OASIS members & interested parties,
A new OASIS technical committee is being formed. The OASIS LegalRuleML Technical Committee has been proposed by the members of OASIS listed in the charter below. The TC name, statement of purpose, scope, list of deliverables, audience, IPR mode and language specified in the proposal will constitute the TC’s official charter. Submissions of technology for consideration by the TC, and the beginning of technical discussions, may occur no sooner than the TC’s first meeting.
The eligibility requirements for becoming a participant in the TC at the first meeting are:
(a) you must be an employee of an OASIS member organization or an individual member of OASIS, and
(b) you must join the Technical Committee, which members may do by using the “Join this TC” button on the TC’s home page at [a].
To be considered a voting member at the first meeting, you must:
(a) join the Technical Committee at least 7 days prior to the first meeting (on or before 12 January 2012); and
(b) you must attend the first meeting of the TC, at the time and date fixed below (19 January 2012).
Participants also may join the TC at a later time. OASIS and the TC welcome all interested parties.
Non-OASIS members who wish to participate may contact us about joining OASIS [b]. In addition, the public may access the information resources maintained for each TC: a mail list archive, document repository and public comments facility, which will be linked from the TC’s public home page.
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CALL FOR PARTICIPATION
OASIS LegalRuleML Technical Committee
The charter for this TC is as follows.
=== Charter ===
1.a Name of the TC:
LegalRuleML Technical Committee
1.b Statement of Purpose:
The goal of the LegalRuleML TC is to extend RuleML [RuleML 2011] with features
specific to the formalisation of norms, guidelines, and legal reasoning.
Legal texts are the source of norms, guidelines, and rules that often feed into
different concrete (usually XML-based) Web applications. Legislative documents
typically provide general norms and specific procedural rules for eGovernment
and eCommerce environments, while contracts specify the conditions of services
and business rules (e.g. service level agreements for cloud computing), and
judgments provide information about arguments and interpretation of norms that
establish concrete case-law.
The ability to have proper and expressive conceptual models of the various and
multifaceted aspects of norms, guidelines, and general legal knowledge is a key
factor for the development and deployment of successful applications.
The LegalRuleML TC aims to produce a rule interchange language for the legal
domain. This will enable modeling and reasoning such that implementers can
structure, evaluate, and compare legal arguments constructed using the rule
representation tools provided.
1.c Scope of Work:
The Artificial Intelligence (AI) and Law communities have converged in the last
twenty years on modeling legal norms and guidelines using logic and other
formal techniques [Ashley 2011]. Existing methods begin with the analysis of a
legal text by a Legal Knowledge Engineer who extracts the norms and guidelines,
applies models and a theory within a logical framework, and finally represents
the norms using a particular formalism. In the last decade, several Legal XML
standards were proposed to describe legal texts [Lupo et al. 2007] with
XML-based rules (RuleML, SWRL, RIF, LKIF, etc.) [Gordon et al. 2009; Gordon
2008]. In the meantime, the Semantic Web, in particular Legal Ontology research
combined with semantic norm extraction based on Natural Language Processing
(NLP) [Francesconi et al. 2010], gave a great impulse to the modelling of legal
concepts [Boer et al. 2008; Benjamins 2005; Breuker 2006].
Based on this, the work of the LegalRuleML Technical Committee will focus on
three specific needs:
1. Closing the gap between natural language text description and semantic
norm modelling, in order to realise an integrated and self-contained
representation of legal resources that can be made available on the Web as XML
representations [Palmirani 2009]. This formal underpinning can then foster
Semantic Web technologies such as: NLP, Information Retrieval (IR), graph
representation, as well as Web ontologies and rules.
2. To provide an expressive XML standard for modelling normative rules that
is able to satisfy the legal domain requirements. This will enable use of a
legal reasoning level on top of the ontological layer that aligns with the W3C
envisioned Semantic Web stack. This approach seeks also to fill the gap between
regulative norms, guidelines and business rules in order to capture and model
the processes embedded in those guidelines and make them usable for the
workflow and business layer [Governatori 2010; Rotolo 2009; Grosof 2004];
3. Supporting the Linked Open Data [Berners-Lee 2010] approach to modelling
regarding not only the semantics of raw data (acts, contracts, court files,
judgments, etc.), but also of rules in conjunction with their functionality and
usage. Without rules or axioms, legal concepts constitute just a taxonomy
The LegalRuleML TC work will address these three main goals and provide means for modeling norms, guidelines, judgements, and contracts using a semantic approach.
In particular, the LegalRuleML work will extend the existing RuleML, RIF and related Web rule work by improved modeling as well as representing and capturing the legal knowledge embedded in legal texts.
Specifically, the LegalRuleML work will facilitate the following functionalities.
A) Support for Modelling different types of rules:
– CONSTITUTIVE RULES, which define concepts or constitute activities that
cannot exist without such rules (especially Legal definitions such as
– TECHNICAL RULES, which state that something has to be done in order for
something else to be attained (especially Rules governing taxation).
– PRESCRIPTIVE RULES, which regulate actions by making them obligatory,
permitted, or prohibited (especially obligations in contracts).
B) Implementing ISOMORPHISM [Bench-Capon-Coenen 1992]. To ease validation and
maintenance, there should be a one-to-one correspondence between the rules in
the formal model and the units of (controlled) natural language text that
express the rules in the original legal sources, such as sections of
C) Manage the REIFICATION [Gordon 1995] of rules that are objects with
properties, such as Jurisdiction, Authority, Temporal attributes [Palmirani
2010; Governatori 2009; Governatori 2005]. These elements have to be added to
the current RuleML to enable effective legal reasoning.
D) Represent NORMATIVE EFFECTS and VALUES. There are many normative effects
that follow from applying rules, such as obligations, permissions,
prohibitions, and also more articulated effects such as those introduced.
Usually, some values are promoted by legal rules as well.
F) Implement DEFEASIBILITY [Gordon 1995, Prakken 1996, Sartor 2005]. When the
antecedent of a rule is satisfied by the facts of a case (or via other rules),
the conclusion of the rule presumably holds, but is not necessarily true. The
defeasibility of legal rules breaks down into the following issues: Conflicts
and Exclusionary rules.
Lastly, the LegalRuleML work will also aim to model legal procedural rules.
Rules not only regulate the procedures for resolving legal conflicts, but also
are used for arguing or reasoning about whether or not some action or state
complies with other, substantive rules. In particular, rules are required for
procedures which regulate methods for detecting violations of the law, i.e.,
which determine the normative effects triggered by norm violations, such as
reparative obligations, which are meant to repair or compensate violations.
Note that these constructions can give rise to very complex rule dependencies,
because the violation of a single rule can activate other (reparative) rules,
which in turn, in case of their violation, refer to other rules, and so forth.
In this case, the Deliberation RuleML and Reaction RuleML parts [Boley et al. 2010] are coordinated within the LegalRuleML module to produce benefits for applications and reasoning engines (avoiding redundancy in the rules as well as facilitating coordination, synchronisation, and cooperation.)
Compatibility with the RuleML 1.0 schemas [Athan et al. 2011; Boley 2011; Boley et al. 2010; Wagner et al. 2004; Boley et al. 2001] and interoperability with the main languages for rule modelling, mainly Common Logic, RIF, and SWRL.
Out of Scope:
Developing tools for LegalRuleML. (This will be started by the supporters of this proposal and others independently once a first stable version of LegalRuleML exists.)
The LegalRuleML TC will provide XML representations that address the aforementioned requirements and support interchange with the business rule domain.
The following deliverables are expected:
D1. LegalRuleML semantic level (e.g. temporal dimension) drafts – within six months of the first TC meeting
D2. LegalRuleML logic level (e.g. defeasibility, deontic, and argumentation) drafts – within eight months of the first TC meeting
D3. LegalRuleML integration with business and process rule drafts – within ten months of the first TC meeting
D4. Pilot use cases – within twelve months of the first TC meeting
D5. Tutorials and general documentation – continuously produced and updated during the entire process
The semantic and logic levels constitute the core part of the LegalRuleML functionality. They define the principles of design, the architecture of the syntax, the main elements for managing patterns, abstract types, groups of attributes, general classes, ontology-level connections, and rule-level connections.
Once the TC has completed work on a deliverable that has become an OASIS Standard, the TC will enter “maintenance mode” for the deliverable.
The purpose of maintenance mode is to provide minor revisions to previously adopted deliverables to clarify ambiguities, inconsistencies, and obvious errors. Maintenance mode is not intended to enhance a deliverable or to extend its functionality.
The TC will collect issues raised against the deliverables and periodically process those issues. Issues that request or require new or enhanced functionality shall be marked as enhancement requests and set aside. Issues that result in the clarification or correction of the deliverables shall be processed. The TC shall maintain a list of these adopted clarifications and shall periodically create a new minor revision of the deliverables including these updates. Periodically, but at least once a year, the TC shall produce and vote upon a new minor revision of the deliverables.
1.e IPR Model
This TC will operate under the “RF (Royalty Free) on Limited Terms” IPR mode as defined in the OASIS Intellectual Property Rights (IPR) Policy.
1.f Anticipated Audience
The anticipated audience for this work includes:
1. Vendors and service providers offering products and/or services in the legal domain (e.g. eGovernment, cloud computing SLAs, contracting, and legislation)
2. Authors of other specifications that require rule language standards for legal, regulatory and policy representations
3. Software architects who design, write, integrate, and deploy rule engines in the legal domain
4. End users modelling legal rules that require an interoperable solution using a standard language
5. The U.S. NIEM community for government domain rule management and representation
6. The OASIS LegalXML MS and other OASIS entities that are providing input for and/or are planning to refer to LegalRuleML from their specifications.
The output documents will be written in (US) English. TC meetings shall be conducted in English.
1. Ashley Kevin D., van Engers Tom M. (Eds.): The 13th International Conference on Artificial Intelligence and Law, Proceedings of the Conference, June 6-10, 2011, Pittsburgh, PA, USA. ACM 2011
2. Athan T., Boley H.: Design and Implementation of Highly Modular Schemas for XML: Customization of RuleML in Relax NG. RuleML America 2011: 17-32
3. Bench-Capon T. and Coenen F.: Isomorphism and legal knowledge based systems. Artificial Intelligence and Law, 1(1):65–86, 1992.
4. Benjamins V. R., Casanovas P., Breuker J., and Gangemi A., editors. Law and the Semantic Web: Legal Ontologies, Methodologies, Legal Information Retrieval and Applications. Springer-Verlag, 2005.
5. Berners-Lee T.: Long Live the Web: A Call for Continued Open Standards and Neutrality, Scientific America, 2010.
6. Boer A., Radboud W., Vitali F.: MetaLex XML and the Legal Knowledge Interchange Format, in Computable Models of the Law, Springer, 2008.
7. Boley H., Paschke A., Shafiq O.: RuleML 1.0: The Overarching Specification of Web Rules. RuleML 2010: 162-178.
8. Boley H., Tabet S., and Wagner G.: Design rationale for RuleML: A markup language for Semantic Web rules. In I. F. Cruz, S. Decker, J. Euzenat, and D. L. McGuinness, editors, Proc. SWWS’01, The first Semantic Web Working Symposium, pages 381–401, 2001.
9. Boley H.: A RIF-Style Semantics for RuleML-Integrated Positional-Slotted, Object-Applicative Rules. RuleML Europe 2011: 194-211
10. Breuker J., Boer A., Hoekstra R., Van Den Berg C.: Developing Content for LKIF: Ontologies and Framework for Legal Reasoning, in Legal Knowledge and Information Systems, JURIX 2006, pp.41-50, ISO Press, Amsterdam, 2006.
11. Francesconi E., Montemagni S., Peters W., Tiscornia D.: Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language Springer 2010.
12. Giovanni S.: Legal concepts as inferential nodes and ontological categories. Artif. Intell. Law 17 (3): pp. 217-251, 2009.
13. Gordon T. F., Guido Governatori, Antonino Rotolo: Rules and Guidance: Requirements for Rule Interchange Languages in the Legal Domain. RuleML 2009: pp. 282-296, 2009.
14. Gordon T. F.: Constructing Legal Arguments with Rules in the Legal Knowledge Interchange Format (LKIF). Computable Models of the Law, Languages, Dialogues, Games, Ontologies 2008, pp. 162-184, 2008.
15. Gordon T. F.: The Pleadings Game; An Artificial Intelligence Model of Procedural Justice. Springer, New York, 1995. Book version of 1993 Ph.D. Thesis; University of Darmstadt, 1993.
16. Governatori G. and Rotolo A.: Changing legal systems: Legal abrogations and annulments in defeasible logic. The Logic Journal of IGPL, 2010.
17. Governatori G., Rotolo A., and Sartor G.. Temporalised normative positions in defeasible logic. In Proc. ICAIL’05, pages 25–34. ACM Press, 2005.
18. Governatori G., Rotolo A.: Norm Compliance in Business Process Modeling. RuleML 2010: pp. 194-209, 2010
19. Governatori G.: Representing business contracts in RuleML. International Journal of Cooperative Information Systems, 14(2-3):pp. 181–216, 2005.
20. Grosof B. Representing e-commerce rules via situated courteous logic programs in RuleML. Electronic Commerce Research and Applications, 3(1):2–20, 2004.
22. Lupo C., Vitali F., Francesconi E., Palmirani M., Winkels R., de Maat E., Boer A., and Mascellani P: General xml format(s) for legal sources – Estrella European Project IST-2004-027655. Deliverable 3.1, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands, 2007.
23. Palmirani M., Contissa G., Rubino R.: Fill the Gap in the Legal Knowledge Modelling. RuleML 2009: 305-314, 2009.
24. Palmirani M., Governatori G. and Contissa G:. Temporal Dimensions in the Rules: an Evolution of LKIF Rule, Jurix 2010.
25. Palmirani M., Governatori G., Rotolo A., Tabet S., Boley H., Paschke A.: LegalRuleML: XML-Based Rules and Guidance. RuleML America 2011: 298-312 26. Prakken H. and Sartor G.: A dialectical model of assessing conflicting argument in legalreasoning. Artificial Intelligence and Law, 4(3-4):331–368, 1996.
27. Rotolo A., Sartor G., and Smith C.: Good faith in contract negotiation and performance. International Journal of Business Process Integration and Management, 5(4), 2009.
28. RuleML. The Rule Markup Initiative. http://www.ruleml.org, accessed 8th
29. Sartor G.: Legal reasoning: A cognitive approach to the law. In E. Pattaro, H. Rottleuthner, R. Shiner, A. Peczenik, and G. Sartor, editors, A Treatise of Legal Philosophy and General Jurisprudence, volume 5. Springer, 2005.
30. Wagner G., Antoniou G., Tabet S., and Boley H.: The abstract syntax of RuleML – towards a general web rule language framework. In Proc. Web Intelligence 2004, pages 628–631.
2.a Identification of similar or applicable work:
The LegalRuleML TC will incorporate definitions and terminologies from OASIS standards, especially from which coming from LegalXML and XACML TCs, as well as from standards work done by non-OASIS organizations. As stated in the charter, the TC will use a standard from one non-OASIS organization and may choose to use the works of other OASIS TCs and standards from non-OASIS organizations, as it sees fit. Liaisons may be established, and the TC may agree to concurrent work items with other TCs and organizations, within the scope defined here. Among other things, the TC may establish liaisons with W3C (RIF), OMG, and other such standards organizations, as it may choose.
2.b The date, time, and location of the first meeting:
The LegalRulML TC will hold its first official meeting on 19 January 2012 at 7:00pm UTC, 2:00pm (U.S. EST), 11:00am (U.S. PDT) 20:00 (CET) 6:00am (Australia EDT) by telephone and will use a free conference call service. CIRSFID will host the first technical meeting.
2.c The projected on-going meeting schedule for the year:
The TC will meet bi-weekly or as otherwise agreed upon by the members of the technical committee.
2.d The names, electronic mail addresses, and membership affiliations of at least Minimum Membership who support this proposal:
1. Monica Palmirani, firstname.lastname@example.org, CIRSFID, University of Bologna, Italy
2. Guido Governatori, email@example.com, NICTA, Queensland Laboratory, Australia, and RuleML director
3. Antonino Rotolo, firstname.lastname@example.org, CIRSFID, University of Bologna, Italy, and RuleML director
4. Carl Mattocks,email@example.com, Individual Member
5. Joseph D.K. Wheeler,firstname.lastname@example.org, MTG Management Consultants, L.L.C.
6. Mark Proctor, email@example.com, Red Hat Inc.
2.e Primary Representative Approval Statements:
Monica Palmirani, firstname.lastname@example.org, CIRSFID, University of Bologna, Italy:
As CIRSFID’s Primary Representative, I approve the LegalXML TC Charter and its goals on legal modelling and reasoning, and support our proposers (listed above) as a named co-proposer.
Guido Governatori, email@example.com, NICTA, Queensland
Laboratory, Australia, and RuleML director:
As NICTA’s Primary Representative, I approve the LegalXML TC Charter and its worthwhile goals, and support our proposers (listed above) as a named co-proposer.
Joseph Wheeler, firstname.lastname@example.org, MTG Management Consultants LLC:
As MTG’s Primary Representative, I approve the LegalRuleML TC Charter and its worthwhile goals, and support our proposers (listed above) as a named co-proposer.
Mark Little, email@example.com, Red Hat Inc.:
As Red Hat’s Primary Representative, I approve the LegalRuleML TC Charter and its worthwhile goals, and support our proposers (listed above) as a named co-proposer.
Monica Palmirani, firstname.lastname@example.org, CIRSFID, University of Bologna, Italy
2.g. Member Section:
The TC intends to be affiliated with the LegalXML MS.
2.h Optional list of anticipated contributions:
The LegalRuleML TC intends to use as a foundation and input the draft RuleML specifications (http://ruleml.org/1.0/) provided by the RuleML Inc. initiative, as well as any subsequent input documents accepted by the LegalRuleML TC.