Logic and Databases: A History of Deductive Databases

Minker, Jack; Seipel, Dietmar; Zaniolo, Carlo

doi:10.1016/b978-0-444-51624-4.50013-7

Cited by 12 publications

(16 citation statements)

References 104 publications

(66 reference statements)

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“…Declarative programming offers, e.g., the following advantages for data and knowledge engineering: security, safety, and shorter development time, as known from information systems with relational databases. There exists a plethora of results about query optimisation in relational and deductive databases, e.g., [12][13][14][15][16] . For instance, Minker and his students have interesting results in the field of semantic query optimisation [17] : evaluation plans can be derived and cached results can be included.…”

Section: Declarative Programmingmentioning

confidence: 99%

“…Typically DDBs will operate on data which are more restrictive than that of Prolog , yet more general than that which may structurally be accessed with Sql . Deductive database languages have been used in many applications, such as data integration, computer networking, program analysis or security [16,20] . A comprehensive description of the semantics of deductive databases and logic programming is given, e.g., in [16] .…”

Section: Rules In Deductive Databases and Logic Programmingmentioning

confidence: 99%

“…Deductive database languages have been used in many applications, such as data integration, computer networking, program analysis or security [16,20] . A comprehensive description of the semantics of deductive databases and logic programming is given, e.g., in [16] . For this paper, it is sufficient to have an intuitive understanding of the meaning of if-then rules.…”

Section: Rules In Deductive Databases and Logic Programmingmentioning

confidence: 99%

“…The concept of top-down evaluation is not relevant for our approach and thus not described here. A comprehensive description of the syntax and semantics of deductive databases and logic programming is given, e.g., in [16] . In the appendix, we give a brief summary of the formal semantics.…”

Section: Forward-chaining Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

Seipel

Nogatz

Abreu

2018

Computer Languages, Systems & Structures

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Declarative if-then rules have proven very useful in many applications of expert systems. They can be managed in deductive databases and evaluated using the well-known forward-chaining approach. For domain-experts, however, the syntax of rules becomes complicated quickly, and already many different knowledge representation formalisms exist. Expert knowledge is often acquired in story form using interviews. In this paper, we discuss its representation by defining domain-specific languages (Dsl s) for declarative expert rules. They can be embedded in Prolog systems in internal Dsl s using term expansion and as external Dsl s using definite clause grammars and quasi-quotations-for more sophisticated syntaxes. Based on the declarative rules and the integration with the Prolog-based deductive database system DDbase , multiple rules acquired in practical case studies can be combined, compared, graphically analysed by domain-experts, and evaluated, resulting in an extensible system for expert knowledge. As a result, the actual modeling Dsl becomes executable; the declarative forward-chaining evaluation of deductive databases can be understood by the domain experts. Our Dsl for rules can be further improved by integrating ontologies and rule annotations.

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Section: Declarative Programmingmentioning

confidence: 99%

Section: Rules In Deductive Databases and Logic Programmingmentioning

confidence: 99%

Section: Rules In Deductive Databases and Logic Programmingmentioning

confidence: 99%

Section: Forward-chaining Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

Seipel

Nogatz

Abreu

2018

Computer Languages, Systems & Structures

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“…A strong research interest in deductive database technology and its applications has reemerged in the recent years, leading to what has been called a resurgence [6] or even a renaissance [1] for DATALOG. This revival is propelled by important new applications areas, such as distributed computations and big-data applications, the success of a first commercial DATALOG System, and progress in semantics extensions to support non-monotonic constructs such as default negation and aggregates -a theoretical thread that had actually continued through the years [10].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Engineering for Hybrid Deductive Databases

Seipel

2017

Electron. Proc. Theor. Comput. Sci.

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Modern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these different formats of knowledge and reason about them. Even the file systems on different computers can be part of the knowledge base. Often, it is necessary to handle different versions of a knowledge base. E.g., we might want to find out common parts or differences of two versions of a relational database. We will examine the use of abstractions of rule bases by predicate dependency and rule predicate graphs. Also the proof trees of derived atoms can help to compare different versions of a rule base. Moreover, it might be possible to have derivations joining rules with other formalisms of knowledge representation. Ontologies have shown their benefits in many applications of intelligent systems, and there have been many proposals for rule languages compatible with the semantic web stack, e.g., SWRL, the semantic web rule language. Recently, ontologies are used in hybrid systems for specifying the provenance of the different components.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014

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Datalog and Logic Databases

Greco

Molinaro

2016

Synthesis Lectures on Data Management

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Logic and Databases: A History of Deductive Databases

Cited by 12 publications

References 104 publications

Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies

Knowledge Engineering for Hybrid Deductive Databases

Datalog and Logic Databases

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