RuleML

The Rule Markup Initiative

New Format Notice: MediaWiki (Web Page Status Update, 8 December 2013)

The RuleML Home Page in HTML and its XML source are now frozen and will not be updated. The RuleML Wiki contains the new RuleML Home Page, which is accessible from the short URL http://ruleml.org.


The RuleML Initiative is an international non-profit organization covering all aspects of Web rules and their interoperation, with a Structure and Technical Groups that center on RuleML specification, tool, and application development. Around RuleML, an open network of individuals and groups from both industry and academia has emerged, having a shared interest in modern 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 went into Version 1.0 in January 2008, and to its Production Rule Representation (PRR), which went into 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. The annual RuleML Symposium has taken the lead in bringing together delegates from industry and academia who share this interest focus in Web rules.

RuleML (Rule Markup Language, which has also become a Rule Modeling Language and a Rule MetaLogic) 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 crosss-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. The current Specification of RuleML is Version 1.0.

Contents

News-Events

Resources

Mission Statement:

The goal of the Rule Markup Initiative is to develop RuleML as the canonical Web language for rules using XML markup, formal semantics, and efficient implementations.

RuleML covers the entire rule spectrum, from derivation rules to transformation rules to reaction rules. RuleML can thus specify queries and inferences in Web ontologies, mappings between Web ontologies, and dynamic Web behaviors of workflows, services, and agents.


Mission Approach:

Rather than focusing on academic research prototypes, RuleML is about rule interoperation between industry standards (such as JSR 94, SQL'99, OCL, BPMI, WSFL, XLang, XQuery, RQL, OWL, DAML-S, and ISO Prolog) as well as established systems (CLIPS, Jess, ILOG JRules, Blaze Advisor, Versata, MQWorkFlow, BizTalk, Savvion, etc.).

The Initiative develops a modular RuleML specification and transformations from and to other rule standards/systems. Moreover, it coordinates the development of tools to elicit, maintain, and execute RuleML rules. It also collects use cases, e.g. on business rules and reactive services.


Primer:

The RuleML Primer has its current focus on the Datalog sublanguage, developing the discount example from its parts.


Lists/Groups:
  • Subscribe to our main mailing list ruleml-all: Archives are available to subscribers.
  • Get up to date with the Fuzzy RuleML TG mailing list: fuzzy-tg.
  • Peek into the Reaction Rules TG mailing list: reaction-tg.
  • Consider to join the engine mailing list: jdrew-all.
  • Discuss Web rule-based agents on the Rule Responder TG mailing list: responder-tg.
  • Join the LinkedIn RuleML group or some of its subgroups: LinkedIn RuleML group.


Challenge Demos:

The page for RuleML Challenge Demos has been created by the research group of Yuh-Jong Hu from the Department of Computer Science at the National Chengchi University (NCCU), Taipei, Taiwan, where it is being maintained by Jack.

The Initiative

The participants of the RuleML Initiative constitute an open network of individuals and groups from both industry and academia. We are not commencing from zero but have done some work related to rule markup or have actually proposed some specific tag set for rules. Our main objective is to provide a basis for an integrated rule-markup approach that will be beneficial to all involved and to the rule community at large. This shall be achieved by having all participants collaborate in establishing translations between existing tag sets and in converging on a shared rule-markup vocabulary. This RuleML kernel language can serve as a specification for immediate rule interchange and can be gradually extended - possibly together with related initiatives - towards a proposal that could be submitted to the W3C.

Uses

If you want to review rule principles, (then) you may look at Rule-Based Expert Systems. (BTW, this is itself a simple rule.) If you want to review XML principles, you may go to the beginning of Knowledge Markup Techniques.

Rules are being used for many interconnected purposes, capturing regularities in application domains such as the following:

Rather than reinventing rule principles and markups in each such community, the idea of RuleML is to 'package' the rule aspect of these domains and and make it available as an (XML) namespace, .../RuleML, which can be mixed with a namespace for natural-language (XHTML) texts and possible domain-specific namespaces (much like MathML is mixed into such domain texts).

Scope

Rules can be stated (1) in natural language, (2) in some formal notation, or (3) in a combination of both. Being in the third, 'semiformal' category, the RuleML Initiative is working towards an XML-based markup language that permits Web-based rule storage, interchange, retrieval, and firing/application.

Markup standards and initiatives related to RuleML include:

Participants' Logos

The RuleML Initiative consists of the participants represented here by their logos.

Participants' Systems (Updated: 2008-07-22)

Besides on the related work, the RuleML Initiative is based on the following systems of the participants listed in parentheses:
  1. Agent Frameworks (Leon Sterling, Department of Computer Science and Software Engineering, University of Melbourne, Australia)
  2. AMIT/ADI (Asaf Adi, Ziva Sommer, IBM Research Lab in Haifa, Israel)
  3. AORML (Gerd Wagner, Faculty of Technology Management, I & T, Eindhoven University of Technology, The Netherlands)
  4. ARTEnterprise (Samir Rohatgi, Brian Sauk, MindBox Inc., USA)
  5. ASP RuleML (Roman Schindlauer, Thomas Eiter, Vienna University of Technology; Giovambattista Ianni, Universita' della Calabria; Austria, Italy)
  6. BotForm&trade (Sven Seelig, Sonja Muller Landmann, Smart Bot Technologies, Germany)
  7. BRML/DAML-RULES (Benjamin Grosof, MIT Sloan School of Management, USA)
  8. CDL (Steve Ross-Talbot, Pi4 Technologies; UK)
  9. CommonRules (Hoi Chan, IBM T.J. Watson Research, USA)
  10. Deimos&Phobos (Grigoris Antoniou, Fachbereich Mathematik & Informatik, Universität Bremen, Germany)
  11. DR-DEVICE (Nick Bassiliades, Logic Programming and Intelligent Systems (LPIS) Group, Dept. of Informatics, Aristotle University of Thessaloniki, Greece; Grigoris Antoniou, Information Systems Laboratory, Institute of Computer Science, FORTH, Heraklion, Crete, Greece)
  12. EAI Rules engine (Ruth Whalen, Darren D'Amato, Sybase Database Management Systems, New Era Of Networks, Inc., USA)
  13. Euler (Jos De Roo, AGFA, Belgium)
  14. FLIP (Jose Hernandez-Orallo, DSIC, Politechnical University of Valencia, Spain)
  15. Java Forward-Chaining Engines Integration (Emmanuel Bonnet, Guilhem Molines, Olivier Nicolas, Genigraph/OpTech Software, France, USA)
  16. Flora-2 (Michael Kifer, Guizhen Yang, Department of Computer Science State University of New York at Stony Brook, USA)
  17. jDREW (Bruce Spencer, Faculty of Computer Science, University of New Brunswick and Institute for Information Technology, National Research Council of Canada, Canada)
  18. Jess (Ernest Friedman-Hill, Distributed Systems Research, Sandia National Labs, USA)
  19. KNOW: Knowledge Norm Of Webmind (Pei Wang, Webmind Inc., USA)
  20. LispMiner (Vojtech Svatek, Jan Rauch, Vaclav Lin, Knowledge Engineering Group, Department of Information and Knowledge Engineering (DIKE), University of Economics, Prague, Czech Republic)
  21. LogicML/Bossam Rule Engine (Minsu Jang, Joochan Sohn, ETRI, Korea)
  22. Mandarax (Jens Dietrich, Department of Computer Science, Polytechnic of Namibia, Namibia)
  23. Obelix (Veljko Milutinovic, Sasa Mitrovic, Faculty of Electrical Engineering, University of Belgrade, Serbia and Montenegro)
  24. OCML (Enrico Motta, John Domingue, Knowledge Media Institute, The Open University, UK)
  25. OntoJava (Andreas Eberhart, International University in Germany, Germany)
  26. OWLTrans (Jing Mei, Networked Information Systems, Freie Universität Berlin, Germany)
  27. PDDL: Planning Domain Definition Language (Drew V. McDermott, Department of Computer Science, Yale University, USA)
  28. Protégé-2000 (Mark Musen, Stanford Medical Informatics, USA)
  29. Prova Language for Rule-based Java Scripting, Information Integration, and Agent Programming (Alex Kozlenkov, School of Informatics, City University, London, UK)
  30. RBML: Rule Base Markup Language (Chris Roberts, Sun Microsystems, USA)
  31. RBSLA: Rule-based Service Level Agreements (Adrian Paschke, Internet-based Information Systems (IBIS) , Department of Informatics, Technical University Munich, Germany)
  32. RFML (Harold Boley, DFKI, Germany)
  33. SeCo (Beat Schmid, Institute for Media and Communications Management, University of St. Gallen, Switzerland)
  34. Semantic Matchmaker (Katia Sycara, Massimo Paolucci, The Intelligent Software Agents Lab, The Robotics Institute, School of Computer Science, Carnegie Mellon University, USA)
  35. TRIPLE (Stefan Decker, Digital Enterprise Research Institute; Michael Sintek, DFKI; Germany, Ireland)
  36. Type-Based Diagnoser (Jan Maluszynski, Swedish Semantic Web Initiative, Department of Computer and Information Science, Linköping University, Sweden)
  37. URML (David Ash, Real Time Agents Inc.; Prabhakar Bhogaraju, MindBox; Said Tabet, Macgregor Inc.; USA)
  38. Versata Logic Suite for Transaction Logic (James Liddle, Kamran Yousaf, Versata; UK)
  39. Vivid Agents/Revise (Michael Schroeder, The School of Informatics, City University London, UK)
  40. VPP (Rand Anderson, Macgregor, USA)
  41. W4 (Carlos Viegas Damásio, CENTRIA (Centro de Inteligência Artificial da Universidade Nova de Lisboa), Portugal)
  42. Xcerpt (François Bry, Sebastian Schaffert, Teaching and Research Unit Programming and Modelling Language, Institute of Computer Science, Ludwig-Maximilians-Universität München, Germany)
  43. XET/XDD (Vilas Wuwongse, Chutiporn Anutariya, Knowledge Representation Laboratory, Asian Institute of Technology, Thailand)
  44. XRML (Jae Kyu Lee, ICEC, KAIST, Korea)

2001-05-11: "RuleML, the emerging standards effort on XML Rules knowledge representation, continues to progress in its design -- and also in its acceptance; notably, IBM this past month joined as participant and publicly disclosed that it is prototyping support for RuleML. Presentation materials from two recent W3C meetings, and a new short overview conference paper, are now available: see http://ebusiness.mit.edu/bgrosof/#XMLRules." (Also see: alphaWorks Posting. Contact: Hoi Chan.)

Initial Steps

Some initial steps taken by the RuleML Initiative have been to structure the area of rule markup, to raise issues and identify tasks, and to propose tentative rule tags/attributes.

Design

The Overarching Specification of Web Rules (Overarching) shows the big picture of how we conceive and formalize rule markup; this has been the basis of much of our more specific work.

Specification

2001-01-31: A preliminary RuleML DTD has been released: RuleML DTD Version 0.7.

2001-07-11: A revised DTD version has been finalized: RuleML DTD Version 0.8.

2001-09-25: A preliminary XML Schema for a Datalog subset of RuleML has been released: RuleML Schema Version 0.8.

2002-04-02: A query DTD version has been realized (cf. Queries): RuleML DTD Version 0.8.

2004-01-28: A revised DTD/XSD version has been released: RuleML Version 0.85.

2004-07-15: A stable XSD version has been released: RuleML Version 0.86.

2004-08-12: A new XSD version has been released: RuleML Version 0.87.

2004-11-02: A monolithic DTD version of FOL RuleML 0.9 has been released: FOL RuleML Version 0.9.

2005-03-01: A new XSD version has been released: RuleML Version 0.88.

2005-05-27: A new XSD version has been released: RuleML Version 0.89.

2005-11-09: A new XSD version has been released: RuleML Version 0.9.

2006-08-24: A new XSD version has been released: RuleML Version 0.91.

2011-12-09: A new XSD / Relax NG version has been released: RuleML Version 1.0.

Queries

As in many deduction approaches, RuleML queries are regarded as headless implications, symmetrically to regarding facts as bodiless implications. They enumerate the bindings of all their free (existentially interpreted) variables.

Queries were added to RuleML 0.8 as a third top-level element of rulebases besides facts and imps (since this extension is purely additive, all queriless RuleML 0.8 rulebases should still validate). This gives us "for free" all refinements of RuleML's existing system of sublanguages via the _body role of queries: because of RuleML's DTD inheritance, additions were only required for ruleml-datalog.dtd (ruleml-datalog.dtd.txt) and urcbindatagroundfact.dtd (urcbindatagroundfact.dtd.txt). In particular, ruleml-datalog.dtd's query-extended rulebase definition is inherited by ruleml-hornlog.dtd, where queries in datalog use only inds and vars but queries in hornlog automatically also permit cterms (because hornlog atoms permit cterms).

RuleML queries are illustrated by our business-rule example discount.ruleml (discount.ruleml.txt) and by Eric Prud'hommeaux's RDF Query example wsdl-rdf-query.ruleml (wsdl-rdf-query.ruleml.txt).

This query incorporation into RuleML assumes that the sublanguage expressiveness should be the same for 'assertions' (facts and imps) and for the 'requests' (queries) on them. So, it cannot, e.g., express queries on ground triples (containing no variables) via non-ground triples (containing variables): creator.ruleml (creator.ruleml.txt). However, users can still employ one rulebase (module) with its DTD for 'assertions', and another rulebase (module) with a different DTD for 'requests'.

ASP RuleML

ASP RuleML defines a sublanguage of RuleML for answer-set programs in XML Schema. This variant facilitates the specification of a number of ASP-related constructs in a general manner. Moreover, it constitutes a base language for specific ASP extensions, such as HEX-programs.

Object-Oriented RuleML

Via Object-Oriented RuleML (OO RuleML) frame-like knowledge representation with facts (instances) and rules (methods) is now directly supported.

Positional-Slotted, Object-Applicative RuleML (Updated: 2012-08-04)

Positional-Slotted, Object-Applicative RuleML (PSOA RuleML) permits relation applications with optional object identifiers and, orthogonally, arguments that are positional or slotted. The resulting positional-slotted, object-applicative (psoa) terms and rules over them were given a first-order model-theoretic foundation (paper, slides), blending slot distribution, as in RIF, with integrated psoa terms, as in RuleML. In order to support reasoning in PSOA RuleML, the implemention of the PSOA2TPTP translator is in progress, which maps PSOA RuleML knowledge bases 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 is being developed at PSOA RuleML.

RIF

RIF RuleML specifications are being collected here:

Convergence of RIF and RuleML is facilitated by PSOA RuleML.

RDF

An experimental RDF translator for a subset of RuleML 0.7 is available in XSLT: RuleML in RDF Version 0.2. RuleML 0.8 now stands in the direct Context of RDF.

2001-06-20: 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.

RuleML Lite

RuleML Lite has been developed basically as a RuleML subset compatible with RDF and OWL-DL that covers webized unary and binary Datalog facts, rules, and queries. The RuleML Lite design has interacted with the SWRL design via the Joint Committee.

FOL RuleML

The FOL RuleML language has been developed in interaction between the RuleML Steering Committee and the Joint Committee, with input from Simplified Common Logic (SCL). FOL RuleML shares/reuses most of the earlier RuleML LP syntax, incorporating First-Order-Logic quantifiers and disjunctions as well as equivalence and negation. FOL RuleML strives for a strict separation of declarative content from procedural (Assert, Query) performatives, as pioneered by KQML. This and further changes to the current RuleML 0.87 will also benefit other sublanguages towards RuleML 0.9, in particular the Horn logic subset. FOL RuleML is the rule component of SWRL FOL and a proposed FOL content language for SWSI. It can be viewed as a generalization of SWRL FOL in that it is an XML form of full FOL, with n-ary relations (predicate symbols) and constructors (logical function symbols).

2004-11-02: FOL RuleML 0.9 has been released, using a monolithic DTD specification.

2004-11-14: An FOL RuleML announcement has been sent.

Induction

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.

Translators

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.

In February 2001 Mike Dean created the first operational RuleML rulebase, GEDCOM, with rules on family relationships (child, spouse, etc.) run via XSLT translators to the XSB, JESS, and n3/cwm engines.

2001-09-17: Harold Boley has specified XSLT translators between the Horn-logic subsets of RuleML and RFML. These can make implementations of both systems available to each other and permit, e.g., a preliminary HTML rendering of RuleML rulebases. The XSLT stylesheets may also serve as blueprints for specifying further translators to/fro RuleML.

2001-09-19: Andreas Eberhart implemented an alpha version of OntoJava. The basic idea is to automatically map Protégé ontologies, instances defined in them, and RuleML rules into a sinlge Java main memory DB / rule engine that can then be used as the basis of an application. He is looking forward to hearing of your ideas and input.

2002-02-04: Andreas Eberhart extended OntoJava by reaction rules:

<java>
        runtime.Loader.load("http://localhost:8080/servlet/SearchGate?flight="
                         + <var>F</var>.name, false);
</java>
This example loads RDF info into the DB, which comes from a kind of Web Service. So emails can be sent as well, etc. While this is not 'cross-platform', it should be interessting from an engineering point of view.

2002-07-08: Said Tabet created an XSLT stylesheet for transforming from a version of RuleML to Jess. The full Java environment for running this is available from Said Tabet.

2003-08-26: Stephen Greene has specified XSLT translators between Positional and Object-Oriented RuleML.

2004-08-12: David Hirtle has created an XSLT translator between RuleML 0.86 and 0.87 as part of the 0.87 release.

2005-03-01: David Hirtle has created an XSLT "upgrader" to translate between RuleML 0.87 and 0.88 as part of the 0.88 release. An XSLT "normalizer" for reconstructing all skipped role tags to achieve a fully-expanded, normal form is also included with this release.

2005-05-27: The XSLT "upgrader" from 0.88 to 0.89 has been created as part of the 0.89 release. An updated XSLT "normalizer" for achieving a normal form is also included.

2005-09-13: Jie Li has updated an earlier translator from RFML to RuleML as part of the ChemXelem use case.

Engines

One or more rule engines will be needed for executing RuleML rulebases. On 2000-11-15, the RuleML Initiative thus joined forces with the Java Specification Request JSR-000094 Java Rule Engine API. This cooperation will enable a direct cross-fertilization between the complementary specifications of the open XML-based Rule Markup Language and of the Java runtime API for rule engines.

2001-06-04: Jens Dietrich implemented the first complete input-processing-output environment for RuleML. To download the api (source code) click Mandarax RuleML. Any feedback is welcome! If you have problems, don't hesitate to contact Jens for assistance.

2002-03-08: Jens Dietrich has finally published Mandarax 1.6 with major improvements, including new docs and all the features discussed in the Mandarax Dagstuhl Talk. One of the new packages is xkb_b1.jar -- it contains a modular driver to translate rule bases to XML and vice versa. I.e., there are tiny adapter objects responsible for exporting/importing rules, facts, terms etc. This should enable us to set up a reference application for any new standard in hours.

2001-06-26: Michael Sintek has implemented a small XSB-based engine that can also be looked at as the first RuleML querying agent. It's a servlet (running in Tomcat) that receives RuleML rulebases in RDF RuleML syntax (since he uses The FRODO rdf2java Tool) together with some queries, evaluates them with XSB Prolog (in auto-tabling mode, which should be equivalent to bottom-up evaluation!), and returns the result as an HTML page containing the bindings as facts of instantiated queries. A future version must, of course, return a RuleML file. Simply try this URL. Click on 'example' and paste the RDF RuleML popping up into the input window (note that pasting XML/RDF cannot be directly done in IE, only in Netscape; use "view source" in IE). Alternatively, you can use the Prolog parser and RDF translator to generate the RDF RuleML. Since we cannot guarantee that the above URLs always work (server reboots etc.), this picture shows the agent in action. Any feedback is welcome! If you have problems, don't hesitate to contact Michael for assistance.

2002-02-06: Bruce Spencer further refined The Design of j-DREW, a Deductive Reasoning Engine for the Semantic Web.

2005-05-06: Marcel Ball [maball AT gmail DOT com] revised the documentation of OO jDREW, summarized in the Position Paper Implementing RuleML Using Schemas, Translators, and Bidirectional Interpreters of the W3C Workshop on Rule Languages for Interoperability.

2007-07-26: Benjamin Craig [ben.craig AT unb DOT ca] has been continuing the development of OO jDREW with a series of extended releases, and is developing the OO jDREW part of Rule Responder.

2008-06-30: A team led by Nick Bassiliades, Grigoris Antoniou and Guido Governatori has released a new version of DR-Device (version 0.81) with support for modalities and proof exporting. DR-Device is a defeasible logic reasoning system with priorities among rules, two types of negation (strong, default) and conflicting (mutually exclusive) literals. The system is implemented on top of CLIPS and has been extended to introduce rule modes that determine the modality of the conclusion and modalized literals in the premises of rule bodies. Furthermore, the system exports in a formal RuleML-like representation an explanation for the proof of the rule program conclusions. The aim is (a) to take advantage of the expressive power of modal logics to define various agent behaviors, and (b) to to increase user/agent trust towards rule-based Semantic Web applications.

Positional-Slotted Language (Updated: 2012-11-17)

The Positional-Slotted presentation, shorthand, and exchange syntax for rules (POSL spec, POSL slides) merges Prolog's positional and F-Logic's slotted syntaxes. Its need has emerged from discussions on ASCII syntaxes in the Joint Committee. A pair of online translators (including Types), in Java Web Start, have 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. library). An updated POSL version is described in Integrating Positional and Slotted Knowledge on the Semantic Web, implemented along with d-POSL in CS 6795 Semantic Web Techniques, Team 1, and available from GitHub via the OO jDREW site as a pair of online translators, in Java Web Start.

Graph inscribed logic (Updated: 2013-06-04)

The Graph inscribed logic (Grailog) was initially developed for the course Logical Foundations of Cognitive Science and has since much evolved, as presented in a series of talks, including RuleML/Grailog: The Rule Metalogic Visualized with Generalized Graphs at PhiloWeb 2011 and The Grailog User Interface for Knowledge Bases of Ontologies & Rules at OMGCambridge2012.

The newest edition of Grailog is being maintained in presentable form: RuleMLGrailog.pdf (RuleMLGrailog.ppt)

RuleML/Grailog comes with a mapping from (2-dimensional, 'analogical') generalized graphs to a RuleML/POSL-type (1-dimensional, 'symbolic') logic generalized to an expressive Rule Metalogic. The transition from RuleML/Grailog to RuleML/POSL to RuleML/XML proceeds in the direction from human-oriented to machine-oriented syntaxes. Since a translator from RuleML/POSL to RuleML/XML already exists (see POSL), its three components (i.e., its source RuleML/POSL, its target RuleML/XML, and the translator itself) could be generalized to cover full RuleML/Grailog expressiveness, and a translator from RuleML/Grailog to RuleML/XML could then be obtained by implementing a translator from RuleML/Grailog (in an appropriate format) to the generalized RuleML/POSL. Alternatively, a translator from RuleML/Grailog (maybe in SVG/XML) directly to the generalized RuleML/XML could be implemented. Subsets of Grailog corresponding to Datalog and Hornlog RuleML will be natural starting points for such an open source translator project. Everyone who is interested in the further development of Grailog or its translators, please contact Harold Boley.

User Interfaces (Updated: 2011-03-21)

RuleML Participant Jens Dietrich's Oryx (version 2.1) has a graphical Knowledge Editor for business rules and a Repository that contains the description of predicates, functions, and database connections. "Oryx works with open XML based formats, support for the emerging RuleML standard and the open source Mandarax XKB 1.0 format is included." (http://www.jbdietrich.com).

Michael Sintek has implemented a Prolog parser and RDF translator to generate RDF RuleML.

Andreas Eberhart wrote a small tool that allows you to convert Prolog (currently, Datalog) rules to RuleML. This Prolog2RuleML tool is available both online and as a command line version.

2005-12-13: A team led by Nick Bassiliades and Grigoris Antoniou has released VDR-Device (version 0.27). VDR-Device is a visual environment for developing defeasible logic rule bases for the Semantic Web. VDR-Device integrates a graphical RuleML-compliant rule editor and a defeasible reasoning system that processes RDF data and RDF Schema ontologies. The rule editor constrains the allowed vocabulary after analyzing the input RDF Schema ontologies, preventing potential syntactic and semantic errors. The reasoning system supports all defeasible logic rule types, priorities among rules, two types of negation and conflicting literals.

2006-04-28: A Java Web Start application called TRANSLATOR has been developed by David Hirtle for translating from Attempto Controlled English to RuleML. The reverse direction, RuleML to English, is a planned extension.

2009-12-08: S2REd is a syntactic+semantic text-based RuleML editor. It is a full-fledged textual XML editor that features: syntax highlighting, spell checking, brace matching, code-block coloring, pretty-printing, well-formedness and validation checking. Nevertheless, S2REd is more than an XML editor: it’s a tool totally dedicated to RuleML authoring, offering domain-specific semantically enabled functionality. Developed by Thetida Zetta, Efstratios Kontopoulos, and Nick Bassiliades. Project URL: S2REd

2011-03-21: S2REd, the syntactic+semantic RuleML editor developed by Thetida Zetta, Efstratios Kontopoulos, and Nick Bassiliades at Aristotle University of Thessaloniki, Greece, has been upgraded to provide support for RuleML 1.0.

Rulebase Library (Updated: 2011-11-20)

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).

Publications

Structure (Updated: 2012-08-04)

Maintained on the wiki at Organizational Structure.

Contacts

If you are interested to join the RuleML Initiative, please send a link describing your work related to rule markup to Harold Boley and Said Tabet; same for general RuleML questions/suggestions. Depending on your specific RuleML interests, you may also contact some RuleML Technical Group (select above) or some RuleML Participant (select above). You are encouraged to subscribe to our main mailing list: ruleml-all.


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