RuleML Inc. is a non-profit organization that drives the specification of standard semantic-technology & business rules, coordinates rule research and development, and holds international meetings.
The standards effort of RuleML Inc. connects Web rule efforts across academia, standards bodies, and industry, and dovetails with Web ontology efforts as part of the semantic technology stack. The XML syntax known as "RuleML" provides a de facto standard for Web knowledge representation that served as the main XML-based input to RIF and provides bridges between SWRL, SWSL, and LegalRuleML. The current Specification of RuleML is Version 1.0.
RuleML Inc. functions as the organizational lead of the RuleML Initiative. Foundational RuleML technology developed by the RuleML Initiative includes user and serialization syntaxes, transformations, model-theoretic semantics, and engines.
The annual RuleML Symposium has taken the lead in bringing together delegates from industry and academia who share this interest focus in Web rules.
See Introducing RuleML for more detail and links to slides.
1 Uses of Rules
Rules describe the general association of causes with effects ('laws'), situations with actions ('triggers'), premises with conclusions ('implications'), and so are used to represent physical, chemical and biological processes, medical guidelines, business and legal policies, conditional equations, probabilities and preferences, grammars, logics, and computer programs. Very special (constantly-true-premise, i.e. premiseless) implications are data records, including relational and object-centered ones (PSOA RuleML). Other special (single-premise, unary-predicate) implications in rulebases are equivalent to taxonomic subsumptions in ontologies (DistriSemWeb), more general rules can be used to extend ontologies, and rules provide transformation and implementation techniques for ontologies. Different and incompatible rule notations had emerged. RuleML has been established as a family of Web rule languages that allow all of these uses to be uniformly and precisely defined. Since rules are both descriptive and operational, a rule specification can be directly executed and debugged as a program. Since rule implications are formulas of a logic, a rule specification can be directly proved correct. Using the XML-based RuleML, specifications can interoperate across heterogeneous applications. Read more...
2 RuleML as a Bridge
RuleML (Rule Markup Language, which has also become a Rule Modeling Language) is a unifying family of XML-serialized rule languages spanning across all industrially relevant kinds of Web rules. As a research-based language family, RuleML acts as the connector between RIF -- via the emerging RIF RuleML subfamily -- and Common Logic -- via the planned CL RuleML subfamily. As an industry-focused de facto standard, RuleML has become the overarching specification of Web rules cross-fertilizing with corresponding OMG specifications (mainly SBVR and PRR) and constituting the foundation of an OASIS specification (LegalRuleML). Through its participation in SWRL and SWSL, RuleML has already accommodated and extended other rule languages, building interoperation bridges between them.
3 Scope of RuleML
The scope of RuleML is characterized here in the following dimensions: Natural and formal languages; deliberation and reaction rules; XML serialization, presentation syntaxes, and default semantics; internal/external translators and reference engines; as well as horizontal and vertical standardization. Read more...
4 The Initiative
RuleML Inc. is an international non-profit organization covering all aspects of Web rules and their interoperation. Its organizational structure and technical groups center on RuleML specification as well as tool and application development. RuleML Inc. functions as the organizational lead of the RuleML Initiative. The RuleML Initiative is an open network of individuals and groups from both industry and academia that has emerged around a shared interest in current rule topics, including the interoperation of Semantic Web rules. The RuleML Initiative has been collaborating with OASIS on Legal XML, Policy RuleML, LegalRuleML, and related efforts since 2004. The Initiative has further been interacting with the developers of ISO Common Logic (CL), which became an International Standard, First edition, in October 2007. RuleML is also a member of OMG, contributing to its Semantics of Business Vocabulary and Business Rules (SBVR), which was released as Version 1.0 in January 2008, and to its Production Rule Representation (PRR), which was released as Version 1.0 in December 2009. Moreover, participants of the RuleML Initiative have supported the development of the W3C Rule Interchange Format (RIF), which attained Recommendation status in June 2010 and published a Second Edition in February 2013. Read more...
5 Related Efforts
Conceptual, semantic, syntactic, serialization, and implementation efforts related to RuleML have been pursued at W3C, OMG, OASIS, and other standards bodies, as well as by universities, government initiatives, and industrial consortia. Some of these are listed here (please let us know of any updates and additions). Read more...
6 Participant Systems
Besides co-evolving with Related Efforts, the RuleML Initiative has been based on systems by its participants, including some of the following systems of the participants listed in parentheses. Read more...
The overall architecture of RuleML comprises a metamodel, semantic principles (e.g., the use of a default and variant semantics) and serialization principles (e.g., the use of 'striped' XML), a systematics of language features for modular language customization (in MYNG), a family of languages defined semantically (e.g., via model theory) and serialization-syntactically (via schema languages such as XSD and RNC), and normalizers defined as (XML-serialization) transformers (e.g., via XSLT). Read more...
Since RuleML should help rule-system interoperation, (XSLT, ...) translators for RuleML rulebases are rather important. Please send us further translator pairs between your system and RuleML -- even if your translators are (still) partial.
- RuleML<->POSL Converter: A pair of online translators between the POSL shorthand and its XML serialization
- PSOA2TPTP: PSOA RuleML translator to TPTP format, which can be executed, e.g., by VampirePrime
Various rule engines have been used to execute (queries posed to) RuleML rulebases as described in the following.
- OO jDREW: Naf Hornlog RuleML engine
- Prova: Reaction RuleML engine
- Drools: Reaction RuleML engine
- DR-DEVICE: Defeasible logic RuleML engine
- NxBRE: Naf Datalog RuleML engine
- VampirePrime: FOL reasoner
11 Positional-Slotted Language
The POsitional-SLotted (POSL) presentation, shorthand, and exchange syntax for rules (original POSL spec and POSL slides) combines Prolog's positional and F-logic's slotted syntaxes. The need for it had emerged from discussions on ASCII syntaxes in the Joint Committee. The bidirectional online online translator (including Types), in Java Web Start, has enabled writing knowledge bases in the RuleML/POSL shorthand while deploying them in the RuleML/XML serialization, as well as getting RuleML/XML rendered as RuleML/POSL. Several applications have been built on POSL (see, e.g. Rulebases:Master). An updated POSL version, as described in Integrating Positional and Slotted Knowledge on the Semantic Web, was implemented along with d-POSL in CS 6795 Semantic Web Techniques, Fall 2011, Team 1. Here is the updated OO jDREW 1.0 POSL/RuleML Translator (Java Web Start). POSL inspired some of the work on Positional-Slotted, Object-Applicative RuleML.
12 Positional-Slotted, Object-Applicative RuleML
This version: PSOA RuleML
Latest version: PSOA RuleML
Positional-Slotted, Object-Applicative RuleML (PSOA RuleML) permits a relation application to have an Object IDentifier (OID) -- typed by the relation -- and, orthogonally, its arguments to be positional or slotted. The resulting positional-slotted, object-applicative (psoa) terms can be used as (positional, relation-applying) classical facts without an OID and with an -- ordered -- sequence of arguments, as (slotted, object-centered) frame facts with an OID and with an -- unordered -- multi-set of slots (each being a pair of a name and a filler), as well as in various other ways. Such psoa facts and rules over them were given a first-order model-theoretic foundation (paper, slides), blending (OID-over-)slot distribution, as in RIF, with integrated psoa terms, as in RuleML. In order to support reasoning in PSOA RuleML, the PSOA2TPTP translator was implemented, which maps PSOA RuleML knowledge bases and queries to the TPTP format, as widely used for theorem provers. With this translator, reasoning in PSOA RuleML is available using the VampirePrime prover. The composition of PSOA2TPTP and VampirePrime to PSOATransRun has been developed under an online GUI. Read more...
RIF RuleML specifications are being collected here:
- Rulelog: Syntax and Semantics [HTML]
- Rulelog: Syntax and Semantics [PDF]
Convergence of RIF and RuleML is facilitated by PSOA RuleML.
Michael Sintek has implemented a (Java) parser for an RDF version of the Horn-logic subset of RuleML 0.8; it reflects an RDF RuleML syntax by (Java) classes that currently generate textual Horn clauses but could be adapted for generating the XML RuleML syntax: The FRODO rdf2java Tool. A converse translator from XML RuleML 0.8 to RDF RuleML 0.8 should be easier to write in XSLT than was possible for the above-linked RuleML 0.7 translator.
Taking newer RDF-rule developments such RIF In RDF into account, these tools should be updated for RuleML 1.0.
15 Graph Inscribed Logic
The Graph inscribed logic (Grailog) is a systematic combination of generalized graph constructs for visual data & knowledge representation ranging from (binary and n-ary) relational logic to Horn logic, description logic, object/frame logic, higher-order logic, and modal logic. Grailog thus provides a framework enabling analytics via 2-dimensional graph-logic visualization for humans in the loop of data & knowledge elicitation, specification, validation, as well as reasoning. Such Grailog visualization also serves as a teaching vehicle for making central (description- and Horn-)logical notions of ontologies and rules accessible to students of AI, Semantic Technologies, and related areas, as initiated for Logical Foundations of Cognitive Science. The Grailog Initiative for data & knowledge visualization is aligned with the Web-rule industry standard RuleML, where co-development is giving rise to synergies. Read more...
16 User Interfaces
User interfaces, particularly editors, for RuleML are described here.
- Rawe: Editor for rule markup of legal texts and conversion to LegalRuleML based on Akoma Ntoso markup
The FLIP Group uses RuleML in machine learning: About using RuleML for expressing machine learning knowledge. In the LispMiner project work with RuleML is directed towards statistical association rules.
18 Rulebase Library
A library of RuleML rulebases is being accumulated here as a collection of use cases for further design discussion and as examples for practical rule exchange (e.g., library and examples). The highest version of RuleML (currently 1.0) should be used whenever possible. If you have an entry, please send us its pointer. The discounting business rules example introduces some of the features: discount.ruleml (discount.ruleml.txt).
RuleML maintains a bibliography about RuleML syntax, semantics, reasoning, and applications, as well as studies directly supporting the RuleML language.
- Check out some Google Scholar results for RuleML.
- See also the results from Microsoft Academic Search