a framework for evaluating agricultural ontologieslast updated: 25th may 2019 a framework for...

Last updated: 25th May 2019

A Framework for Evaluating Agricultural Ontologies

A. Goldstein 1*, L. Fink2 and G. Ravid2

1 Tel-Aviv University, Department of Technology and Information Management, Israel

2 Ben Gurion University of the Negev, Department of Industrial Engineering and Management, Israel

Abstract

An ontology is a formal representation of domain knowledge, which can be interpreted by

machines. In recent years, ontologies have become a major tool for domain knowledge

representation and a core component of many knowledge management systems, decision support

systems and other intelligent systems, inter alia, in the context of agriculture.

A review of the existing literature on agricultural ontologies, however, reveals that most of the

studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This

is undesired because without well-structured evaluation processes, it is difficult to consider the

value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies

and share them on the Semantic Web or between semantic aware applications. With the growing

number of ontology-based agricultural systems and the increasing popularity of the Semantic Web,

it becomes essential that such development and evaluation methods are put forward to guide future

efforts of ontology development.

Our work contributes to the literature on agricultural ontologies, by presenting a method for

evaluating agricultural ontologies, which seems to be missing from most existing studies on

agricultural ontologies. The framework supports the matching of appropriate evaluation methods

for a given ontology based on the ontology’s purpose.

Keywords

Agricultural ontology, Pest Control, OWL, Ontology evaluation, decision support


Introduction

An ontology is a formal representation of domain knowledge, which can be interpreted by

machines (Chandrasekaran et al. 1999). In other words, ontologies formally define the entities

(concepts) of a domain, their attributes and the relationships among them, in a machine

interpretable way. Thus, ontologies have become a major tool for domain knowledge

representation and a core component of many knowledge management systems, decision

support systems and other intelligent systems (Bose and Sugumarat 2007; Bose and Sugumarat

2007; Chandrasekaran et al. 1999; Delir Haghighi et al. 2013; Yu et al. 2005; Noy and

McGuinness 2001).

Ontologies can be used for several purposes: First, an ontology, being a formal explicit

description of domain knowledge, can be used by machines for knowledge deduction (W3C

2006). For example, if a data set specifies that a certain chemical “x” is effective against a

certain pest “y” and that a certain pesticide “z” contains that chemical “x”, then a machine can

infer that the pesticide “z” is effective against this pest “y”. Such inference can be made if an

ontology declares that a pesticide is effective against a pest if a chemical it contains is effective

against the pest. This makes ontologies suitable for serving as the underlying knowledge base

of decision support systems (DSS) and expert systems.

Second, ontologies enable sharing conceptual schemata of data, allowing application programs

and databases to interoperate without having to share data structures (Gruber 1993; W3C

2006). As a result, if two application programs or webpages share an ontology then it is possible

to automatically extract and aggregate information from these applications and webpages. In

addition, it is possible to translate concepts of one application to concepts representing the same

thing in the other application.

Third, ontologies enable reuse of domain knowledge (Noy and McGuinness 2001; Roussey et

al. 2010). Once an ontology is published it can be used by various applications in various


domains. Furthermore, several ontologies describing different parts of a domain can be

integrated to create one large ontology of the domain (Noy and McGuinness 2001).

A pivotal use of ontologies is for the creation of the so called Semantic Web. The Semantic

Web (also known as Web of Data or Web of Linked Data) is an extension of the Web through

standards by the World Wide Web Consortium (W3C) with the aim of providing a formal

representation of the information on the World Wide Web, to facilitate sharing and reusing of

data on the web across applications (Berners-Lee et al. 2001; W3C 2006).

Currently, most of the data on the web reside in HTML documents that can be read by humans

and by machines. While humans are capable of understanding the meaning of these documents,

machines cannot extract meaning from these documents other than searching them for

particular keywords. The Semantic Web initiative is aimed at changing this situation and

making data in Web documents understandable for machines – by using ontologies. Ontologies

provide a formal language that specifies how data on the web is related to objects (i.e. instances

of classes) in the real world. In addition, since these ontologies are described in standard

formats it is possible to integrate and combine data from diverse sources on the Web, thereby

making the Web one huge database.

To support the Semantic Web initiative, the W3C has developed a set of standards and tools.

For example:

The Resource Description Framework (RDF) – a formal language for specifying

relations between web resources that represent objects in the real world using triples

in the form of subject-predicate-object.

The RDF Schema (RDFS) – an extension of RDF with additional classes that allow

the specification of the ontology’s schema or data-model, thereby adding semantics

to ontologies.

The Web Ontology language (OWL) – an extension of RDF, which allows the


specification of ontologies with higher level of semantics that include operations on

classes (e.g. union and intersection) and is able to express description logic

predicates, which can be used for inference and assertions. OWL also allows binding

a concept of one ontology with a similar concept of another ontology (using

“owl:sameAs” property), thereby making additional concepts of the other ontology

available for use, even though defined in a different ontology.

The Simple Knowledge Organization System (SKOS) – a standard which is built

upon RDF and RDFS and is used for the specification and publication of

vocabularies on the Semantic Web.

SPARQL – a standardized query language, which enables querying decentralized

collections of RDF data that are stored in one or more triplestores. A triplestore is a

special database for the storage and retrieval of RDF triples.

The increasing use of ontologies is also seen in agriculture (e.g.Beck et al. 2010; Chang et al.

2008; Kragt et al. 2016; Li et al. 2013; Liao et al. 2015; Roussey et al. 2010; Song et al. 2012;

Tomic et al. 2015; Zhang et al. 2002), where they are used for various purposes, such as

agriculture knowledge sharing across farmers around the world (and in different languages)

(AGROVOC Thesaurus; Chang et al. 2008; Maliappis 2009), creating semantic

interoperability of agricultural systems (Aqeel-ur and Zubair 2011; Goumopoulos et al. 2009;

Tomic et al. 2015), and supporting farmer decisions (Gaire et al. 2013). This is not surprising,

given that agriculture is a knowledge-centric field that covers many areas of expertise, many

world-wide used practices and technologies, and includes numerous concepts that are often

designated by different names with similar meaning (Liao et al. 2015; Palavitsinis and

Manouselis 2014) and fragmented across different systems (Janssen et al. 2017).

A review of the existing literature on agricultural ontologies, however, reveals that most of the

studies, which propose agricultural ontologies, are lacking a clear ontology construction


method and, more importantly, explicit evaluation procedures. This is undesired because

without well-structured development and evaluation processes, it is difficult to consider the

value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies

and share them on the Semantic Web or between semantic aware applications. Error!

Reference source not found. summarizes for each of the surveyed agricultural ontologies,

their goal and whether their construction and evaluation processes are available.

With the growing number of ontology-based agricultural systems and the increasing popularity

of the Semantic Web, it becomes essential that such development and evaluation methods are

put forward to guide future efforts of ontology development.

Table 1. A summary of agricultural ontologies, their goals, and whether their construction

and evaluation processes are described

Goal

Study

Share

vocabularies,

integrate data

Knowledge

search and

exploration

System

interoperability

Decision

support

Construction

method

described

Evaluation

method

described

(Roussey et al.

2010)

- -

(Chang et al.

2008)

- -

(Goumopoulos

et al. 2009)

+ +

(Aqeel-ur and

Zubair 2011)

- -

(Gaire et al.

2013)

- -

(Tomic et al.

2015)

+ -

(Li et al. 2013) + -

(Song et al.

2012).

- -


(Liao et al.

2015)

Partial +

(Beck et al.

2005)

+ -

(Maliappis

2009)

Partial -

Our work extends the existing literature on agricultural ontologies, by presenting a

comprehensive method for evaluating agricultural ontologies. The method is demonstrated on

the case of the pest-control ontology.

The rest of the paper is organized as follows: next, in the Materials and methods section, we

present a review of ontology evaluation methods, as well as survey the purposes for which

ontologies are used in the context of agriculture. In the Results section, we present the

framework for matching evaluation methods based on the ontology purpose. We demonstrate

how the framework should be applied in a case study of pest-control ontology. Finally, we

discuss the resulting framework and provide conclusion.

Materials and methods

The development of the evaluation framework for agricultural ontologies included three steps:

First, literature on existing ontology evaluation approaches (not necessarily in the context of

agriculture) are surveyed.

Second, given the goals / uses of ontologies in the context of agriculture, which were identified

in the Introduction section, we define a framework that matches evaluation approaches to

different ontology purposes.

Third, we demonstrate the application of the framework, using a case-study of a pest-control

ontology, which has been developed for integrating knowledge from different websites as well

as concepts from other ontologies in order to provide a pest-control knowledge base, and for


supporting pest-control decisions of farmers, as it serves as the knowledge base of a pest-

control decision support application.

A Review of Ontology Evaluation Methods

Various ontology evaluation methods have been proposed in the literature. These methods are

commonly classified into the following types (Brank et al. 2005; Yu et al. 2007):

Evaluation against a gold standard - This method compares an ontology with common

standards or with another ontology that is considered as a benchmark. Such a method

is typically used in cases where the ontology was automatically or semi-automatically

generated. In many cases the application of this method is impossible since such a gold

standard does not exists.

Application-based evaluation - The application based evaluation (or task-based

evaluation Yu et al. 2007) uses the ontology for completing tasks within an application

and measure its effectiveness. Since comparing several optional ontologies in the

context of a given application environment is usually not feasible, often the proposed

ontology is evaluated in a quantitative or qualitative manner by measuring its suitability

for performing tasks within the application. The advantage of this method is that it

allows assessing how well the ontology fulfills its objectives. Nevertheless, the

evaluation is only relevant for that particular application. If the ontology is to be used

in another application the evaluation is irrelevant.

Criteria-based evaluation - This method evaluates the ontology against a set of

predefined criteria. Depending on the criteria, the evaluation is conducted either

automatically (Yu et al. 2007) or manually, usually by experts (Delir Haghighi et al.

2013). Various criteria have been used in the literature. Gruber (1995) defines five

criteria: Clarity – the ontology should effectively and objectively communicate the

definitions of terms; Coherence – the ontology should support inferences that are


consistent with the definitions and have no contradictions; Extendibility – it should be

possible to extend the ontology to support possible uses of the shared vocabulary,

without altering the ontology; Minimal encoding bias – the conceptualization should be

independent of the particular encoding that is used as possible; and Minimal ontological

commitment – the ontology should define as few restrictions on the domain of discourse

as possible.

Gómez-Pérez (1996) also defines five criteria that are partially overlapping to Gruber’s

criteria: Consistency and Expendability, which are similar to Gruber’s coherence and

extendibility respectively; Conciseness – definitions should be clear and unambiguous

and yet expressed in few words; Completeness – the ontology captures all that is known

about the real world in a finite structure; and Sensitiveness – how sensitive the ontology

is to small changes in a given definition.

Some criteria (e.g. expendability, clarity and completeness) are difficult to evaluate and

require manual (and subjective) inspection by domain experts or ontology engineers

(Yu et al. 2007). Other criteria can be measured quantitatively by various measures.

For example, consistency can be measured based on the number of circularity errors,

partition errors and semantic inconsistency errors, and conciseness can be measured

based on the number of redundancy error, grammatical redundancy errors and number

of identical formal definition of classes (Gómez-Pérez 2001).

Selection of appropriate criteria and measures for the evaluation an ontology depends

on the ontology requirements and goals, as demonstrated by Yu et al. (2007).

Data driven evaluation – In this evaluation method the ontology is compared with

relevant sources of data (e.g. documents, dictionaries, etc.) about the domain of

discourse. For example, Brewster et al. (2004) uses such a method to determine the

degree of structural fit between an ontology and a corpus of documents.


To the above classification of methods, Brank et al. (2005) add another dimension of

classification – one that is based on the level of evaluation. They argue that an ontology could

be evaluated on six different levels, representing different ontology aspects, as follows. First,

the lexical, vocabulary, or data level – in this level the concepts, facts or instances that are

included in the ontology are evaluated, usually by comparing them with different domain-

related data-sources, as well as string similarities techniques (e.g. in Maedche and Staab 2002).

Second, the hierarchy or taxonomy level – in this level we evaluate whether an appropriate

hierarchy of ‘is-a’ relations between concepts is defined (e.g. Yu et al. 2007). Third, other

semantic relations – in this level, semantic relations, besides ‘is-a’, are evaluated. Forth, the

context level – an ontology may be part of a larger collection of ontologies and may reference

or be referenced by definitions in these other ontologies. In this level we evaluate these inter-

ontology references. The context of an ontology could also be an application that uses the

ontology. If this is the case, we evaluate how effective the ontology is with respect to the

achieving the application goals. Fifth, the syntactic level – here we evaluate whether the

ontology is correctly specified and follows the syntactic requirements of the selected formal

language. This level is relevant when the ontology is created manually. Many of the existing

ontology editors (e.g. Protégé) include automatic mechanisms for syntactic evaluation. Finally,

the structure, architecture and design level – in this level we evaluate whether the ontology

satisfies pre-defined design principles or criteria, structural concerns and whether it is suitable

for further development.

Brank et al. (2005) map between the four evaluation methods (i.e., gold standard,

application-based, criteria-based and data-driven) and the above levels. For example, while all

methods are suitable for evaluating the ‘lexical, vocabulary, or data’ level, the ‘hierarchy or

taxonomy’ level, and the ‘other semantic relations’ level, only the application-based and

criteria-based methods are suitable for evaluating the ‘context’ level. In addition, only the


golden-standard and criteria-based methods are suitable for evaluating the ‘syntactic’ level, and

only the criteria-based method is suitable for evaluating the ‘structure, architecture and design’

level.

Ontology Purposes in Agriculture

The selection of appropriate evaluation methods for a given ontology should account for the

ontology’s purpose. Our literature review, reveals that in the context of agriculture, ontologies

are used for four main purposes: as means to share vocabularies and integrate data (e.g. Chang

et al. 2008), for knowledge search and exploration (e.g. Beck et al. 2005; Chang et al. 2008;

Li et al. 2013; Palavitsinis and Manouselis 2014; Song et al. 2012), for system interoperability

(e.g. Aqeel-ur and Zubair 2011; Liao et al. 2015; Tomic et al. 2015), and for decision support

and for automating decisions (e.g. Bournaris and Papathanasiou 2012; Gaire et al. 2013). The

evaluation of ontologies with these different purposes requires focusing on different ontology

aspects, i.e., calls for evaluation of different ontology levels (Brank et al. 2005), and thus

requires using different evaluation methods.

Results

Proposed framework

To facilitate the selection of suitable evaluation methods, we propose a framework that links

between the ontology purpose, the ontology aspects (levels), and the appropriate evaluation

method (Brank et al. 2005; Yu et al. 2007). The framework recommends appropriate evaluation

methods based on the purpose of the ontology.

For example, evaluation of the hierarchy or taxonomy level is in particular important in

ontologies that are aimed at supporting knowledge search and exploration in order to foster

efficient search of knowledge. This can be done using any of the evaluation methods (Brank et

al. 2005), but particularly suitable are the gold standard method (assuming a gold standard

exists) and the criteria based method (e.g. Guarino and Welty 2002). The evaluation of the


context level is important in particular in ontologies that are aimed at decision support –for

measuring the effectiveness of the ontology-based DSS. In addition, ontologies aimed at

integration of data from different ontologies may also benefit from the evaluation of the context

level – in order to ensure that references between ontologies are correct. This can be attained

using an application-based method or using experts’ assessments of predefined criteria (Brank

et al. 2005).

Evaluation of the semantic relations level is in particular important in ontologies that are

aimed at creating shared vocabularies and integrating data (or ontologies) to create system

interoperability, since they require agreement on the meaning of things. Such evaluation can

be attained by each one of the evaluation methods. Syntactic level evaluation is important for

any ontology, as correct syntactic specification is mandatory in order that the ontology can be

used by information systems, computerized agents and the Semantic Web. Likewise, since the

building blocks of any ontology are the concepts, instances or facts that form it, evaluation of

the lexical level, i.e., the vocabulary used to represent these concepts and facts, is important for

any ontology. Evaluation on this level usually involve data-driven evaluation methods.

The framework recommendations are summarized in Fig. 1. Each quarter of Fig. 1 links

a particular ontology purpose to ontology levels that are important for evaluation, which are

linked to suitable evaluation methods. The links to and from each level are marked with a

unique colour and line type. When several methods are suitable, a thicker link to a method

indicates that the method is more suitable.


Fig. 1 A framework for evaluation method selection

To complement the framework, we propose an iterative evaluation process, in which, we

identify the ontology purposes, for each purpose, different ontology levels are selected for

evaluation and for each level suitable evaluation methods are applied. The process is depicted

in Error! Reference source not found..

Fig. 2. Ontology evaluation method


Framework Application in the Case of a Pest-Control Ontology

Step 1: purpose identification

The proposed pest-control ontology is intended to serve two purposes: 1. integration of

knowledge from different websites as well as concepts from other ontologies in order to

provide a pest-control knowledge base, and 2. supporting pest-control decisions.

Step 2: Selecting levels of evaluation

The integrative role of the ontology requires evaluating the lexical level (to ensure that the

vocabulary used by the ontology is sufficient), the semantic relations level (to ensure that there

are no semantic ambiguities among concepts from different sources), and the syntactic level,

as shown in the upper left side of Fig. 1. The decision support role of the ontology requires, in

addition to the lexical and syntactic levels, the evaluation of the context level.

While the hierarchy level may also be important for evaluation in ontologies aimed at

facilitating knowledge search, since our ontology does not specify crops and pests’ hierarchies

(e.g. categorization of crops to families) but relies on the hierarchies of AGROVOC, we do not

evaluate the hierarchy level.

Step 3: Selecting evaluation methods

For each level of evaluation, corresponding evaluation methods are selected.

The evaluation of the syntactic level is easily obtained: Protégé (Noy and McGuinness

2001), our ontology editor, provides automatic syntax checking for OWL ontologies. Protégé

also provides mechanisms that not only assure correct syntax but also assert that the ontology

is free from logical problems.

To evaluate the Semantic relations level, Criteria-based evaluation is often used (e.g.,

Delir Haghighi et al. 2013; Yu et al. 2007). Among the criteria specified above in the Materials

and Methods section, we consider the following as relevant for evaluating the semantic

relations level: clarity, coherence, minimal encoding bias, conciseness, and completeness


(other criteria such as extendibility and minimal ontological commitment, are more related to

the context level). While a gold-standard approach may also be applicable, to the best of our

knowledge, there are no relevant gold standards or other pest-control ontologies available.

To evaluate the Context level, we examine the usability of the ontology and the

effectiveness of the system that uses the ontology (Brank et al. 2005). In our context, the goals

of evaluation are: 1. Validate the usability of the ontology for supporting pesticide usage

decisions; 2. Evaluate how using the ontology-based application improves users’ performance.

To address the first goal, we developed a prototypical Web-based application for

supporting pesticide-usage decisions that is based on the proposed pest-control ontology. To

evaluate the effectiveness of the ontology-based application we performed an experiment, in

which participants were given two simple tasks which required retrieving information on

pesticide application regulation. The experiments measure the increased effectiveness of using

the Web-based application in accomplishing those tasks.

To evaluate the lexical level, comparisons with various sources of data are usually used

(Brank et al. 2005). Such evaluation would be redundant in our case, as the ontology was build

based on existing online data sources (see the Specify Formal Ontology section).

Step 4: evaluation

A comprehensive demonstration of the application of the above mentioned evaluation methods

in the context of the pest-control ontology is detailed in Goldstein et al. 2019.

Discussion

Ontologies are a powerful tool for representing domain knowledge, and thus a growing number

of knowledge management systems and DSS are based on ontologies, among other things, in

the context of agriculture.

In this paper, a framework for ontology evaluation is presented. The framework combines

ideas from pivotal existing evaluation methods (Brank et al. 2005; Yu et al. 2007). Its use is


then demonstrated on the case of pest-control ontology.

Clear and structured methods for ontology evaluation are highly important – without them,

it is difficult to consider an ontology as a contribution to research and practice. With the

growing use of ontologies and the Semantic Web for developing agricultural systems, such

methods become increasingly important. Furthermore, as revealed by the literature review,

most of the studies that develop agricultural ontologies do not present the method of

development and, even worse, do not discuss how the developed ontologies were evaluated,

making it difficult to share and reuse them. Our work thus narrows this gap in the literature of

agricultural ontologies, by proposing a development and evaluation method that can be

followed by other studies.

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