27,328 research outputs found
Perspectives in deductive databases
AbstractI discuss my experiences, some of the work that I have done, and related work that influenced me, concerning deductive databases, over the last 30 years. I divide this time period into three roughly equal parts: 1957–1968, 1969–1978, 1979–present. For the first I describe how my interest started in deductive databases in 1957, at a time when the field of databases did not even exist. I describe work in the beginning years, leading to the start of deductive databases about 1968 with the work of Cordell Green and Bertram Raphael. The second period saw a great deal of work in theorem providing as well as the introduction of logic programming. The existence and importance of deductive databases as a formal and viable discipline received its impetus at a workshop held in Toulouse, France, in 1977, which culminated in the book Logic and Data Bases. The relationship of deductive databases and logic programming was recognized at that time. During the third period we have seen formal theories of databases come about as an outgrowth of that work, and the recognition that artificial intelligence and deductive databases are closely related, at least through the so-called expert database systems. I expect that the relationships between techniques from formal logic, databases, logic programming, and artificial intelligence will continue to be explored and the field of deductive databases will become a more prominent area of computer science in coming years
How can graph databases and reasoning be combined and integrated?
Nowadays the graph data model has been accepted as one of the most suitable data models to formalize relationships among entities of many domains. Deductive databases based on the Datalog language have been used to deduce new information from large amounts of data. Most of the attempts to combine logic and graph databases are based on translating knowledge in graph databases into Datalog and then use its inference engine. We aim to open the discussion about combining graph databases and a graph-oriented logic to define «native» deductive graph databases. This is, graph databases equipped with an inference mechanism based on graph based logic. To be concrete, we plan to use the recently introduced graph navigational logic.Peer ReviewedPostprint (published version
General logical databases and programs: Default logic semantics and stratification
AbstractDefault logic is introduced as a well-suited formalism for defining the declarative semantics of deductive databases and logic programs. After presenting, in general, how to use default logic in order to define the meaning of logical databases and logic programs, the class of stratifiable databases and programs is extensively studied in this framework. Finally, the default logic approach to the declarative semantics of logical databases and programs is compared with the other major approaches. This comparison leads to showing some advantages of the default logic approach
Introducing Dynamic Behavior in Amalgamated Knowledge Bases
The problem of integrating knowledge from multiple and heterogeneous sources
is a fundamental issue in current information systems. In order to cope with
this problem, the concept of mediator has been introduced as a software
component providing intermediate services, linking data resources and
application programs, and making transparent the heterogeneity of the
underlying systems. In designing a mediator architecture, we believe that an
important aspect is the definition of a formal framework by which one is able
to model integration according to a declarative style. To this purpose, the use
of a logical approach seems very promising. Another important aspect is the
ability to model both static integration aspects, concerning query execution,
and dynamic ones, concerning data updates and their propagation among the
various data sources. Unfortunately, as far as we know, no formal proposals for
logically modeling mediator architectures both from a static and dynamic point
of view have already been developed. In this paper, we extend the framework for
amalgamated knowledge bases, presented by Subrahmanian, to deal with dynamic
aspects. The language we propose is based on the Active U-Datalog language, and
extends it with annotated logic and amalgamation concepts. We model the sources
of information and the mediator (also called supervisor) as Active U-Datalog
deductive databases, thus modeling queries, transactions, and active rules,
interpreted according to the PARK semantics. By using active rules, the system
can efficiently perform update propagation among different databases. The
result is a logical environment, integrating active and deductive rules, to
perform queries and update propagation in an heterogeneous mediated framework.Comment: Other Keywords: Deductive databases; Heterogeneous databases; Active
rules; Update
Inductive Logic Programming in Databases: from Datalog to DL+log
In this paper we address an issue that has been brought to the attention of
the database community with the advent of the Semantic Web, i.e. the issue of
how ontologies (and semantics conveyed by them) can help solving typical
database problems, through a better understanding of KR aspects related to
databases. In particular, we investigate this issue from the ILP perspective by
considering two database problems, (i) the definition of views and (ii) the
definition of constraints, for a database whose schema is represented also by
means of an ontology. Both can be reformulated as ILP problems and can benefit
from the expressive and deductive power of the KR framework DL+log. We
illustrate the application scenarios by means of examples. Keywords: Inductive
Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid
Knowledge Representation and Reasoning Systems. Note: To appear in Theory and
Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables
An intelligent tutoring system for program semantics
In this paper, we describe an item of e-learning software that is intended to help students taking university computer science courses to understand the fundamentals of logic programming and deductive database semantics. The software is implemented in PROLOG and empowers students
to explore their understanding of the semantics of logic programs and deductive databases. The software is also able to intelligently diagnose student misconceptions and includes a number of example programs/databases that permit students to test their understanding. We describe the development and evaluation of the software, and we present details of the analysis of the results of our investigation into the effectiveness of our e-learning tool. The results of our field study of the e-learning tool suggests that it of value in helpingstudents to understand program and database semantics
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