=Paper=
{{Paper
|id=None
|storemode=property
|title=Combining Rules and Ontologies with Carneades
|pdfUrl=https://ceur-ws.org/Vol-799/paper12.pdf
|volume=Vol-799
|dblpUrl=https://dblp.org/rec/conf/ruleml/Gordon11
}}
==Combining Rules and Ontologies with Carneades==
https://ceur-ws.org/Vol-799/paper12.pdf
Combining Rules and Ontologies with Carneades
Thomas F. Gordon1
Fraunhofer FOKUS, Berlin
thomas.gordon@fokus.fraunhofer.de
Abstract. The Carneades software system provides support for con-
structing, evaluating and visualizing arguments, using formal representa-
tions of facts, concepts, defeasible rules and argumentation schemes. This
paper illustrates how rules and ontologies can be combined in Carneades
with a prototype legal application for analyzing open source software
license compatibility issues in particular cases.
2
Introduction
This paper illustrates how rules and ontologies can be used in combination in the
Carneades argumentation system [14] with examples from a prototype legal ap-
plication for analyzing open source license compatibility issues [15]. As Bing [3],
Fiedler [7], McCarty [17] and many others have noted, legal argumentation is not
primarily deductive, but rather a modeling process of shaping an understanding
of the facts, based on evidence, and an interpretation of the legal sources, to
construct a theory for some legal conclusion [3]. The parties in a legal dispute
construct competing theories and argue about their relative merits. Carneades
is designed to support all the steps in this process of theory construction, argu-
mentation and evaluation.1
The Carneades software system is based on a well-founded formal model of
structured argumentation with support for proof burdens and standards [9,10],
now called Carneades Argument Evaluation Structures (CAES). It has been
formally proven that the Carneades model of argument is a specialization of
both Prakken’s ASPIC+ model of structured argumentation [8] and Brewka’s
Abstract Dialectical Frameworks [5] and thus an instantiation of Dung’s Abstract
Argumentation Framework [4,8]. Carneades has also been shown by Governatori
to be closely related to Defeasible Logic [16]. A formal model of abduction in
Carneades argument evaluation structures has been developed [2], which is useful
for identifying relevant issues and computing minimal sets of statements, called
positions, which, if proven, would make some goal statement acceptable (in) or
not acceptable (out) in a stage of a dialogue.
Building on this formal foundation, the Carneades software provides a num-
ber of tools for interactively constructing, evaluating and visualizing arguments,
as well as computing positions. Arguments are constructed using formalizations
of facts, concepts, defeasible rules and argumentation schemes [11,12]. Facts and
concepts are represented using the Web Ontology Language (OWL), an XML
schema for description logic [1], a subset of first-order logic and thus with a
monotonic (strict) entailment relation. Legal rules and argumentation schemes
[18] are both modeled as defeasible inference rules, represented in the Legal
Knowledge Interchange Format (LKIF) [6]. The rules of alternative, competing
theories of the law can be included in a single model.
A combination of forwards and backwards reasoning is used to construct
arguments: a description logic reasoner constructs the deductive closure of the
concepts and facts in a forwards manner; the Carneades rules engine uses back-
wards reasoning to apply the defeasible inference rules in a goal-directed and
stratified way to the deductive closure of the description logic theory of facts
and concepts. The LKIF rule language has been extended to provide a way to
declare the domain of variables using predicates defined in OWL, similar to the
way variables are typed in programming languages. These domain declarations
provide important control information that enables the rule engine to iterate
over instances of the domains to more efficiently instantiate the rules.
1
http://carneades.github.com
3
In addition to the arguments constructed automatically from a knowledge-
base of facts, concepts and rules, arguments can manually entered into the system
by the user. These arguments can be completely ad hoc or instantiations of argu-
mentation schemes. The Carneades system currently includes a library of about
20 of Walton’s most important argumentation schemes along with a software
assistant which steps the user through the process of selecting and instantiating
schemes.
As the arguments are constructed and edited, they are visualized in an argu-
ment map [13]. The graphical user interface, called the Carneades Editor, sup-
ports argument evaluation by providing tools to accept and reject statements,
assign proof standards and weigh arguments. After every modification, the un-
derlying computational model of argument is used to update and visualize the
acceptability status of statements in the map. The di↵erential legal e↵ects of
competing theories can be analyzed by assuming their rules to be valid and then
checking how this e↵ects the acceptability of issues of interest in the argument
map. Moreover Carneades provides a find positions assistant which can be used
to abduce theories with desired legal e↵ects.
The rest of this paper show how ontologies and rules can be used in com-
bination in Carneades with examples from the prototype legal application for
analyzing open source license compatibility issues. We start with examples from
a simple OWL ontology for describing software licenses and use and derivation
relationships between works of software. Next we show how to use the ontology
to model the facts of a case. We then show how to model some rules of copy-
right law in LKIF, focusing on the issue of whether linking to a software library
produces a derivative work.
Concepts and Facts
Carneades uses the Web Ontology Language (OWL), a World Wide Web stan-
dard XML schema for representing and interchanging description logic knowl-
edge bases. These knowledge bases have two parts, for concepts (TBox) and
facts (ABox). The top-level concepts, called classes in OWL, for our application
are CopyrightLicense, CopyrightLicenseTemplate, LegalEntity, LicenseTerm and
Work. The Work class is for all works protectable by copyright. There is a Soft-
wareEntity subclass of Work, intended to cover all kinds of software artifacts.
The main property of software entities of interest for license compatibility
issues is the isDerivedFrom property, expressing that one entity has been derived
from another. The ontology includes properties for representing various ways
that software can use other software, such compiledBy and linksTo.
The software ontology was used to model an example software project, roughly
based on the current version of the Carneades system.
4
Rules
Description logic (DL) is semantically a decidable subset of first-order logic. This
means that the inferences of description logic reasoners are strict: if the axioms
of a DL knowledge base are true in some domain, then all of the inferences made
by a (correctly implemented) DL reasoner are necessarily also true, without ex-
ception. While DL is very powerful and useful, monotonic logics are not sufficient
for modeling legal rules, such as the rules of copyright law, in a maintainable and
verifiable way, isomorphic with the structure of legislation and regulations. Leg-
islation is typically organized as general rules subject to exceptions. Arguments
made by applying legal rules are defeasible. Their conclusions can be defeated
with better counterarguments. Various legal rules may conflict with each other.
Theses conflicts are resolved using legal principals about priority relationships
between rules, such as the principal of lex superior, which gives rules from a
higher authority, such as federal law, priority over rules from a lower authority,
such as state law. These properties of legal rules are well known in AI and Law
and have been studied extensively. References are omitted for lack of space.
Thus we model legal rules using a defeasible rule language which has been de-
veloped especially for this purpose, as part of the Legal Knowledge Interchange
Format (LKIF), and use description logic (OWL more specifically) for more lim-
ited purposes: 1) to declare the language of unary and binary predicate symbols
and 2) to make assertions about these predicates, using DL axioms, which are
judged to be universally true and beyond dispute in the domain.
Here we illustrate the LKIF rule language by modeling two interpretations of
the concept of a derivative work in copyright law. We begin with the general rule
that the copyright owner of software may license the software using any license
template he chooses.
SE may be licensed using
the T template
Since LKIF is an XML schema, rules are represented in XML. This particular
rule has a head (conclusion) but no body (conditions). Even though the rule has
no conditions, inferences made using this rule are not necessarily or universally
true, but remain defeasible. We will make use of this feature to express exceptions
to this general rule below.
The rule has been assigned an identifier, DefaultLicenseRule, which may be
used to formulate statements about the rule. That is, rules are reified and may
be reasoned about just like other objects.
The predicate symbol of the statement (proposition) in the head of the rule
is specified using the pred attribute. Its value can be the name of a class or
5
property in a OWL ontology, as in this example. The ? entity reference refers to
the ontology, using its URI.
Declaring predicate symbols in ontologies makes it possible to divide up the
model of a complex domain theory into modules, with a separate LKIF file for
each module. OWL provides a way to import the classes and properties of other
OWL files, recursively. Similarly, LKIF provides a way to import both LKIF
and OWL files. OWL makes it easy to manage predicate symbols across the
boundaries of modules and to make sure that symbols in di↵erent modules refer
to the same class or property when this is desired.
The XML syntax for rules in LKIF is rather verbose and not especially
readable. Fortunately, it is easy to write programs for converting XML into
more readable formats. Moreover, XML structure editors exist which use style
sheets to enable authors to edit XML documents directly in a more readable
form. Using this feature, the above rule can be displayed in the editor as follows:
rule DefaultLicenseRule
head SE may be licensed using the T template
We will use this more readable format for displaying LKIF rules in the remainder
of this article. Next let us formulate an exception to the general rule that any
license template may be used for reciprocal licenses:
rule ReciprocityRule
head
not: SE1 may be licensed using the T1 template
domains
SE1 uses SE2
SE2 has license L
body
L is reciprocal
SE1 is derived from SE2
unless exists T2 : L is an instance of template T2
such that T1 is compatible with T2
This reciprocity rule states that a software entity, SE1, may not be licensed
using a license template, T1, if the software is derived from another software
entity, SE2, licensed using a reciprocal license, L, unless L is an instance of a
template license, T2, which is compatible with T1. The use of domains in this rule
provides control information to make use of forward chaining in the description
logic reasoner, as discussed in the introduction. Notice that the conclusion of
the rule is negated and that the last condition of the rule expresses a further
exception, using an unless operator.
These two rules illustrate two kinds of exceptions. In argumentation terms,
arguments constructed using the ReciprocityRule rebut arguments constructed
using the DefaultRule and arguments which make use of the explicit exception
of the ReciprocityRule, by showing that the licenses are compatible, undercut
the reciprocity argument.
6
Let us end this brief overview with rules modeling two conflicting views about
whether or not linking creates a derivative work. According to the lawyers of the
Free Software Foundation, linking does create a derivate work. Lawrence Rosen,
a legal expert on open source licensing issues, takes the opposing point of view
and argues that linking per se is not sufficient to create derivate works.
rule FSFTheoryOfLinking
head
SE1 is derived from SE2
body
SE2 is a software library
SE1 is linked to SE2
The FSF theory of linking is valid
rule RosenTheoryOfLinking
head
not: SE1 is derived from SE2
body
SE2 is a software library
SE1 is linked to SE2
The Rosen theory of linking is valid
The last condition of each of these rules requires that the interpretation of copy-
right law represented by the rule is legally valid. Making this condition explicit
enables us to argue about which theory of linking is correct, to compare the
e↵ects of these two theories on particular cases, and to use abduction to derive
positions about which theory to prefer.
Conclusion
We have illustrated how ontologies and rules can be used together in the Carneades
argumentation system with a prototype legal application for analyzing software
licensing issues. To our knowledge, no other argumentation or rule-based system
currently provides the combination of tools required for this application: 1) au-
tomatic argument construction from a knowledge base of strict and defeasible
rules; 2) argument mapping; 3) argument evaluation; 4) interactive construction
of arguments using argumentation schemes; 5) exploration of e↵ects of alterna-
tive legal theories; and 6) computation of positions, using abduction. The source
code of the application is freely available, as open source software.
The current user interface is a desktop application, written in Java using the
Swing user interface library. Work is in progress on a web version of Carneades.
The user interface of this web version is a Rich Internet Application (RIA)
implemented using only World-Wide-Web Concortiums standards, in particular
XML and Javascript (AJAX). Argument graphs in the web version are rendered
using Scalable Vector Graphics (SVG), another W3C standard.
7
Acknowledgements
This work was partially funded by the European projects Qualipso (IST-FP6–
034763) and IMPACT (IST-FP7–247228). Earlier version of this paper were
presented at the Jurix 2010 workshop on Modeling Legal Cases and Legal Rules
and the Thirteenth International Conference on Artificial Intelligence and Law
(ICAIL 2011).
References
1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., and Patel-Schneider,
P., eds. The Description Logic Handbook — Theory, Implementation and
Applications. Cambridge University Press, 2003.
2. Ballnat, S. and Gordon, T.F. Goal Selection in Argumentation Processes
— A Formal Model of Abduction in Argument Evaluation Structures. Pro-
ceedings of the Third International Conference on Computational Models of
Argument (COMMA), IOS Press (2010), 51–62.
3. Bing, J. Uncertainty, Decisions and Information Systems. In C. Ciampi, ed.,
Artificial Intelligence and Legal Information Systems. North-Holland, 1982.
4. Brewka, G., Dunne, P.E., and Woltran, S. Relating the Semantics of Ab-
stract Dialectical Frameworks and Standard AFs. Proceedings of the Twenty-
Second International Joint Conference on Artificial Intelligence (IJCAI–
2011), (2011), in press.
5. Brewka, G. and Gordon, T.F. Carneades and Abstract Dialectical Frame-
works: A Reconstruction. Proceedings of the Third International Conference
on Computational Models of Argument (COMMA), IOS Press (2010), 3–12.
6. ESTRELLA Project. The Legal Knowledge Interchange Format (LKIF).
2008.
7. Fiedler, H. Expert Systems as a Tool for Drafting Legal Decisions. In A.A.
Martino and F.S. Natali, eds., Logica, Informatica, Diritto. Consiglio Nazionale
delle Richere, Florence, 1985, 265–274.
8. Gijzel, B.V. and Prakken, H. Relating Carneades with abstract argumenta-
tion. Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence (IJCAI–2011), (2011), in press.
9. Gordon, T.F., Prakken, H., and Walton, D. The Carneades Model of Argu-
ment and Burden of Proof. Artificial Intelligence 171, 10–11 (2007), 875–896.
10. Gordon, T.F. and Walton, D. Proof Burdens and Standards. In I. Rah-
wan and G. Simari, eds., Argumentation in Artificial Intelligence. Springer-
Verlag, Berlin, Germany, 2009, 239–260.
11. Gordon, T.F. and Walton, D. Legal Reasoning with Argumentation Schemes.
12th International Conference on Artificial Intelligence and Law (ICAIL
2009), ACM Press (2009), 137–146.
12. Gordon, T.F. Constructing Arguments with a Computational Model of an
Argumentation Scheme for Legal Rules – Interpreting Legal Rules as Rea-
soning Policies. Proceedings of the Eleventh International Conference on Ar-
tificial Intelligence and Law, (2007), 117–121.
8
13. Gordon, T.F. Visualizing Carneades Argument Graphs. Law, Probability and
Risk 6, 2007, 109–117.
14. Gordon, T.F. An Overview of the Carneades Argumentation Support Sys-
tem. In C.W. Tindale and C. Reed, eds., Dialectics, Dialogue and Argumen-
tation. An Examination of Douglas Walton[2BC?]s Theories of Reasoning.
College Publications, 2010, 145–156.
15. Gordon, T.F. Report on a Prototype Decision Support System for OSS Li-
cense Compatibility Issues. 2010.
16. Governatori, G. On the Relationship between Carneades and Defeasible
Logic. Proceedings of the International Conference on Artificial Intelligence
and Law (ICAIL–2011), (2011).
17. McCarty, L.T. Some Arguments About Legal Arguments. International Con-
ference on Artificial Intelligence and Law, (1997), 215–224.
18. Walton, D., Reed, C., and Macagno, F. Argumentation Schemes. Cambridge
University Press, 2008.