A generic design environment for the rural industry knowledge acquisition
Introduction
The knowledge acquisition process generally involves problem formulation from the extracted knowledge in the problem domain. Gunn et al. [14] discusses existing knowledge acquisition techniques based on manual learning, machine learning and logic programming. However, these methods suffer from difficulties such as poor understanding of the key issues of knowledge, users’ ability to incorporate issues outside their area of expertise and the need to engage highly computational techniques for the purpose [14], [25], [30]. Meantime rural industry uptake of agricultural DSS is low (e.g. [4], [17], [21]), and end user development of DSS is problematic [29]. The problems identified present a clear motivation for the development of a straightforward knowledge acquisition method for rural businesses. Farmers’ knowledge of local conditions and expert knowledge of science and best practice are both required for effective decision making, particularly in a context of an industry undergoing rapid changes. Combination of knowledge from the both parties is important. Therefore, in this paper, we describe an expert-driven knowledge acquisition method for modelling domain knowledge relevant to building farm-specific DSS for rural business operators.
We called the new solution software environment an “end user enabled design environment” (EUEDE). EUEDE represents a specific type of design environment where end users (the people who will be the primary user of the system) involvement is central. Another reason is that our aim is to focus on the end user’s thought processes, relevant technology and their own judgement in order to give them active participation in their own application development. This design environment enables the farmer (end user), to build a specific DSS that assists decision making by reporting on the potential production that can be achieved using relevant improvement strategies. In addition, the design itself will be able to deal with a range of rapidly changing factors in rural business operations (for example as shown in [18]).
Previous studies reported that low adoption rates are one of the problems associated with current DSS usage in rural business domains [4], [17], [21]. These studies identify three main reasons that influence the adoption rate of agricultural DSS, namely:
- (1)
The developed DSS does not adapt to the rapidly changing situation in farming businesses.
- (2)
Many of the DSS modules are developed by researchers with the intention of discovering data relationships rather than solving real world and practical problems.
- (3)
Researcher or solution developer often uses their theoretical knowledge for problem solving rather than using farmers’ practical knowledge for problem solving.
In addition, Fountas et al. [10] suggests that farmers base their problem solving on their unique experience and familiarity with their own farm and that because of this, farmers are more likely to utilise information in ways not fully understood by researchers or advisors. The factors suggested above indicate that there is a need for site-specific, updatable, and reconfigurable systems to accommodate changes within the context of each farm. The EUEDE design environment discussed in this paper offers a different knowledge acquisition process whereby a domain expert can provide up-to-date and generic problem-relevant knowledge into the knowledge base. Subsequently, this knowledge base is used for building a specific DSS for the rural business operator.
The EUEDE aims to allow the business operators to reuse and share their knowledge in a specific business context. Previously, a problem ontology model using a knowledge acquisition architecture was developed in medical informatics [1] which implemented the idea of reuse and sharing concepts. Achour’s et al. [1] work was about designing a knowledge acquisition tool in which medical experts are enabled to create and maintain a knowledge base. Their solution model for knowledge acquisition has not revealed its generic capability for general users although it has practical implications in the medical industry. We also adopted the idea of creating and reusing knowledge components in designing for generic feasibility of knowledge acquisition in EUEDE. Our approach goes further though, in facilitating the flexibility to build a specific DSS for decision requirements at the end user level.
In comparison to expert systems and other conventional DSS, our approach aims to present a new ontologically informed architecture, that will deal with problems such as systems rigidity, end user subjectivity in the context of use, obsolescence, limitations of a single expert source, maintenance problems due to requirement for a knowledge engineering intermediary and differences in problem solving emphases between end users and designers. IDIOMS [11] is a design environment for building intelligent DSS that proposed a solution for the above issues. The IDIOMS approach prioritised a constraint-based knowledge representation for extracting expert decision-making rules from databases rather than acquiring rules qualitatively from human experts. In the knowledge acquisition process of EUEDE, an ontological set of expert parameters and rules for decision making is identified by domain experts, which is then used to generate the target-relevant DSS according to the end users decision-making requirements.
Unlike the other knowledge acquisition methods in the rural domain such as POSEIDON [14] and decision-tree induction [25], EUEDE enables a straightforward knowledge acquisition process for the domain experts. In this approach, domain experts identify required knowledge components for a scoped problem domain. They specify the decision-making parameters, variable factors, instances and their relationships (examples from the dairy case are given in Table 2, Table 4). Afterwards, they formulise relationships by defining ratios for each potential level of production in each production class and add expert suggestions for improvement within each class. These ratios come from known industry statistics and science, are stored in the knowledge repository and are later used in displaying guidance in the developed specific DSS. Without extensive re-engineering, experts can update this knowledge with current or emerging information, such as new policies, market requirements or properties from new diet or climate science. This provides a further advance in that the terms understood within the industry are designed into the ontology rather than interpreted by an intermediary and relate directly to its source research and other documentation.
The rest of the paper is organised in the following manner. The next section (Section 2) describes a case of a single system development in the dairy industry, this is used as an example and proof of concept to key stakeholders. Section 3, methods and data extraction, describes the methods adopted and data collection procedures for the EUEDE. Section 4 describes the data collected from expert focus group sessions then Section 5 describes how these knowledge components are converted into a generic model. Section 6 explains the knowledge acquisition and decision model in the developed design environment. Finally, the discussion and summary section (Section 7) presents a brief summary and justification of the ontology development for outlining the knowledge acquisition system within the target problem domain.
Section snippets
A dairy industry case: the milk protein problem
In this section we describe a representative rural industry application: the protein level of cows’ milk. Several recent research studies have reported on the effect of climate change on rural business domains, for instance on Australian milk protein production [16]. Heat stress can reduce milk protein [6], but apart from climate-relevant factors, biological factors (such as a cow’s health, stage of lactation, body condition and genetics) also influence milk protein production [13], [26].
Methods and data extraction
Following an overview presentation of the proposed design the dairy industry stakeholders suggested that milk protein enhancement was a suitable test domain. A case-study approach was then used to acquire an in-depth understanding from documentation and from dairy experts of the decision-making factors related to milk protein production.
For initial knowledge acquisition several two hours to three hour focus group (focus group method [22]) sessions were held, comprising dairy domain experts with
Data findings
In this section, we will show how the six identified factors apply specifically for milk protein enhancement, but we contend that these are also applicable to rural livestock production potentials generally, including dairy, beef cattle, sheep, goat, pig, and chicken farming industries.
We describe the relationships between the inputs and the critical factors of milk protein enhancement in dairy operations which were identified from the extracted knowledge and supplemented by reference to an
Generic knowledge modelling
Rather than use the knowledge base to develop a specific expert system, we parameterised the extracted knowledge using a specific ontology-based development methodology. We partially utilised an approach for ontology development called METHONTOLOGY [9], which advocates the use of a structured informal representation to support the ontology development [3]. The scope of ontology development allows development of generic and reusable decision-making components that can enable the domain experts
Knowledge acquisition and decision model
The aim of the knowledge acquisition approach was to simplify rural business domain knowledge into different building-blocks and store it into a central knowledge repository so that it could be used for DSS development. Fig. 6 shows the knowledge acquisition process and associated decision support building. Domain experts extract the knowledge components such as business production goals and the factors relevant to achieving those. Subsequently, domain experts formulate the rules by defining
Discussion and summary
The objective of this paper was to discuss an upgradeable knowledge model of the design environment which offers a straightforward method of rural business knowledge acquisition for building specific DSS. The paper reported the application of the rural business knowledge in the development of a new design environment called EUEDE where the business operators get assistance from domain experts in building their specific DSS. For instance, the goal was to explore dairy operational knowledge from
Acknowledgements
We thank Geoff Johnston, David Barber and Geoff Hetherington for participating in this research project and express our appreciation to the Queensland Department of the Primary Industries and Fisheries for their continuous funding support and relevant support for this project to be up and running. We also acknowledge the Australian Research Council for their continuous financial support.
References (32)
Some issues in the design of agricultural decision support systems
Agricultural Systems
(1996)- et al.
A model of decision-making and information flows for information-intensive agriculture
Agricultural Systems
(2006) - et al.
Knowledge acquisition for natural resources management
Computer and Electronics in Agriculture
(1999) - et al.
The effects of providing shade to lactating dairy cows in a temperate climate
Livestock Science
(2006) Changing systems for supporting farmer’s decisions: problems, paradigms, and prospects
Agricultural Systems
(2002)- et al.
Induction and evaluation of decision trees for lactation curve analysis
Computers and Electronics in Agriculture
(2003) Dietary influence on protein level in milk and milk yield in dairy cows
Animal Feed Science Technology
(1996)- et al.
A UMLS based knowledge acquisition tool for rule based clinical decision support system development
Journal of the American Medical Informatics Association
(2001) - et al.
An ontology for bioinformatics applications
Bioinformatics
(1999) - J. Bally, T. Boneh, A.E. Nicholson, K.B. Korb, Developing an ontology for the meteorological forecasting process,...
Human centered decision support: the IDIOMS system
Journal of AI & Society
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