F-logic

Last updated

F-logic (frame logic) is a knowledge representation and ontology language. F-logic combines the advantages of conceptual modeling with object-oriented, frame-based languages and offers a declarative, compact and simple syntax, as well as the well-defined semantics of a logic-based language.

Contents

Features include, among others, object identity, complex objects, inheritance, polymorphism, query methods, encapsulation. F-logic stands in the same relationship to object-oriented programming as classical relational calculus stands to relational database programming.

Overview

F-logic was developed by Michael Kifer at Stony Brook University and Georg Lausen at the University of Mannheim. F-logic was originally developed for deductive databases, but is now most frequently used for semantic technologies, especially the semantic web. F-logic is considered as one of the formalisms for ontologies, but description logic (DL) is more popular and accepted, as is the DL-based OWL.

A development environment for F-logic was developed in the NeOn project and is also used in a range of applications for information integration, question answering and semantic search. Prior to the version 4 of Protégé ontology editor, F-Logic is supported as one of the two kinds of ontology.

The frame syntax of the Rule Interchange Format Basic Logic Dialect (RIF BLD) standardized by the World Wide Web Consortium is based on F-logic; RIF BLD however does not include non-monotonic reasoning features of F-logic. [1]

In contrast to description logic based ontology formalism the semantics of F-logic are normally that of a closed world assumption as opposed to DL's open world assumption. Also, F-logic is generally undecidable,[ citation needed ] whereas the SHOIN description logic that OWL DL is based on is decidable. However it is possible to represent more expressive statements in F-logic than are possible with description logics.

The most comprehensive description of F-logic was published in 1995. [2] The preliminary paper [3] [4] from 1989 won the 1999 Test of Time Award from ACM SIGMOD. [5] A follow-up paper [6] from 1992 won the 2002 Test of Time Award from ACM SIGMOD. [5]

F-logic syntax

Classes and individuals may be defined in F-logic as follows:

man::person. woman::person. brad:man. angelina:woman.

This states, that "men and women are persons" and that "Brad is a man", and "Angelina is a woman".

Statements about classes and individuals may be made as follows:

person[hasSon=>man]. brad[hasSon->{maddox,pax}]. married(brad,angelina).

This defines that "the son of a person is a man", "Maddox and Pax are the sons of Brad" and "Brad and Angelina are married".

In addition it is possible to represent axioms in F-logic in the following manner:

man(X)<-person(X)ANDNOTwoman(X).X:person[hasFather->Y]<-Y:man[hasSon->X].

These mean "X is a man if X is a person but not a woman" and "if X is the son of Y then X is a person and Y is the father of X".

(Note: the latter inference is not factual, because in addition to fathers, sons also have mothers. The gender of X is determined by the premise, but the gender of Y is not.)

The Flora-2 system introduced a number of changes to the syntax of F-logic, making it more suitable for a knowledge representation and reasoning system as opposed to just a theoretical logic. In particular, variables became prefixed with a ?-mark, the distinction between functional and multi-valued properties was dropped and replaced by cardinality constraints, plus other important changes. For instance, the above pair of sentences look like this in Flora-2:

man(?X)<-person(?X)\and\nafwoman(?X).?X:person[hasFather->?Y]<-?Y:man[hasSon->?X].

Here \naf is default negation ("naf" stands for Negation as failure, based on the well-founded semantics); Flora-2 also supports so-called explicit negation, [7] which is closer to negation in classical logic.

F-logic based languages

Related Research Articles

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems, and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.

Logic programming is a programming, database and knowledge representation paradigm based on formal logic. A logic program is a set of sentences in logical form, representing knowledge about some problem domain. Computation is performed by applying logical reasoning to that knowledge, to solve problems in the domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses:

Prolog is a logic programming language that has its origins in artificial intelligence, automated theorem proving and computational linguistics.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors.

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

Datalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down evaluation model. This difference yields significantly different behavior and properties from Prolog. It is often used as a query language for deductive databases. Datalog has been applied to problems in data integration, networking, program analysis, and more.

The Semantic Web Rule Language (SWRL) is a proposed language for the Semantic Web that can be used to express rules as well as logic, combining OWL DL or OWL Lite with a subset of the Rule Markup Language.

<span class="mw-page-title-main">Ian Horrocks</span> British academic (b.1958)

Ian Robert Horrocks is a professor of computer science at the University of Oxford in the UK and a Fellow of Oriel College, Oxford. His research focuses on knowledge representation and reasoning, particularly ontology languages, description logic and optimised tableaux decision procedures.

The Rule Interchange Format (RIF) is a W3C Recommendation. RIF is part of the infrastructure for the semantic web, along with (principally) SPARQL, RDF and OWL. Although originally envisioned by many as a "rules layer" for the semantic web, in reality the design of RIF is based on the observation that there are many "rules languages" in existence, and what is needed is to exchange rules between them.

Allen's interval algebra is a calculus for temporal reasoning that was introduced by James F. Allen in 1983.

Transaction Logic is an extension of predicate logic that accounts in a clean and declarative way for the phenomenon of state changes in logic programs and databases. This extension adds connectives specifically designed for combining simple actions into complex transactions and for providing control over their execution. The logic has a natural model theory and a sound and complete proof theory. Transaction Logic has a Horn clause subset, which has a procedural as well as a declarative semantics. The important features of the logic include hypothetical and committed updates, dynamic constraints on transaction execution, non-determinism, and bulk updates. In this way, Transaction Logic is able to declaratively capture a number of non-logical phenomena, including procedural knowledge in artificial intelligence, active databases, and methods with side effects in object databases.

A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms to work with. The inference rules are commonly specified by means of an ontology language, and often a description logic language. Many reasoners use first-order predicate logic to perform reasoning; inference commonly proceeds by forward chaining and backward chaining. There are also examples of probabilistic reasoners, including non-axiomatic reasoning systems, and probabilistic logic networks.

WSML or Web Service Modeling Language is a formal language that provides a syntax and semantics for the Web Service Modeling Ontology (WSMO).

<span class="mw-page-title-main">Ontology engineering</span> Field that studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

In information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems.

HiLog is a programming logic with higher-order syntax, which allows arbitrary terms to appear in predicate and function positions. However, the model theory of HiLog is first-order. Although syntactically HiLog strictly extends first order logic, HiLog can be embedded into this logic.

Flora-2 is an open source semantic rule-based system for knowledge representation and reasoning. The language of the system is derived from F-logic, HiLog, and Transaction logic. Being based on F-logic and HiLog implies that object-oriented syntax and higher-order representation are the major features of the system. Flora-2 also supports a form of defeasible reasoning called Logic Programming with Defaults and Argumentation Theories (LPDA). Applications include intelligent agents, Semantic Web, knowledge-bases networking, ontology management, integration of information, security policy analysis, automated database normalization, and more.

Rulelog is an expressive semantic rule-based knowledge representation and reasoning (KRR) language. It underlies knowledge representation languages used in systems such as Flora-2, SILK and others. It extends well-founded declarative logic programs with features for higher-order syntax, frame syntax, defeasibility, general quantified expressions both in the bodies of the rules and their heads, user-defined functions, and restraint bounded rationality.

<span class="mw-page-title-main">Semantic Web Services Language</span>

The Semantic Web Services Language (SWSL) is a general-purpose logical language for specifying Semantic Web Services Ontologies (SWSOs), as well as individual Web services. The Semantic Web Services Language (SWSL) describes the syntax elements of SWSL and its semantic and semantic foundations. It can be used with the underlying language and network structure of Semantic Web Services. Syntactically, first-order logic is a subset of the Semantic Web Services Language (SWSL).

References

  1. M. Krötzsch (October 2010). Description Logic Rules. IOS Press. p. 10. ISBN   978-1-61499-342-1.
  2. M. Kifer, G. Lausen, J. Wu (1995). "Logical foundations of object-oriented and frame-based languages", Journal of the ACM 42(4), July 1995, 741–843. doi : 10.1145/210332.210335
  3. M. Kifer and G. Lausen (1989). "F-logic: a higher-order language for reasoning about objects, inheritance, and scheme", ACM SIGMOD Record 18(2), June 1989, 134–146. doi : 10.1145/66926.66939
  4. M. Kifer and G. Lausen (1997). "F-logic: a higher-order language for reasoning about objects, inheritance, and scheme", re-issued 1997. doi : 10.1145/66926.66939
  5. 1 2 "SIGMOD Test of Time Award". sigmod.org. Retrieved 2019-03-19.
  6. M. Kifer, W. Kim, Y. Sagiv (1992). "Querying object-oriented databases", ACM SIGMOD Record 21(2), June 1992, 393–402. doi : 10.1145/141484.130342
  7. Alferes, José Júlio; Pereira, Luís Moniz; Przymusinski, Teodor C. (April 1998). "'Classical' negation in nonmonotonic reasoning and logic programming". Journal of Automated Reasoning . 20 (1–2): 107–142. doi:10.1023/A:1005900924623. S2CID   8882426.