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 open standards effort of the organization 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 resulting Web-rule Specification of RuleML constitutes a system for Web knowledge representation that provides bridges between, e.g., RIF, SWRL, SWSL, TPTP, Common Logic, and LegalRuleML. Its families are Deliberation RuleML Version 1.02, Consumer RuleML Version 1.02, and Reaction RuleML Version 1.02.
RuleML Inc. functions as the organizational lead of the RuleML Initiative. Foundational Web-rule technology developed by the RuleML Initiative includes presentation and serialization syntaxes, lattice-configurable languages, transformations, model-theoretic semantics, and engines.
See Introducing RuleML for details and links to slides.
- 1 Uses of Rules
- 2 RuleML as a Bridge
- 3 Scope of RuleML
- 4 The Initiative
- 5 Related Efforts
- 6 Participant Systems
- 7 Architecture
- 8 Specification
- 9 Editors
- 10 Translators
- 11 Engines
- 12 Positional-Slotted Language
- 13 Positional-Slotted Object-Applicative RuleML
- 14 RIF
- 15 RDF
- 16 Graph Inscribed Logic
- 17 User Interfaces
- 18 Rule Learning
- 19 Rule-Based Data Access
- 20 Rulebase Library
- 21 Papers-Publications
- 22 Rule Events with RuleML
- 23 Structure
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, preferences, and probabilities; statistical correlations; grammars; transition functions; logics; database views; as well as declarative (functional, logic, and functional-logic) programs. While the if-then associations of imperative programs are hard-coded into a control flow, those of rule systems are 'soft-coded' into a rulebase (e.g., a set of rules) such that a rule engine can choose an appropriate rule for invocation in each computational cycle. Through transformation chains of iterated rule choices and invocations, arbitrary computations can be performed. Rules thus constitute the smallest units of computation, which can be aggregated into rulebase modules of rules and other rulebases. Read more...
2 RuleML as a Bridge
RuleML (Rule Markup Language, which has also become a Rule Modeling Language) is a unifying system of families of languages for Web rules specified, in part, through schema languages (normatively, in Relax NG) for Web documents and data originally developed for XML and later transferred to other formats such as JSON. As a research-based language system, RuleML acts as the connector between RIF -- via the emerging RIF RuleML -- and Common Logic -- via the planned CL RuleML. As an industry-focused de facto standard, RuleML spans across all industrially relevant kinds of rules and has become the overarching specification of Web rules cross-fertilizing with corresponding OMG specifications (mainly SBVR, PRR, and API4KP) 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 semantic styles; 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 semantic styles) and serialization principles (e.g., the use of 'striped' XML), a lattice of language features for modular language customization (in MYNG), a system of families of languages defined semantically (e.g., via model theory) and schema-syntactically (via schema languages such as XSD and RNC), and formatters (normalizers and compactifiers) defined as (XML-serialization) transformers (e.g., via XSLT). Read more...
8.1 Deliberation RuleML
8.2 Reaction RuleML
8.3 Consumer RuleML
User interfaces, particularly editors, for RuleML are described here.
- LIME: Editor for language-independent markup, with a demo for legal texts in LegalRuleML, called RAWE.
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
- TPTP RuleML: Datalog+, Hornlog+, and FOL RuleML translators to TPTP format
- DMN<->RuleML Translator: OMG DMN (S-Feel) translator service
Various rule engines have been used to execute (queries posed to) rulebases for subsets of RuleML as described in the following.
- OO jDREW: Naf Hornlog RuleML engine
- Prova: Reaction RuleML engine
- DR-DEVICE: Defeasible logic RuleML engine
- NxBRE: Naf Datalog RuleML engine
- VampirePrime: FOL reasoner
- XSB Prolog: Prolog engine
- PSOATransRun: Translator-based PSOA RuleML implementation
- PSOA Prova: PSOATransRun fork targeting Prova
12 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 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.
13 Positional-Slotted Object-Applicative RuleML
This is the main text about the multi-paradigm -- particularly, graph-relational -- data and rule language Positional-Slotted Object-Applicative RuleML (PSOA RuleML). If you are new to PSOA RuleML, you may want to start at Learn PSOA RuleML.
PSOA RuleML permits an atom, i.e. a predicate application, to be [in an oidless/oidful dimension 1] without or with an Object IDentifier (OID) -- typed by the predicate -- and the arguments of the predicate to be [in an orthogonal (descriptor-)variety dimension 2] tupled, slotted, or tupled+slotted (a tuple has zero or more ordered elements while a slot is a pair of a name and a filler). Moreover, PSOA RuleML since Version 1.0 permits an atom to be [in an orthogonal (descriptor-)dependency dimension 3] independent (having only predicate-independent descriptors), dependent (having only predicate-dependent descriptors), or independent+dependent. The resulting positional-slotted object-applicative (psoa) atoms can be used as (oidless, tupled, dependent) relationship facts without an OID and with a dependent -- ordered -- sequence of arguments, as (oidful, slotted, independent) framepoint facts with an OID and with an -- unordered -- multiset of independent slots, as well as in various other ways. For illustrating this systematics, the first section will present variations of a preview example regarding a business fact and rule. For another illustration see PSOA RuleML Explained with Blockchain Examples. Also, the slide Data in Perspective: The Rich TA Example as Grailog Visualization and PSOA RuleML Facts and the PSOAPerspectivalKnowledge paper focus on the dependency dimension as well as the broader notion of perspectivity (non-perspectival vs. perspectival).
With facts and rules over them collectively referred to as clauses, and clauses being the main items in a Knowledge Base (KB), the following characterizations will be advanced:
- An (in)dependent atom/clause/KB has at least one and has only (in)dependent descriptors/atoms/clauses. An independent+dependent atom has at least one independent descriptor and has at least one dependent descriptor; an independent+dependent clause/KB has at least one independent atom/clause and has at least one dependent atom/clause or it has at least one independent+dependent atom/clause.
- A (non-)perspectival atom does (not) have some dependent descriptor; a (non-)perspectival clause/KB does (not) have some perspectival atom/clause.
Given these, two observations (and their reverses) can be made:
- If an atom/clause/KB is dependent then it is perspectival. (If an atom/clause/KB is perspectival then it is dependent or independent+dependent.)
- If an atom/clause/KB is independent then it is non-perspectival. (If an atom/clause/KB is non-perspectival then it is empty or independent.)
In the above-linked Rich TA Example, three clauses, all facts, constitute a perspectival and independent+dependent KB: The slot workload+>high is dependent on the predicate TA. The first fact is constituted by the single-slot atom John#TA(workload+>high), which is perspectival and dependent. The second fact is similar to the third. The slot gender->male is independent while the slots dept+>Physics and dept+>Math are dependent on, respectively, the predicates Teacher and Student. The third fact is constituted by the atom John#Student(... dept+>Math gender->male), equivalently John#Student(... gender->male dept+>Math), which is perspectival and independent+dependent.
These notions, e.g. perspectivity, can be further lifted from a single KB to knowledge representation with multiple KBs. Thus, perspectival knowledge representation makes use of at least one dependent descriptor in an atom of a clause of a KB. Moreover, there is a spectrum of perspectivalness from exactly one dependent descriptor (minimally perspectival knowledge) to only dependent descriptors (maximally perspectival knowledge, i.e. dependent knowledge).
Psoa KBs were given a first-order model-theoretic foundation (original paper and slides, revised paper and slides, as well as further revised paper and slides), allowing, e.g., to normalize -- via objectification -- any oidless atom of the systematics to an oidful atom, and -- via describution -- any oidful multi-descriptor atom to a conjunction of single-descriptor atoms (distributing the atom's OID over its descriptors). In order to support reasoning in PSOA RuleML, the PSOATransRun system has been developed, including a combination of 1) a normalizing translator PSOA2Prolog, from PSOA RuleML to a subset of the logic programming language ISO Prolog, with 2a) the highly efficient XSB Prolog engine or 2b) the very widespread SWI Prolog engine. See the Examples section for PSOATransRun-executable examples of PSOA RuleML knowledge bases, queries, and answers. Read more...
RIF RuleML specifications are being collected here:
Interoperation between 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.
16 Graph Inscribed Logic
Copyright © RuleML Inc. -- Licensed under the RuleML Specification License 1.0 (http://ruleml.org/licensing/RSL1.0-Grailog)
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 can support Declarative Notation for Artificial Intelligence. Grailog 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...
17 User Interfaces
User interfaces, particularly editors, for RuleML are described here.
- LIME: Editor for language-independent markup, with a demo for legal texts in LegalRuleML, called RAWE.
18 Rule Learning
The FLIP Group uses RuleML in machine learning: About using RuleML for expressing machine learning knowledge. The Dagstuhl Seminar 15442, "Approaches and Applications of Inductive Programming", October 2015, included a presentation on "RuleML as a Declarative Language for Inputs, Outputs, and Background Knowledge in Inductive Programming" (pdf, pptx) as well as RuleML/Relfun and FLIP demos. The Inductive Programming (IP) community catalyzed around this Dagstuhl Seminar and the inductive-programming.org online platform continues giving feedback on RuleML to serve as a basis for functional-logic integration and IP-algorithm benchmarking. If you consider participating in an IP working group or future IP events, please contact José Hernández-Orallo.
19 Rule-Based Data Access
Ontology-Based Data Access (OBDA) -- from Ontology-Based Data Integration (OBDI) to Ontology-Based Data Querying (OBDQ) to Ontology-Based Data Management (OBDM) -- has become an active R&D topic in recent years, and is emerging as a major application area of Semantic Technologies for heterogeneous databases. OBDA ontologies encompass rule knowledge to enrich the factual data mapped -- again via rules -- to a global (homogeneous) schema from the local (heterogeneous) schemas of one or more databases. Given these and other roles of rules, we will focus on Rule-Based Data Access (RBDA) -- with a foundation in the basic Rule-Based Data Integration (RBDI), an emphasis on the central Rule-Based Data Querying (RBDQ), and some examination of the advanced Rule-Based Data Management (RBDM).
- For a tutorial-style introduction see "The Many Uses of Rules in Ontology-Based Data Access" (original abstract, current slides)
A preprint of the paper "A Datalog+ RuleML 1.01 Architecture for Rule-Based Data Access in Ecosystem Research", presented at RuleML 2014, is available (GeospatialRBDA, SpringerLink: Abstract). For a discussion on related topics, please contact any of the authors, Harold Boley, Rolf Grütter, Gen Zou, Tara Athan, or Sophia Etzold. Read more...
20 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 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.
Deliberation RuleML 1.01
- Business Scenario Rules
- Entries of Rulebase Competition 2014
Rule-Based Data Access
- ΔForest (simplified presentation syntax)
RuleML maintains a bibliography about RuleML syntax, semantics, reasoning, and applications, as well as studies directly supporting the RuleML language. On the RuleML Wiki:
On RuleMLer's pages:
Also see results for RuleML from
22 Rule Events with RuleML
- RuleML+RR 2019: 3rd International Joint Conference on Rules and Reasoning, Free University of Bozen-Bolzano, Italy.
- RuleML+RR 2018: 2nd International Joint Conference on Rules and Reasoning, University of Luxembourg, Luxembourg.
- RuleML+RR 2017: International Joint Conference on Rules and Reasoning, London, UK.
- RuleML 2016: Tenth International Web Rule Symposium, Stony Brook, New York, USA.
- RuleML 2015: Ninth International Web Rule Symposium, Berlin, Germany.
- RuleML 2014: Eighth International Web Rule Symposium, Prague, Czech Republic.
- RuleML 2013: Seventh International Web Rule Symposium, Seattle, USA.
- RuleML 2012: Sixth International Web Rule Symposium, Montpellier, France.
- RuleML 2011 @ BRF: Fifth International Web Rule Symposium]: Research Based and Industry Focused, Fort Lauderdale, FL, USA.
- RuleML 2011 @ IJCAI: Fifth International Web Rule Symposium, Barcelona, Spain.
- RuleML 2010: Fourth International Web Rule Symposium]: Research Based and Industry Focused, Washington, DC, USA.
- Semantic Rules Track at SemTech 2010, San Francisco, CA, USA.
- RuleML 2009: Third International RuleML Symposium on Rule Interchange and Applications, Las Vegas, Nevada, USA.
- RuleML 2008: Second International RuleML Symposium on Rule Interchange and Applications, Orlando, FL, USA.
- RuleML 2007: First International RuleML Symposium on Rule Interchange and Applications, Orlando, FL, USA.
- RuleML 2006: Second International Conference on Rules and Rule Markup Languages for the Semantic Web, Athens, Georgia, USA.
- Special Workshop on Reaction RuleML (Special Event at ISWC06), Athens, Georgia, USA.
- RuleML 2005: First International Conference on Rules and Rule Markup Languages for the Semantic Web, Galway, Ireland.
- RuleML 2004: Third International Workshop on Rules and Rule Markup Languages for the Semantic Web, Hiroshima, Japan.
- RuleML 2003: Second International Workshop on Rules and Rule Markup Languages for the Semantic Web, Sanibel Island, FL, USA.
- RuleML 2002: First International Workshop on Rule Markup Languages for Business Rules on the Semantic Web, Sardinia, Italy.