Ontological representation of scientific laws

Ontological representation of scientific laws

Rafał Trójczak, Piotr Kulicki, Robert Trypuz

Faculty of PhilosophyThe John Paul II Catholic University of Lublin

6th International Conference on Non-Classical LogicsŁódź 2013

Trójczak, Kulicki, Trypuz (KUL) Science Ontology Łódź 2013 1 / 21

Ontologies within ProOptiBeef

Outline

1 Ontologies within ProOptiBeef

2 Representation of scientific laws

3 Gaining new knowledge

4 Conclusion and Future Perspective

5 Acknowledgment



ProOptiBeef

ProOptiBeef – Optimising beef production in Poland according tostrategy “from fork to farm”a

Project aim: to increase the level of innovation in Polish beef sectorthrough comprehensive research and development in the field of beefqualityWithin the scope of ProOptiBeef there are:

experimental taskstheoretical activities

ahttp://www.prooptibeef.pl


http://www.prooptibeef.pl


Tower of Babel

Variety of Expertsmarketing and consumerresearch, economics ofconsumption;

sensory analysis;

development of green areas;

cattle feeding and farming;

evaluation of material of animalorigin;

technology and chemistry ofmeat;



Ontology in ProOptiBeef

By “ontology” here we meanformal specification of domain knowledge shared by parties involved ininformation exchange.

Ontologies are intended tohelp in expressing the resultsof the project in anunambiguous way;

be used as a component of asystem for searchinginformation in a database ofscientific articles;

be used as a component inexpert system gathering theresults of the project andrevealing them to the public.



Ontology in ProOptiBeef

OntoBeef Library

Domain: knowledge about beef, its production and consumption.

Papers: metadata knowledge about documents and contains adescription of hundreds of scientific papers on domain.

Science: description of problems, methods, data and theses.


Representation of scientific laws

Outline





5 Acknowledgment



Definition of scientific law

Scientific law is a constant relationship between things, and more precisely,between the qualities possessed by the objects or between events in whichthe objects participatea.

aS. Krajewski, “Prawa nauki - przegląd zagadnień metodologicznych”, 1982



Taxonomy of scientific laws

Scientific Law

Methodological Law

Qualitative Law

Quantitative Law

Objective Law

CorrelationLaw

Functional Law

Monotonic Law

Law of InclusionOrdering

Law



DL Conventional Notation

u intersection of concepts C u D C and Dt union of concepts C t D C or D¬ complement of concept ¬C not C∀ universal restriction ∀R.C all R-successors are in C∃ existential restriction ∃R.C an R-successor exists in Cv concept inclusion C v D all C are D: concept assertion a : C a is a C: role/relation assertion (a, b) : R a is R-related to b

{. . . } concept creator {a, b} concept with elements a, b



Examples of different laws

Example (Methodologic law)

t1: Near infra red spectroscopy can be applied for prediction ofbeef tenderness.

t1 is an instant of methodological law class:

t1 : methodological law

Each NIR spectroscopy is method (which can be used) in t1:

NIR spectroscopy v ∃ isMethodIn {t1}Each concrete beef tenderness is subject (of study) in t1:

beef tenderness v ∃ isSubjectIn {t1}



Example (Correlation law)

t2: There exists a correlation between thermal shortening of meatunder thermal treatment and beef thermal loss.

t2 : correlation law

thermal shortening v ∃ isParameterIn {t2}

beef thermal loss v ∃ isParameterIn {t2}



Example (Functional law)

t3: Beef slaughtering season has influence on beef fat acids profile.

t3 : functional law

beef slaughtering season v ∃ isIndependentParameterIn {t3}

beef fat acids profile v ∃ isDependentParameterIn {t3}

Example (Monotonic law)

t4: Beef tenderness improves with longer aging time.

t4 : monotonic law

beef aging time v ∃ isIndependentParameterIn {t4}

beef tenderness v ∃ isDependentParameterIn {t4}

(t4, positive) : hasMonotonicTypeRel



Example (Inclusion law)

t5: Bovine serum albumin is the main beef allergen.

t5 : inclusive law

bovine serum albumin v ∃ isInstantOfSubclassIn {t5}

beef allergen v ∃ isInstantOfSuperclassIn {t5}



Example (Ordering law)

t6: Addition of antioxidant to ground beef improves its oxidativestability.

t6 : ordering law

ground beef v ∃ isContextIn {t6}

oxidative stability v ∃ isQualityIn {t6}

antioxidant v ∃ isDifferentiationFactorIn {t6}

(t6, positive) : hasMonotonicTypeRel


Gaining new knowledge

Outline





5 Acknowledgment




OWL-API-poweredapplication to deduce newscientific laws

Obtaining new knowledgefrom ontology:

transitivity offunctional laws;“reverse inheritance”













law 1

law 2

law 3

D

D

I

I

D

I

Functional Laws in Science Ontology

Q1

Q2

Q3







C1

C2 C3 law 2law 1

C1

C2 C3 law 2law 1

law 1 law 2

Before reasoning

After reasoning


Conclusion and Future Perspective

Outline





5 Acknowledgment




We presented:

an ontology of scientific laws;

its implementation in JAVA application;

simple reasoning methods for obtaining a new knowledge from theone explicitly written in the ontology.

Future Perspectives:

ontology – evaluation, verification and testing;

implementation new reasoning methods;

interface for inputting a new data to Science.


Acknowledgment

Outline





5 Acknowledgment


Acknowledgment

Acknowledgment

Research was realised within the Project no. WND-POIG.01.03.01-00-204/09Optimising of Beef Production in Poland According to “from Fork to Farm”Strategy co-financed by the European Regional Development Fund under theInnovative Economy Operational Programme 2007 – 2013.


Education

Ontological representation of scientific laws