Ontology-based representation of scientific laws on beef production and consumption

Ontology-based representation of scientific lawson beef production and consumption

Piotr Kulicki1, Robert Trypuz1, Rafał Trójczak1, Jerzy Wierzbicki2, and AlicjaWoźniak2

1 The John Paul II Catholic University of Lublin, Faculty of PhilosophyAl. Racławickie 14,

20-950 Lublin, Poland{kulicki,trypuz}@kul.pl2 Polish Beef Associationul. Kruczkowskiego 3,

00-380 Warszawa, [email protected]

Abstract. We present an ontology called Science designed for repre-senting scientific laws and rejected hypotheses from scientific papers andexperimental results in the area of beef production and consumption.The ontology is designed on the basis of 1) classifications of scientificlaws presented in the works of Polish philosophers of science and nature:K. Ajdukiewicz and W. Krajewski and 2) the analysis of empirical ma-terial from the project database of articles. Science imports OntoBeefontology of the subject domain. Both ontologies, i.e. Science and Onto-Beef, are based on DOLCE. It is shown how to represent scientific lawsin the ontologies and how to generate new scientific laws from the oldones coded in the Science.

Keywords: ontology, scientific law, livestock, beef

1 Introduction

Each science researches some specific piece of reality. It commits to the existenceof categories of beings in its domain of interest and creates vocabulary enablingto express scientific laws concerning these beings. Ontologies are valuable toolsfor specifying the meaning of words in such vocabularies.

The most important of scientific activities are observations and experimentswhich are directed towards discovering and formulating scientific laws. Accordingto Krajewski [7] scientific law is a constant relationship between things (notclasses of things), and more precisely, between the qualities possessed by theobjects or between events in which the objects participate. We believe that anexplicit ontological representation of scientific laws can be useful for browsingthe content of scientific papers and for discovering new knowledge.

In this paper, our domain of interest is beef production and consumption,being, in fact, an overlap of many sciences, such as cattle feeding and farming,

2 Kulicki, Trypuz, Trójczak, Wierzbicki and Woźniak

sensory analysis, evaluation of material of animal origin, chemical research, tech-nology and chemistry of meat, marketing and consumer research, economics ofconsumption and development of green areas. Since 2011 we have been exploringthe domain taking part in an interdisciplinary project ProOptiBeef3. Within theproject, an OWL domain ontology called OntoBeef was created.

This paper describes an ontology-based representation of scientific laws withinthe domain of beef production and consumption by using OntoBeef categories.

It is already known that in food and agricultural science ontology serves notonly as lingua franca for researchers in the area but it is also used as a powerfultool for scientific information especially for efficient and user friendly searchsupport [13,8,12] and expert systems [9]. In the research presented in this paperwe are aiming at an ontology based expert system in which knowledge fromliterature is expressed in terms of ontology to facilitate search and to enablegaining new knowledge.

2 Previous works

OntoBeef and its applications have been presented in [8,12]. It is an OWL ontol-ogy consisting currently of 2349 classes, 96 object properties and 12892 axioms4.It is more structured than well known agricultural thesauri such as AGROVOC[2]. Compared to other agricultural ontologies such as ATO, VT, ATOL [6,11,4,5]it focuses on concepts related to beef, but in the same time covers a wider spec-trum of phenomena.

OntoBeef is based on an OWL version of DOLCE [10] as a foundationalontology. Let us mention here four top classes of DOLCE important for this paper.DOLCE distinguishes: 1) endurants (i.e. entities ‘wholly’ present at any timeof their existence”, e.g., Bovinae), 2) perdurants (‘i.e. ‘entities that ‘happen intime’, e.g., Feeding), 3) qualities (i.e. entities that can be perceived or measured,e.g., Tenderness) and 4) regions (i.e. quality spaces providing values used formeasuring qualities, e.g., pH).

So far OntoBeef has been used as the basis for two applications. The firstone is a Java desktop application named Oxpecker (see [8, section V]). It fa-cilitates the access to the database of descriptions of scientific articles, whichwere collected in the project. The database contains descriptions of around 2800articles. It collects information about the structure of papers (title, author(s),abstract, etc.) and some content data such as definitions of terms, scientific lawsand rejected hypotheses. Oxpecker uses the annotation of articles by IRIs ofOntoBeef classes to find articles which best match the user’s query. The otherOntoBeef -driven application is a web application which at the beginning servedas a thesaurus5. It enabled us to search for categories and had a Linked OpenData connections to four external ontologies. Recently the application got somefunctionalities of Oxpecker and for each OntoBeef class it provides the scientific3 http://www.prooptibeef.pl/4 http://onto.beef.org.pl/ontobeef/owl/Domain.owl5 http://onto.beef.org.pl/ontobeef/

Ontology-based representation of scientific laws 3

laws which govern its instances. The last of these functionalities is the mainsubject of this paper and is extensively described below.

3 Science ontology

Most of the papers, collected in DB of articles created in ProOptiBeef, haveproved and disproved statements attached. The statements were extracted fromthe articles by a group of experts who analyzed the papers within ProOptiBeefproject. OntoBeef was applied to represent them. For this purpose we havecreated a new ontology, we called it Science6, which imports OntoBeef and usesits classes to express scientific laws.

Currently Science provides the taxonomy of scientific laws and its ontolog-ical characterization. It does not contain many aspects important for completecharacterization of scientific laws; for instance representation of methods, obser-vations, material samples and experimental results is missing in it. We plan todevelop richer ontology of science in future works.

The types of scientific laws, we adopted in the project, were taken from theworks of Polish philosophers of science and nature: Ajdukiewicz and Krajewski[1,7]. They represent standard, classical views on science which we find appro-priate for our task as confluent with the usual practice in the kind of science wedeal with. The distinctions proposed by them were analyzed in terms of theiruse in ontological modeling and reasoning. Then we have chosen the types oflaws which are present in our domain of interest and specified their meaning.Some types of laws introduced in [7] (like, for instance, structural laws7) havenot been taken into account. We would like to note that our specification ofmeaning of ‘ordering law’ is slightly different from the original one presented in[1,7]—the difference we shall explain while describing this type of law bellow. Wealso provided some additional distinctions for example between methodologicaland objective laws.

Figure 1 depicts the final taxonomy of scientific laws. Below we shall presentsome examples of laws chosen from our Science ontology (so far we have coded56 scientific laws). For each type of laws we shall present its natural languagedescription (as given by domain experts) and its ontological formalization inRDF or Description Logic.

Let us start with the top distinction between methodological and objectivelaws. Methodological (or Meta-) laws concern primarily scientific methods whichcan be used to obtain some knowledge about qualities of things. Qualities ofthings are concerned secondarily here. An example of meta-law is:

Example 1 (Meta-law).

t1: Near infra red spectroscopy can be applied for prediction of beef tenderness.6 http://onto.beef.org.pl/ontobeef/owl/Science.owl7 Structural ordering laws state that there is an order of elements in space or in time,e.g. “if a plant has a root and a shoot, then the first one is lower than the second”[7].


Scientific Law

Methodological Law

Qualitative Law

Quantitative Law

Objective Law

CorrelationLaw

Functional Law

Monotonic Law

Law of InclusionOrdering

Law

Fig. 1. Taxonomy of scientific laws

We represent it in Science as below. Let us note that science and domain areprefixes in the RFD models of Science and OntoBeef, respectively.

<owl:NamedIndividual rdf:about="&science;t1"><rdf:type rdf:resource="&science;Meta_Law"/><skos:prefLabel xml:lang="en">t1</skos:prefLab><thesisContent xml:lang="en">Near infra red spectroscopy can be applied for prediction of beef tenderness.

</thesisContent></owl:NamedIndividual>

<owl:Class rdf:about="&domain;2072"><skos:prefLabel xml:lang="en">NIR spectroscopy</skos:prefLab><rdfs:subClassOf><owl:Restriction><owl:onProperty rdf:resource="&science;isMethodIn"/><owl:hasValue rdf:resource="&science;t1"/>

</owl:Restriction></rdfs:subClassOf>

</owl:Class>

<owl:Class rdf:about="&domain;2213"><skos:prefLabel xml:lang="en">beef tenderness</skos:prefLab><rdfs:subClassOf><owl:Restriction><owl:onProperty rdf:resource="&science;isSubjectIn"/><owl:hasValue rdf:resource="&science;t1"/>

</owl:Restriction></rdfs:subClassOf>

</owl:Class>


The same information represented above in RDF model, for readability rea-son, we shall present in shorter DL-like form (the translation has been performedautomatically by the Protégé editor) as follows:

– 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}

Note that owl:Restriction part of RDF model containing value restrictionowl:hasValue is now expressed by formula ‘∃ Role {t1}’, where Role standsfor some object property, with singleton ‘{t1}’. Henceforward, where it will beself-explanatory, we shall prefer the shorter way of expressing the ontologicalstatements.

Another thing we would like to draw the reader’s attention is the way we treatscientific laws. Following Krajewski we believe that scientific laws say somethingprimarily about concrete objects (individuals) rather then classes or notions.That is why, while modeling meta-law t1 (and other laws we analyze as thefollowing examples), we stated that ‘each concrete beef tenderness is subject (ofstudy) in t1’, instead of possible reified, skos-like approach, in which we couldhave write that ‘beef tenderness (as a reified class or a notion) is subject (ofstudy) in t1’.

In spite of the philosophical justification we also have technical reasons toprefer that way of modeling over the reifying classes. Now we can use the On-toBeef ontology, which contains multiple relation between its notions and canbe used for other purposes, directly in the representation of scientific laws. Itmakes it possible, among others, to include the presentation of scientific laws inour thesaurus web application in a direct manner.

Objective laws concern primarily objects, not research methodologies. Theyare divided into qualitative and quantitative laws. Qualitative laws concern pri-marily endurants or perdurants, while quantitative ones concern primarily de-pendencies between qualities possessed by endurants or perdurants. More pre-cisely, qualitative laws focus on values the qualities have in some quality spaces.Quantitative laws are then divided into correlation and functional laws.

Correlation law states a correlation between values of qualities, but it isunknown how they are related, i.e. how they influence each other. For this typeof law we just state that two (or more) qualities are parameters in it (see examplebelow).

Example 2 (Correlation law).


t2: There exists a correlation between thermal shortening of meet under thermaltreatment and beef thermal loss.

Its modeling in Science is:

– t2 : correlation law– thermal shortening v ∃ isParameterIn {t2}– beef thermal loss v ∃ isParameterIn {t2}

Functional laws state that a value of one (dependent) quality C1 is a functionof a value of the other (independent) quality C2 (see example below).

Example 3 (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}

If the function is known to be monotonic, function law is a monotonic law.Type of monotonicity is expressed by hasMonotonicTypeRel object property. Itranges a class with two values, namely: positive and negative. Let us consideran example below.

Example 4 (Monotonic law).

t4: Beef tenderness improves with longer aging time.

Knowledge that t4 is an instance of a class of monotonic law and has positivemonotonic type we express in RDF model as follows:<owl:NamedIndividual rdf:about="&science;t4">

<rdf:type rdf:resource="&science;Monotonic_Law"/><hasMonotonicTypeRel rdf:resource="&science;positive"/>

</owl:NamedIndividual>

Then we express the part of characterization a monotonic law inherits fromthe functional one:

– beef aging time v ∃ isIndependentParameterIn {t4}– beef tenderness v ∃ isDependentParameterIn {t4}

Now let us with to qualitative laws. Among them there are laws of inclusionand ordering laws. Laws of inclusion establishes a subsumption relation betweenclasses of endurants or perdurants.

Example 5 (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}

One may wonder why we use object properties: ‘isInstantOfSubclassIn’ and‘isInstantOfSuperclassIn ’ instead of simply connect classes by ‘rdfs:subClassOf’.Our intention behind this ontological choice was to separate a taxonomy of ourontology, which is established after meaning negotiation with our domain expertsfrom a taxonomical knowledge coming from scientific laws. After the experts ac-cept the inclusion law, it becomes a part of ontology and two classes beforebound to it are connected by rdfs:subClassOf.

Ordering laws establish an order within a class C of endurants or perdurants.The class is divided into two subclasses C1 and C2 by a given differentiationfactor F – C1 is a class of objects C without F and C2 is a class of object Cwith F . It is worth noting that in our modeling of ordering laws, classes C1 andC2 are not explicitly mentioned. Then it is stated that all objects from classC1 have greater (or smaller) values of quality Q then all elements of C2. Order-ing laws, like monotonic laws, have positive or negative monotonicity. Orderinglaws, unlike monotonic laws, do not use number scale to measure quality. Let usconsider the following law:

Example 6 (Ordering law).

t6: Addition of antioxidant to ground beef improves its oxidative stability.

We formalize this example as follows:

– t6 : ordering law– ground beef v ∃ isContextIn {t6}– oxidative stability v ∃ isQualityIn {t6}– antioxidant v ∃ isDifferentiationFactorIn {t6}– hasMonotonicTypeRel(t6, positive)

The modeling should be understood as follows: all instances of ‘ground beef withaddition of antioxidant’ have greater values of quality ‘oxidative stability’ thenall instances of ‘ground beef without antioxidant’. In short: adding antioxidantto ground beef has a positive impact to its oxidative stability.

We have mentioned above that our understanding an ‘ordering law’ differsfrom the original one presented in [1,7]. In our specification of ordering laws, theyestablish an order between instances of one class of objects with and withoutdifferentiation factor. In general ordering laws may establish an order betweenobject of different kind (e.g. we can compare hardness of glass and diamond).

4 Gaining new knowledge

We use OWL-API application to deduce new scientific laws on the basis of onesalready coded in Science ontology. Since we present ongoing research, we shall


law 1

law 2

law 3

D

D

I

I

D

I

Functional Laws in Science Ontology

Q1

Q2

Q3

Fig. 2. Application finds a new function law 3 taking into account two already existingin the ontology laws 1 and 2. Dashed arrow with “D” (“I”) is a relation “treats as(in)dependent quality”.

point out only two of possibly many ways of obtaining new knowledge from ourontology.

The first type of reasoning is illustrated in figure 2. It concerns functionallaws. Having two laws in the ontology, e.g. law 1 treating Q1 as dependent qualityand Q2 as independent one, and having law 2 treating Q2 as dependent qualityand Q3 as independent one, the reasoning system generates a new scientific law(law 3) treating Q1 as dependent quality and Q3 as independent one.

Example 7 (New laws). From two theses present in Science:

– rate of glycolysis v ∃ isIndependentParameterIn {t7}– beef aging time v ∃ isDependentParameterIn {t7}

and

– beef aging time v ∃ isIndependentParameterIn {t4}– beef tenderness v ∃ isDependentParameterIn {t4}

our application infers a law:

– rate of glycolysis v ∃ isIndependentParameterIn {t-new}– beef tenderness v ∃ isDependentParameterIn {t-new}

New laws found by the application are intended to be verified by the domainexperts and if accepted, will be added to the list of laws.

The second type of reasoning is a ‘reverse inheritance’ of scientific laws. It isillustrated in figure 3. Let us consider class C1 with two children C2 and C3 and


assume that C1 is not governed by any scientific law in our ontology, whereasC2 and C3 are governed by scientific laws: 1 and 2 respectively. In this situationour system signalizes that although there is no law governing all instances ofC1, there are laws which take into account some of them. System points outthe subclass of C1 and provides information about the laws governing them. Forinstance, “sensory attribute” has no law in our prototype, whereas its subclass“beef tenderness” or “beef color” are governed by laws. While browsing “sensoryattribute” system informs about the laws of its subclasses.

C1

C2 C3 law 2law 1

C1

C2 C3 law 2law 1

law 1 law 2

Before reasoning

After reasoning

Fig. 3. Application signalizes existence of laws considering subclasses of given class.

5 Conclusions and further work

We presented an ontology of scientific laws appropriate for agricultural and foodscience and the way in which the results of research from scientific papers can berepresented in it. Ontological representation enables efficient and precise searchwithin the set of represented scientific laws. It also leads to new knowledgeobtained by connecting laws from articles and ontology.

Further works include ontological connection of scientific laws with the databaseof scientific papers and with a repository of experimental results, which is devel-oped within ProOptiBeef project. Another direction is to study mutual supportand conflicts between theses expressed in the papers from the database. We planto apply there the ontology of beliefs presented in [3].

We are also planning to enhanced Science ontology by representation of meth-ods, observations, material samples and experimental results.


Acknowledgments. Research was realized within the Project no. WND-POIG.01.03.01-00-204/09 Optimizing of Beef Production in Poland According to “fromFork to Farm” Strategy co-financed by the European Regional DevelopmentFund under the Innovative Economy Operational Programme 2007 – 2013.

We would like to also thank Paweł Garbacz for his help in creation of scienceontology and the anonymous referees for useful comments on the earlier versionof the paper.

References

1. Kazimierz Ajdukiewicz. Pragmatic Logic. Dordrecht, Reidel, 1974.2. Caterina Caracciolo, Armando Stellato, Ahsan Morshed, Gudrun Johannsen, Sa-

chit Rajbhandari, Yves Jaques, and Johannes Keizer. The agrovoc linked dataset.Semantic Web, 4(3):341–348, 01 2013.

3. Pawel Garbacz, Marek Lechniak, Piotr Kulicki, and Robert Trypuz. Do you stillwant to vote for your favorite politician? Ask Ontobella! In FOMI, pages 102–113,2009.

4. W. Golik, O. Dameron, J. Bugeon, A. Fatet, I. Hue, C. Hurtaud, M. Reichstadt, M.-C. Salaün, J. Vernet, L. Joret, F. Papazian, C. Nédellec, and P.-Y. Le Bail. ATOL:The Multi-species Livestock Trait Ontology. In J.M. Dodero, M. Palomo-Duarte,and P. Karampiperis, editors, Metadata and Semantics Research, volume 343 ofCommunications in Computer and Information Science, pages 289–300. SpringerBerlin Heidelberg, 2012.

5. J.F. Hocquette, C. Capel, V. David, D. Guéméné, J. Bidanel, C. Ponsart, P.L.Gastinel, P.Y. Le Bail, P. Monget, P. Mormede, M. Barbezant, F. Guillou, and J.L.Peyraud. Objectives and applications of phenotyping network setup for livestock.Animal Science Journal, 83(7):517–528, 2012.

6. L. M. Hughes, J. Bao, Z.-L. Hu, V. Honavar, and J. M. Reecy. Animal traitontology: The importance and usefulness of a unified trait vocabulary for animalspecies. Journal of Animal Science, 86(6):1485–1491, 2008.

7. Władysław Krajewski. Prawa nauki - przegląd zagadnień metodologicznych.Książka i Wiedza, 1982.

8. Piotr Kulicki, Robert Trypuz, and Jerzy Wierzbicki. Towards beef productionand consumption ontology and its application. In Proceedings of FedCSIS, pages483—488, 2012.

9. Michael T. Maliappis. Applying an agricultural ontology to web-based applications.International Journal of Metadata, Semantics and Ontologies, 4:133–140, 2009.

10. C. Masolo, S. Borgo, A. Gangemini, N. Guarino, A. Oltramari, and L. Schneider.The WonderWeb Library of Fundational Ontologies and the DOLCE ontology.WonderWeb Deliverable D18, Final Report (vr. 1.0. 31-12-2003). Technical report,ISTC-CNR, 2003.

11. Carissa A Park, Susan M Bello, Cynthia L Smith, Zhi-Liang Hu, Diane H Mun-zenmaier, Rajni Nigam, Jennifer R Smith, Mary Shimoyama, Janan T Eppig, andJames M Reecy. The vertebrate trait ontology: a controlled vocabulary for theannotation of trait data across species. Journal of Biomedical Semantics, 4(13),2013.

12. Rafał Trójczak, Robert Trypuz, Przemyslaw Gradzki, Jerzy Wierzbicki, and AlicjaWozniak. Evaluation of beef production and consumption ontology and presenta-tion of its actual and potential applications. In Proceedings of FedCSIS, 2013.


13. Katia Vila and Antonio Ferrandez. Developing an ontology for improving questionanswering in the agricultural domain. In M.A. Sicilia F. Sartori and N. Manouselis,editors, MTSR 2009, CCIS 46, pages 245–256. Springer, 2009.

Ontology-based representation of scientific laws on beef production and consumption

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