Designing of Ontology for Domain Vocabulary on Agriculture Activity Ontology (AAO) and a Lesson Learned

  • Sungmin Joo
  • Seiji Koide
  • Hideaki Takeda
  • Daisuke Horyu
  • Akane Takezaki
  • Tomokazu Yoshida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10055)

Abstract

This paper proposes Agriculture Activity Ontology (AAO) as a basis of the core vocabulary of agricultural activity. Since concepts of agriculture activities are formed by the various context such as purpose, means, crop, and field, we organize the agriculture activity ontology as a hierarchy of concepts discriminated by various properties such as purpose, means, crop and field. The vocabulary of agricultural activity is then defined as the subset of the ontology. Since the ontology is consistent, extendable, and capable of some inferences thanks to Description Logics, so the vocabulary inherits these features. The vocabulary is also linked to existing vocabularies such as AGROVOC. It is expected to use in the data format in the agricultural IT system. The vocabulary is adopted as the part of “the guideline for agriculture activity names for agriculture IT systems” issued by Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan. Also we investigated the usefulness of the ontology as the method for defining the domain vocabulary.

Keywords

Ontology Agriculture Agronomic sciences Knowledge representation Core vocabulary Vocabulary management 

1 Introduction

The various IT systems have been introduced in farm management to realize better management, i.e., more efficient resource management, finer production control and better product quality. Now data management is indispensable in farm management. Data in farm management is also used in own purpose but the aggregated data is used for statistics, analysis and prediction for area agriculture.

Data in agricultural IT systems is nonetheless not easy to federate and integrate since the languages to describe data are not unified. Terminology in agriculture such as names of activity, equipment, and crop has not been well standardized mainly because agriculture has been local. Some of locality comes from diversity of culture and environment and others from the way of business, i.e., farms are small and run independently. But introduction of IT systems changed the situation; farms can be connected beyond the barrier of individual farms, regions, and even culture. But un-unified terminology exists as the problem. Without unified terminology, smooth data exchange cannot be enabled. So standardization of terminology is the key to enhance agriculture with IT systems. We focus on agriculture activity in this paper. Agriculture activity is the most basic element of farm management and also the most difficult to standardize since it is more abstract than other types of terminology like equipment and crop.

In this paper, we investigate the existing vocabulary system for agricultural activities. Then we propose the agriculture activity ontology by paying attention to the linguistic feature of agricultural activities. We also explore reasoning functions with the ontology and web services to utilize the ontology. Finally, we discuss the future directions of the improvement and extension of the ontology.

2 An Existing Resource: AGROVOC

In this section, we survey the features of AGROVOC (a portmanteau of agriculture and vocabulary) [1] as an existing agricultural vocabulary system. AGROVOC is the most well-known vocabulary in agriculture supervised by Food and Agriculture Organization (FAO) of the United Nations. AGROVOC is the thesaurus containing more than 32,000 terms of agriculture, fisheries, food, environment and other related fields. It has international interoperability as it is provided in 21 languages. Each term can have the hierarchical structure with narrower concept and broader concept, and there are 25 top-most concepts such as activities, organisms, location, products and so on. 1,434 narrower concepts are provided in activities which contains the concepts about agriculture activity.

AGROVOC is the well-known vocabulary system which has international interoperability and it contains many terms. However, There are some insufficient features in order to use as the core vocabulary. First of all, the relationship between concepts is not clear. Most of narrower/broader relationship is attached only by considering the pair-wise relationship. Thus hierarchy by these relationships are not so consistent. For instance, Vegetative propagation has Rooting as the narrower concept. Figure 1 shows the broader concept and the narrower concept of harvesting. mowing is located as the narrower concept of harvesting, but it is not an appropriate classification considering the general meaning of mowing. This kind of problem occurs because AGROVOC is established vocabulary system as the thesaurus. This vague relationship between concepts makes the problem when adding a new term; it is difficult to define the relation with concepts in AGROVOC, i.e., to find the best position to the new term.
Fig. 1.

The broader concept and the narrower concept of harvesting in AGROVOC (http://oek1.fao.org/skosmos/agrovoc/en/page/c_3500).

In addition, the number of activity names about rice farming, which is important in Asia including Japan, are insufficient. For example, in rice farming, especially pulling seedlings and midseason drainage which are important activity in a rice paddy, are not contained in AGROVOC.

The ambiguousness of relationship among concepts in the existing vocabulary system of agricultural activities is thus problematic. It is required to clearly define the relationships among concepts and specify them. In order to solve these problems, this paper suggests the establishment of the ontology for agriculture activity. The ontology can define the clear concepts by separating concepts and representation. Also it can reflect the characteristics of the domain more clearly by structuralizing the relationships.

3 Designing of Agricultural Activity Ontology

This paper describes the Agriculture Activity Ontology (AAO) as the basis of the core vocabulary of agriculture activity, and it provides semantics for agricultural activity names. Also, AAO is formalized by Description Logics in order to define and classify the agricultural activities clearly. Formalization by Description Logics makes it possible to judge the inconsistency and subsumption among concepts, and to enable more logical inferences. The ontology designed by Description Logics can be converted to OWL so that it can processed by computers.

3.1 The Structuralization of the Agricultural Activities

Our strategy to structuralize agricultural activities is the top-down, i.e., starting from the most general activity and expanding it to more specific activities. The important criteria for the top-down approach is how we can classify more specific concepts consistently. We define more specific concepts by specifying attributes and their values. We furthermore define the general rule for specifying attributes.

We start with the top concept Agriculture Activity which denotes all kind of activities related on crop and/or fields. Then we break down the concept into more concrete concepts. When farmers plan or do a certain agricultural activity, the first decision is what for they would take the action, i.e., purpose is the first attribute to distinguish agriculture activities. After the purpose is well specified, we use other attributes, i.e., act (type of action), target, place, means, equipment, and season in this order. Crop is also introduced so as to define the activity for a specific crop. These eight attributes are used to define the concept and to form the hierarchy of the agricultural activity.

The basic idea of formalization of Agriculture Activity Ontology is that concepts correspond to concepts in Descriptions Logics (DLs) while attributes such as purpose correspond to roles in DLs. By adding the role and the role value, the concepts is defined as the narrower concept of the original concept. If the added role is what is already used in the original concept and the value of the role of the new concept is narrower than that in the original concept, the new concept is also narrower concept of the original concept. It should be noted; not all concepts correspond to terms for farm management since some abstract concepts are introduced just to classify. So we distinguish the abstract concepts not corresponding to terms and concrete concepts corresponding to terms. We call former category and the latter term. For example, thinning and cutting root are terms, and their broader concept activity for uniformity is the category. The category and the term are succeeding the values of the attributes, and they have the relationship of inclusion.

Now let’s look at the ontology in detail. At first, we classify the agriculture activity into two; crop production activity which is related to the crop production directly, and administrative activity which is related to the farm management. crop production activity is classified into the following four activities: crop growth activity which is for the purpose of crop growth, activity for environmental control which is for the purpose of the environment control, activity for post production which is for the purpose of the post production, and activity for support for crop production which is for the purpose of the indirect support in the crop production. So as to define narrower concepts, purpose, act, target, place, means, equipment, season, crop were used as attributes. Classification is conducted by using values of these attributes.

The activity Seeding can be defined as follows; First of all, Seeding is one of the activities of Activity for seed propagation, and Activity for control of propagation is the broader concept of Activity for seed propagation, Crop growth activity is the broader concept of activity for control of propagation. Crop production activity is the broader concept of Crop growth activity. Lastly, the broader concept of Crop production activity is agriculture activity which is the broadest concept. All these concepts are classified by purpose so that purpose attribute is used. Since the values of purpose attributes are hierarchical, activity concepts are hierarchical. Seed propagation is the narrower concept of Control of propagation, and it then the narrower of Crop growth. Crop growth is the narrower concept of Crop production.
$$\begin{aligned} Crop\_production\_activity\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.crop\_production \end{aligned}$$
(1)
$$\begin{aligned} Crop\_growth\_activity\equiv & {} Crop\_production\_activity \nonumber \\&\sqcap \forall purpose.crop\_growth \end{aligned}$$
(2)
$$\begin{aligned} Activity\_for\_control\_of\_propagation\equiv & {} Crop\_growth\_activity \nonumber \\&\sqcap \forall purpose.control\_of\_propagation \end{aligned}$$
(3)
$$\begin{aligned} Activity\_for\_seed\_propagation\equiv & {} Activity\_for\_control\_of\_propagation \nonumber \\&\sqcap \forall purpose.seed\_propagation \end{aligned}$$
(4)
The formula (1), (2), (3), (4) can be represented as the formula (5).
$$\begin{aligned} Activity\_for\_seed\_propagation\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation\nonumber \\&\sqcap \forall purpose.control\_of\_propagation\nonumber \\&\sqcap \forall purpose.crop\_growth\nonumber \\&\sqcap \forall purpose.crop\_production \end{aligned}$$
(5)
Here the purposes of seed propagation, control of propagation, crop growth, crop production have the following relation of inclusion by definition.
$$\begin{aligned} seed\_propagation \sqsubseteq control\_of\_propagation \sqsubseteq crop\_growth \sqsubseteq crop\_production \nonumber \\ \end{aligned}$$
(6)
Thus the formula (5) is represented as below.
$$\begin{aligned} Activity\_for\_seed\_propagation\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation \end{aligned}$$
(7)
On the other hand, Seeding is the activity whose purpose is seed propagation, place is field, target is seed, and act is sow. So it is defined as below.
$$\begin{aligned} Seeding\equiv & {} Activity\_for\_seed\_propagation \nonumber \\&\sqcap \forall act.sow\nonumber \\&\sqcap \forall target.seed \nonumber \\&\sqcap \forall place.field \end{aligned}$$
(8)
Therefore, Seeding in the agricultural activities is defined like below from the formula (7) and (8).
$$\begin{aligned} Seeding\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation\nonumber \\&\sqcap \forall act.sow\nonumber \\&\sqcap \forall target.seed \nonumber \\&\sqcap \forall place.field \end{aligned}$$
(9)
Among Seeding, when the crop is rice and the place is nursery box, it is classified as seeding on nursery box, when the place is paddy field, it is classified as direct seeding in flooded paddy field and when the place is well drained paddy field, it is classified as direct seeding in well drained paddy field. As a result, Seeding on nursery box, Direct seeding in flooded paddy field and Direct seeding in well drained paddy field are in the relationship of siblings, and they are the narrower concept of seeding. Here these activities can be represented by the description logic as below.
$$\begin{aligned} Seeding\_on\_nursery\_box\equiv & {} Seeding \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.nursery\_box \end{aligned}$$
(10)
$$\begin{aligned} Direct\_seeding\_in\_flooded\_paddy\_field\equiv & {} Seeding \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.paddy\_field \end{aligned}$$
(11)
$$\begin{aligned} Direct\_seeding\_in\_well\_drained\_paddy\_field\equiv & {} Seeding \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.well\_drainded\_paddy\_field \nonumber \\ \end{aligned}$$
(12)
The place value of nursery box, paddy field and well drained paddy field are defined as a part of field.
$$\begin{aligned} nursery\_box\sqsubseteq & {} field, \nonumber \\ paddy\_field\sqsubseteq & {} field,\nonumber \\ well\_drained\_paddy\_field\sqsubseteq & {} field\end{aligned}$$
(13)
Thus, from the formula (11), (12) and (13), we define the activity Seeding on nursery box, Direct seeding in flooded paddy field and Direct seeding in well drained paddy field as below.
$$\begin{aligned} Seeding\_on\_nursery\_box\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation\nonumber \\&\sqcap \forall act.sow\nonumber \\&\sqcap \forall target.seed \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.nursery\_box \end{aligned}$$
(14)
$$\begin{aligned} Direct\_seeding\_in\_flooded\_paddy\_field\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation\nonumber \\&\sqcap \forall act.sow\nonumber \\&\sqcap \forall target.seed \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.paddy\_field \end{aligned}$$
(15)
$$\begin{aligned} Direct\_seeding\_in\_well\_drained\_paddy\_field\equiv & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.seed\_propagation\nonumber \\&\sqcap \forall act.sow\nonumber \\&\sqcap \forall target.seed \nonumber \\&\sqcap \forall crop.rice \nonumber \\&\sqcap \forall place.well\_drainded\_paddy\_field \nonumber \\ \end{aligned}$$
(16)
By combining adding more attributes and subdividing the attribute values, we can flexibly form the hierarchical structure to represent terminology used in agriculture without loosing logical consistency.

3.2 Polysemic Concepts

There are many activities conducted for the multiple purposes in the agricultural activities. The typical case is Activity for mulching. One of its purposes is spreading organic matter and other things on the surface of the soil, but there are other purposes; to keep the temperature optimal and controls the temperature, and to refrain weeds. The other example is Puddling. It is to plow the bottom of a rice field, but it is also intended to conduct for the purpose of water retention, i.e., preventing from the water leak, and for the purpose of land leveling, i.e., flattening the soil. In our formalization, these concepts are interpreted as polysemic concept and modelled as disjunction of multiple concepts since none of multiple concepts are mandatory rather optional. Here puddling can be expressed with DL as follows;
$$\begin{aligned} Puddling\equiv & {} Pulverization \nonumber \\&\sqcup Land\_leveling \nonumber \\&\sqcup Activity\_for\_water\_retention \end{aligned}$$
(17)
Now Puddling is expanded as follows;By converting the disjunction form into the formula (18) to the conjunction form, we can infer the formula (19).
$$\begin{aligned} Puddling\sqsupseteq & {} Agriculture\_activity \nonumber \\&\sqcap \forall purpose.(land\_preparation \sqcup water\_retention) \nonumber \\&\sqcap \forall act.(crush \sqcup level) \nonumber \\&\sqcap \forall place.paddy\_field \end{aligned}$$
(19)
The polysemic concepts defined with multiple concepts can properly express features for the activities conducted for multiple attributions in the Agriculture Activity Ontology.

3.3 Synonym

There are many synonyms in the vocabulary for agricultural activities. It is easily treated in DL as follows;
$$\begin{aligned} Seeding\equiv & {} Sowing \end{aligned}$$
(20)
In addition, expressions in multiple languages are also represented as synonyms. It is important especially for non-English speaking countries1.So as to correspond to the variety of the vocabulary, the Agriculture Activity Ontology enables to separate the concepts themselves and expressions of the concepts properly.

4 Reasoning by Agriculture Activity Ontology

Generally speaking, the more abstract concepts are, the more difficult it is to define them. The more specific concepts are, the easier it is to take the specific attributes into account. On a specific agriculture activity, it is easy to define the activity with the specific attributes, such as the purpose, the target, the means, etc. Since the purpose of AAO is to keep the agriculture activity terms consistent and well-organized, placing new terms at the appropriate location in the ontology is mandatory. For instance, suppose that we want to add a new term Making scarecrow. It is composed of attribute the purpose of pest animal suppression and attribute the means of physical means, then the abstract activity Activity for pest animal suppression by physical means may become the abstraction of Making scarecrow, even if it is not specified explicitly. Furthermore, more abstract Activity for pest animal suppression must be the abstraction of Activity for pest animal suppression by physical means without attribute the means.
$$\begin{aligned} making\_scarecrow\equiv & {} \forall purpose.pest\_animal\_suppression \nonumber \\&\sqcap \forall act.make \nonumber \\&\sqcap \forall target.scarecrow \nonumber \\&\sqcap \forall means.physical\_means \end{aligned}$$
(22)
The question is what is the broader concept of Making scarecrow in AAO, and how we can find it. We set up the ontology of attributes with the relationship of inclusion as follows.
$$\begin{aligned} pest\_animal\_suppression \sqsubseteq biotic\_suppression \sqsubseteq biotic\_control \end{aligned}$$
(23)
We also set up the hierarchical structure of the agriculture activity as follows.
$$\begin{aligned} Activity\_for\_pest\_animal\_suppression\_by\_physical\_means \equiv \nonumber \\ Activity\_for\_pest\_animal\_suppression \sqcap \forall means.physical\_means \end{aligned}$$
(24)
$$\begin{aligned} Activity\_for\_pest\_animal\_suppression \equiv \nonumber \\ Activity\_for\_biotic\_control \sqcap \forall purpose.pest\_animal\_suppression \end{aligned}$$
(25)
$$\begin{aligned} Activity\_for\_biotic\_control\equiv & {} activity\_for\_environmental\_control \nonumber \\&\sqcap \forall purpose.biotic\_control \end{aligned}$$
(26)
Activity for pest animal suppression by physical means is a conjunction of pest animal suppression (for purpose) and physical means (for means). Thus, there is no contradiction by making Activity for pest animal suppression by physical means a broader concept of Making scarecrow.
The main task of Description Logics is to compute truth value in subsumption checking [2]. However, it cannot discover subsumers or subsumees for a given subsumee or subsumer in a given ontological hierarchies. Therefore, we introduced Schank’s algorithm for Case-Based Reasoning (CBR) [3] into our OWL [4] reasoning engine named SWCLOS [5, 6]2, whereby an appropriate position of a given collection of pairs of attributes and values can be automatically discovered in coherent hierarchies of concepts and their attribute values, starting from a given domain top concept and descending subsuming chains to specific ones. In the systematization of AAO reasoning, the knowledge expressed in DLs is described in OWL. The following shows an example of the formula (12) described in Turtle [7].
In SWCLOS, we can see the form of any OWL entity in lisp-like expression. The following demonstrates the expression of cavoc.aao:making_scarecrow in SWCLOS. Note that it has no subsumer concept defined here. Command refine-abstraction-from performs Schank’s algorithm with parameters, a domain top concept and an entity for the discovery of position.
Here, in gx(9) the appropriate position in the hierarchy was decided, and gx(10) demonstrated the cavoc.aao:Making_scarecrow should be a subclass of cavoc.aao:Activity_for_pest_animal_suppression_by_physical_means. Namely, following results were inferred.
$$\begin{aligned} making\_scarecrow\sqsubseteq & {} \{ (\forall purpose.activity\_for\_pest\_animal\_suppression\nonumber \\&\sqcap \forall act.make \nonumber \\&\sqcap \forall target.scarecrow \nonumber \\&\sqcap \forall means.physical\_means)\nonumber \\&\sqcap Activity\_for\_pest\_animal\_suppression\_by\_physical\_means\} \nonumber \\ \end{aligned}$$
(27)

5 Web Services Based on Agricultural Activity Ontology

AAO is hosted on CAVOC (Common Agricultural VOCabulary, www.cavoc.org). In this section, we explain the web services of CAVOC based on the agricultural activity ontology.
Fig. 2.

Main page of AAO (http://cavoc.org/aao).

5.1 Namespace of Agriculture Activity

CAVOC allows browsing and searching concepts of AAO (Fig. 2, www.cavoc.org/aao). The key feature of CAVOC is that it provides URIs for names of agriculture activities. The agriculture activity ontology has unique namespace, and each agriculture activity has URI. Each URI is structured using the http://cavoc.org/aao/ns/1/ namespace, therefore all of the terms and categories are preceded with this URL. Figure 3 is an example of the URI for Seeding. In the page, the hierarchical structure is represented in order to indicate the narrower concept, the broader concept, and the relationship between concepts. In addition, it provides the brief natural language explanation of the concept by using values of attributes. The simple interface allows users to browse concepts of AAO through a tree interface, and to search for specific terms.
Fig. 3.

The namespace of seeding (http://cavoc.org/aao/ns/1/seeding).

5.2 Version History

We have developed the agriculture activity ontology with some versions. Table 1 shows the overview of the versions of the agriculture activity ontology. In version 0.94, the first version to open publicly, the concepts were classified by two attributes of purpose and method. In version 1.00, the attributes were used to specify definition of activity concepts and they also have hierarchical structure. Now the description of the hierarchical relationship was logically defined based on the description logic. In the version 1.10, More concepts were introduced by consulting experts in agricultural fields and farm management systems. In the version 1.31, the newest version, the concepts were classified with eight attributes of purpose, act, target, place, means, equipment, season and crop.
Table 1.

Listing of the history of AAO version changes

Version

Date initiated

Categories

Terms

Concepts

Attributes

Maximum layer

0.94

12/05/2015

59

126

185

2

6

1.00

02/11/2015

67

234

301

7

6

1.10

12/02/2016

73

257

330

7

7

1.31

22/04/2016

86

269

355

8

7

Fig. 4.

The SPARQL endpoint of AAO.

5.3 Data Sharing

The data of AAO can be downloaded in the RDF/Turtle formats from http://cavoc.org/aao/. This format is well supported by many semantic tools, and it is possible to convert it into other RDF formats if needed. Also, we provide a SPARQL endpoint for users to explore AAO data using SPARQL queries (Fig. 4).

6 Discussion and Future Work

The agriculture activity ontology ver 1.31, the latest version of the agriculture activity ontology, has 355 concepts which are either categories and terms. It covers most of terms in the national agricultural statistics in Japan. Structuralization with attributes are our own idea so that we need discussion and communication with experts in agriculture more extensively to verify the value of the ontology.

The extension to crop-specific ontologies is one of the important directions of AAO. The scope of the agriculture activity ontology is not just the general terms of agriculture but also covers agriculture activities specialized by crop. The activity specialized in the crop currently contains 10 types of crop such as rice, melon, and other things. By using the attribute of the crop, it is possible to extend to the crop-specific ontologies. We are now developing the crop-based ontologies that can define crop-specific concepts by using crop independent concepts.

There are still issues in Structuralization of the ontology. One of them is composite concept. In the agriculture IT systems, there are cases in which the multiple works are managed as a single activity by combining multiple activities. For example, Raising seedling is composite includingSeeding, Fertilization, Watering and other things. However, when the agriculture is planned or implemented, it is managed as Raising seedling. We express this activity by combining existing activities. The concept can be expressed with part-of relationship, but the simple solution is not always suitable since all of the concepts are not sometimes necessary. We are now considering more appropriate formalization for combination of the relevant activities.

International interoperability is next to do. We have already connections with other activities for agricultural ontologies (for example Crop Ontology Group3). Our research has begun from the purpose of establishing the core vocabulary for the field of agriculture of Japan, although it can be independent regardless of language culture so that it can be applied to various languages and cultures. We will improve the ontology in order to develop international core vocabulary.

We designed a domain vocabulary by using ontology based on Description Logics. The design used by the Description Logics can make a concept which has ambiguous meaning classified clearly and deal with the situation when new vocabularies have to be added. The meaning of the concept was defined as suitable attributes for the domain. The structure was constructed to make sense the meaning of the concept by using the value of attributes and it could make the effective processing when the vocabulary lists have to be generated automatically or when the related applications have to be realized. Also it can be used as a tool like dictionary in a namespace for each vocabulary.

7 Conclusion

We provide the Agriculture Activity Ontology (AAO) so as to standardize the vocabulary for agricultural activities. By using the ontology, it is possible to define concepts of agriculture activities beyond the linguistic diversity of the vocabulary for agricultural activities. The agriculture activity ontology was adopted as the part of “the guideline for agriculture activity names for agriculture IT systems” issued by Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan in 2016, which is one of the achievements of this study [8]. We are now working to extend our idea to other agriculture domains, i.e., the standardization of vocabulary for agriculture such as the crop, distribution, and agricultural pesticide.

Footnotes

  1. 1.

    Indeed, AAO is basically written in Japanese and expressions of concepts and roles in English are optional. But we here provide the English version of AAO for simplicity of explanation.

  2. 2.

    SWCLOS is a lisp-based OWL Full processor on top of Common Lisp Object System (CLOS). It is downloadable from https://github.com/SeijiKoide/SWCLOS.

  3. 3.

Notes

Acknowledgement

This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Technologies for creating next-generation agriculture, forestry and fisheries” (funding agency: Bio-oriented Technology Research Advancement Institution, NARO).

References

  1. 1.
    AGROVOC Multilingual agricultural thesaurus. Subsequences. J. Mol. Biol. 147, 195–197 (1981). http://aims.fao.org/vest-registry/vocabularies/agrovoc-multilingual-agricultural-thesaurus
  2. 2.
    Baader, F., et al. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  3. 3.
    Riesbeck, Christopher K., Roger C. Schank, Inside Case-Based Reasoning, ISBN 0-89856-767-6, LEA (1989)Google Scholar
  4. 4.
    OWL Web Ontology Language Guide, W3C Recommendation 10, February 2004 https://www.w3.org/TR/owl-guide/
  5. 5.
    Koide, S., Takeda, H.: OWL-full reasoning from an object oriented perspective. In: Mizoguchi, R., Shi, Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 263–277. Springer, Heidelberg (2006). doi:10.1007/11836025_27 CrossRefGoogle Scholar
  6. 6.
    Koide, S.: Theory and implementation of object oriented semantic web language Dr.thesis, Department Informatics School of Multidisciplinary Sciences, The Graduate University for Advanced Studies (SOKENDAI) (2010)Google Scholar
  7. 7.
    RDF 1.1 Turtle, Terse RDF Triple Language, W3C Recommendation 25, February 2014. https://www.w3.org/TR/turtle/
  8. 8.
    Ministry of Agriculture, Fisheries and Forestry (MAFF), Japan: the guideline for agriculture activity names for agriculture IT systems (2016). http://www.kantei.go.jp/jp/singi/it2/senmon_bunka/shiryo/shiryo04.pdf (in japanese)

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Sungmin Joo
    • 1
  • Seiji Koide
    • 2
  • Hideaki Takeda
    • 1
  • Daisuke Horyu
    • 3
  • Akane Takezaki
    • 3
  • Tomokazu Yoshida
    • 3
  1. 1.National Institute of InformaticsTokyoJapan
  2. 2.Ontolonomy, LLC.YokohamaJapan
  3. 3.National Agriculture and Food Research OrganizationIbarakiJapan

Personalised recommendations