Download Discordance Detection in Regional Ordinance: Ontology

Discordance Detection in Regional Ordinance:
Ontology-based Verification
Shingo Hagiwara([email protected])
School of Information and Science
Japan Advanced Institute of Science and Technology
January 29, 2007
1 Aim of the Project
cal items which includes negative prefixes as ‘un-,’ ‘dis-,’
‘in-,’ and so on cannot coexist with their original positive words. Also, there are antonyms that have conflictive meanings without prefixes, as ‘liquid’ and ‘solid,’ or
‘vice’ and ‘virtue.’ Furthermore, some situations are incompatible with each other, which we can easily know by
our common sense. For example, ‘submission with signature’ is incompatible with ‘electric submission.’
Then, we would like to rely on the notion of conflict [1]
where the opposition of antonyms or negatively prefixed
words are represented.
In this paper, we propose a procedure of discordance detection in an actual legal code, that is the regional ordinance of Toyama Prefecture, Japan.
In this study, we expand the notion of inconsistency to
the discordance including antonyms based on an ontology,
and precluded the conventional negative connective. We
have implemented the system that converts XML logical
formats to Prolog, and has inspected the whole code.
2 Approach and Idea
Definition 2.1 Let be inconsistency, be propositional variables. . Then, and are in
conflict.
2.1 Discordance
The logical inconsistency becomes apparent only when
appear in a set of propositions. Howboth of and
ever, the inconsistency may not be seen from the superficial sentences of the legal code. To clarify such latent inconsistency, we need to supply some premises of
the rules ( ). Then, we can derive inconsistency as
. Therefore, we can regard such a part
as discordance. In addition, there might be a loop of implications. For example, in a database where is the logical truth, we cannot collect
the evidences of .
In verification of discordance, we use a definition of
inconsistency as follows.
However, if we were to define conflicts, we must enumerate all the possible combinations of predicates which
appear in a legal code, where the number of pairs would
be ¾ . To avoid this problem, we employ an ordersorted hierarchy of ontology.
2.3
Conceptual Conflict
Next, we consider a concept of conflict in order-sorted
logic.
First, we introduce (meet) operation that returns the
infimum (the greatest lower bound) with regard to ‘
’,
taking two sorts [2].
2.2 Conflict
Definition 2.2 exclusive relation
Let be sorts and be the minimum sort. Then,
The discrepancy or the discordance is not only the logical inconsistency. In the lexicon of legal code, such lexi-
1
Rules of the law
Ontology
XML
(FOL
and
OWL)
Converter
Knowledgebase
of the
law
Ordered sorts
Validation
Code for
Execution
Prolog
4
Validator
Conflict Result
Loop Result
We employed Gabbay’s conflict instead of the conventional negative connective. Thus, we could employ ordered sorted hierarchy in ontology to detect
incompatible notions.
We have implemented a discordance detection system based on the logical format of XML, where
those XML files were converted into Prolog, and the
verification program scans the code to detect discordance.
Future Direction
Our future target would be the handling of ‘’. We simply divided those rules including disjunctions to implement them in Horn clause. However, we need to consider
the computational efficiency. Also the input format of our
system is XML based on first order logic (FOL). Translating natural language sentences into FOL still remains a
tough problem.
Text data
Figure 1: Overview of Implementation
As stated above, the exclusive relation can express the
conflict on ordered sorts.
An ontology consists of tree-structured hypernymhyponym relations, together with extraneous knowledge
base. In this study, we regard an ontology as a ordered
sort. Therefore, we extract conceptual conflicts from an
ontology with the above definition.
5
2.4 Implementation
Shingo Hagiwara and Satoshi Tojo. Discordance detection in regional ordinance: Ontology-based validation. In
Legal Knowledge and Information Systems JURIX, Pariss,
2006. JURIX, IOS Press.
Shingo Hagiwara, Mikito Kobayashi, and Satoshi Tojo.
Belief updating by communication channel. In Seventh
Workshop on Computational Logic in Multi-Agent Systems (CLIMA-VII), 2006. (Revised Selected and Invited
Papers, volume 4371 of Lecture Notes in Computer Science. Springer, 2006).
we explain our implementation which consists of two programs. Its overview is Figure. 1.
In the figure, one of the programs is a converter, written in Ruby, and the role is conversion of XML files into
Prolog code. Another one is a validator, written in Prolog, and the role of which is validation of the code output
by the converter.
References
[1] Dov M. Gabbay and A. Hunter. Negation and contradiction. In Dov Gabbay and Heinrich Wansing, editors, What is Negation?, pages 89–100. Kluwer Publishers, 1999.
3 Progress of 2006
Publication in 2006(after April)
[2] K. Kaneiwa and S. Tojo. An order-sorted resolution with implicitly negative sorts. In International
Conference on Logic Programming, pages 300–314.
Cyprus, 2001.
We have targeted the real problem of ordinance revision held in Toyama prefecture in 2002, instead of
artificial toy problem.
2