66-74, 1993.

Liu M., Deductive database languages: Problems and solutions, ACM Computing Surveys, 31(1), 27-62, 1999.

Minker, J. (ed.), Foundations of Deductive Databases and Logic Programming, Morgan Kaufmann, Los Altos, CA, 1987.

Ramakrishnan, R., Database Management Systems, McGraw-Hill, Boston, 1998.

Reiter, R. Toward a logical reconstruction of relational database theory. In On Conceptual Modelling (Brodie, M. L., Mylopoulos, J., and Schmit, J. W., eds.), Springer-Verlag, New York, 191-238, 1984.

Silberschatz, A., Korth, H. and Sudarshan, S.,, Database System Concepts (3rd ed.), McGraw-Hill, New York, 1997.

Staudt, M. and Jarke, M., Incremental maintenance of externally materialized views, Proceedings of Very Large Data Bases .(VLDB'96), pp. 75-86, 1996.

Ullman, J. D., Principles of Database and Knowledge Based Systems, (Volumes I and II), Computer Science Press, Rockville, MD, 1989.

Chapter 5