development and application of agriculture ontologies

Interest Group on Agricultural Data (IGAD) 3 April, 2017, Barcelona, Spain

Development and Application of Agriculture Ontologies

Hideaki Takeda, Sungmin Joo

Daisuke Horyu, Akane TakezakiNational Agriculture and Food Research Organization (NARO)

National Institute of Informatics (NII)

Standardization of Agricultural Activities Background

Issues

Purpose

Agricultural IT systems are widely adopted to manage and record activities in the fields efficiently. Interoperability among these systems is needed to integrate and analyze such records to improve productivity of agriculture.

To provide the standard vocabulary by defining the ontology for agricultural activity

Data in agricultural IT systems is not easy to federate and integrate

due to the variety of the languages

It prevents federation and integration of these systems and their data.

http://www.toukei.maff.go.jp/dijest/kome/kome05/kome05.html

しろかき

“Puddling”

砕土“Pulverization”

代かき“Puddling”

代掻き

“Puddling”

代掻き作業“Puddling Activity”

荒代 ( かじり )

“Coarse pudding”

荒代かき

“Coarse pudding”

整地“Land grading”

均平化

“land leveling”

Define activity concepts

Define hierarchy

Seeding: activity to sow seeds on fields for seed propagation.

Purpose: seed propagationPlace : fieldTarget : seedAct : sow

“Seeding”

Define activities with properties and their values

The hierarchy of activities is organized by property- New properties and their values are added

- “purpose”, “act”, “target”, “place”, “means” , “equipment”, “season”, and “crop” in order.

- Property values are specialized

Seeding

property value

Agricultural Activity Ontology(AAO)

Formalization by Description Logics

Crop production activity

Crop growth activity

purpose ： crop production

purpose ： crop growth

Agricultural activity

Activity for control of propagationActivity for seed

propagation

purpose ： control of propagation

purpose ： seed propagation

Seedingact : sowtarget ： seedplace ： field

Activity for seed propagation

Seeding

Hierarchy by purpose

Designing of Agricultural Activity Ontology(AAO)

Differentiate concepts by property

purpose : seed propagationplace : paddy fieldtarget : seedact : sowcrop ： rice

purpose : seed propagation purpose : seed propagationplace : fieldtarget : seedact : sow

Agricultural activity 　 >…> Activity for seed propagation > Seeding

purpose : seed propagationplace : well-drained paddy fieldtarget : seedact : sowcrop ： rice

Direct sowing of rice on well-drained paddy field Direct seeding in flooded paddy field

Well-drained paddy field < field paddy field < field


Activity for seeding Direct seeding in flooded paddy field

Direct sowing of rice on well-drained paddy field

Seeding on nursery box

The Structuralizaion of the Agricultural Activities (Protégé)


Polysemic concepts

[disjunction form]

[conjunction form]

Pudlling

Subsoil breakingPulverizationLand preparation

Water retentionActivity for water management

Land leveling

Polysemic relationship

Pulverization by harrow

purpose : pulverizationpurpose : water retentionpurpose : land leveling

Definition of agriculture activities with multiple purposes or other properties.

Puddling 　


Water retention

Land leveling Pulverization

Puddling 　

Polysemic concepts (Protégé)


Synonym


Expressions in multiple languages are also represented as synonyms. (It is important especially for non-English speaking countries)

Reasoning by Ontology

Reasoning by Agriculture Activity Ontology

Activity for biotic control

Activity for suppression of pest animals

Activity for suppression of pest animals by physical

means

control of pest animals

Physical means

means(0,1)

purpose(0,1)

Biotic control

purpose(0,1)

Activity for suppression of pest animals by chemical

means

Chemical means

purpose(0,1)

means(0,1)

Making scarecrow‘

suppression of pest animals

Purpose(0,1)

build

act(0,1)

scarecrow

target(0,1)

Physical means

Means (0,1)

？ Example of 「 Making scarecrow 」

?


Infer the most feasible upper concept for the given constraints for a new words

Reasoning by Ontology

かかし作り

物理的手段

means(0,1)

means(0,1)

Inference with SWCLOS

[1] Seiji Koide, Theory and Implementation of Object Oriented Semantic Web Language, PhD Thesis, Graduate University for Advance Studies, 2011

[1]

[1]

Activity for biotic control

Activity for suppression of pest animals

Activity for suppression of pest animals by physical

means

control of pest animals

Physical means

means(0,1)

purpose(0,1)

Biotic control

purpose(0,1)


Activity for suppression of pest animals by chemical

means

Chemical means

purpose(0,1)

means(0,1)

Making scarecrow

make

act(0,1)

scarecrow

target(0,1)

Infer the most feasible upper concept for the given constraints for a new words

Reasoning by Agriculture Activity Ontology

Making scarecrow is a subclass of Activity for suppression of pest animals by physical means

Applying Agricultural Activity Ontology URI

Give a unique URI for each concept

http://cavoc.org/aao/ns/1/ は種

　

http://www.cavoc.org/ http://www.cavoc.org/aao

Web Services based on Agriculture Activity Ontology

• Version Historyver. 141: published on January 5, 2017. 410 words and concepts.ver 1.33: published on September 23, 2016. 374 words and concepts,ver 1.31 : published on April 22, 2016. 355 words collected, the concepts were classified with 8 attributes.ver 1.10 : published on February 12, 2016. 330 words collected, new words are collected.ver 1.00 : published on November 2, 2015. 301 words collected, defined with Description Logics, introduction of property.ver 0.94 : published on May 12, 2015. 185 words collected.

Web Services based on Agriculture Activity Ontology　 Data Sharing

The data of AAO can be downloaded in the RDF/Turtle formats from cavoc.org/aao/.

we provide a SPARQL endpoint for users to explore AAO data using SPARQL queries.

[the SPARQL Endpoint of AAO][Download]

Web Services based on Agriculture Activity Ontology　 Converting synonyms to core vocabulary

http://www.tanbo-kubota.co.jp/foods/watching/14_2.html

“Puddling Activity”“sowing”

…

AAO

PuddlingSeeding

…

Converting

[system]

API

Puddling Activity and sowing…

[system’]

Puddlingand seeding…

Working time survey on Census of agriculture and forestry

Automatic Statistical Processing Based on Agriculture Activity Ontology

Puddling30 min.

Farmer #1

Puddling Activity40 min.

Land preparation50 min.

Farmer #2

Farmer #3

Puddling :Average work time : 45min

Puddling30 min.

Puddling40 min.

Puddling50 min.

Handwritten data,Excel file ... Mapping by man

power

Census of agriculture and forestry

Automatic statistical processing system

Agriculture IT system

Data file (csv)

Synonym list of AAO(csv)(http://cavoc.org/aao.php)

Common Agricultural Vocabulary(http://cavoc.org)

API OR

Converting to core vocabulary

AgricultureActivity names

Accumulation and integration

Census of agriculture and forestry

Core vocabulary

Converting to core vocabulary

Agriculture IT system Agriculture Activity Ontology

環境制御作業土壌制御作業土壌準備作業耕起プラウ耕秋耕春耕耕耘ロータリー耕荒起こしスタブル耕整地砕土ハロー作業代かき

activities 　 total worktime 代かき　　 129min 耕起　　 223min　　　　…

census(work time)

area7.2612.9514.7512.95

activity代かき

2 番耕起代かき

3 番耕起

work time45min125min84min98min

作業分類「耕起整地」における作業の内容

荒起し秋田起しの労働、本田の砕土、

しろかき( 荒しろを含む ) から本田かん水、整地までの労働( 先にかん水して行う耕うんから代かきまでの一貫作業を含む )

activity list for census of agriculture and forestry

avg.range

…

Mapping activity names using Agriculture Activity Ontology

puddling

Plowing #2 Plowing #3

puddling

Plowing

puddling

Plowing

STEP 1 : Importing csv data

Automatic statistical processing system

Mapping properties for agriculture activities

Plowing #1 Plowing #2Plowing #3FertilizationTopdressingWeeding on the leveeCompost applicationRice reapingLand gradingLevee repairApplying herbicidePloughing with stubble cultivatorPlowing by ploughRice transplantingSeeding(etc)Puddling

How did we build Agriculture Activity Ontology?

• Share the experience of building ontologies

• Design Process– 0th Step: Project Formation– 1st Step: Survey– 2nd Step: Analysis of Data– 3rd Step: Proposed Structure (1st)– 4th Step: Introduction of Descriptions Logics– 5th Step: Evaluation and Enrichment by domain experts

Design Process- 0th Step: Project Formation -

• 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).

• Project aim: define common vocabulary on agriculture activity – To share knowledge among farmers of different crops and different regions and

different systems – Human understandable and machine readable

• Four members from two organizations– Ontology Expert Researchers from National Institute of Informatics (NII)

– Information Expert Researchers from National Agriculture and Food Organization (NARO)

Design Process- 1st Step: Survey -

• Survey of existing vocabularies– Agrovoc: defined by FAO. Most popular and famous

vocabulary in the domain• International• Maintenance• Machine readable (LOD)

– Agropedia• In Japanese• With explanations

– MAFF Guideline (prototype version)• Official• Related to Elements in Official Statistics

AGROVOC

ThesaurusAGROVOC organizes words by synonym, narrower/broader, and related relationship.

harvesting topping(beets)balinggleaningmechanical harvestingmowing

AGROVOC. . .

Narrower/broader relationship is not clearly defined. So relationship among bother words are often mixed and misunderstood.

relationship between siblings

AGROVOC is the most well-known vocabulary in agriculture supervised by Food and Agriculture Organization(FAO) and the thesaurus containing more than 32,000 terms of agriculture, fisheries, food, environment and other related fields.

The number of activity names about rice farming, which is important in Asia including Japan, are insufficient.

農業 ITシステムで用いる農作業の名称に関する個別ガイドライン（試行版）

Design Process- 2nd Step: Analysis of data

Design Process- 3rd Step: Proposed Structure (1st) -

Define hierarchy clearly

Accept various synonymous words

Hierarchy is convenient for human to understand and for computers to process. But it often be confused by mixing different criteria on relationship among concepts/words. It causes difficulty when adding new concepts/words and when integrating different hierarchies.

Names for a single concept may be multiple by region and by crop

Define relationship clearly between upper and lower concepts as basis of classification

Clarify an entry word and their synonyms for each concept

harvesting topping(beets)balinggleaningmechanical harvestingmowing

Thesaurus (AGROVOC)

. . .

harvesting mechanical harvesting

manual harvesting

. . .

Inheritrelationship between siblings

Representation: ”Harvesting”

Design Process- 4th Step: Introduction of Description Logics -

• Consideration of the structure– Discovery of logical structure– Reformation of the structure by Description Logics

• Use of a property for each is-a relation– Introduction of a new property– Is-a hierarchy of a property value

• Re-arrangement of classes


manual harvesting

. . .

Harvest Harvest

Harvest

InheritbyMachine

manually

＋

＋


[Act]

Ontology


manual harvesting

. . .


[Means]

[Means]

Design Process- 5th Step: Evaluation and Enrichment by domain experts -

• Ask evaluations to experts – individual crops experts– Farmer management system developer

• Feedback– Some alternation of class structure– Many new words

• Crop-specific words• Area-specific words (dialect)

What we’ve learnt• Survey and critics of existing vocabularies

– Understanding of pros and cons– Fix the target

• Data-driven approach– Avoid too abstract discussion

• Small group of knowledgeable persons of two sides (domain and informatics)– Constructive discussion

• Make the core then extend it– Introduction of AI experts– Introduction of more domain experts

• Communication is important

Crop Vocabulary (CVO)• Next target: Standardize crop names

– ジャガイモ (jagaimo), 馬鈴薯 (bareisho) poteto@en– 大豆 (daizu), 枝豆 (edamame), soy@en– …

• Crop Concept– Crop name

• Synonym– Japanese common name

• Scientific name– Edible part– Mature/Immature– Other properties

• Planting method …

Data-drivenCrop names foragricultural chemicals

Guideline name (2017) CVO

Guideline name (2016)

Crop statistics

Household budget survey

Vegetable Code

MAFFEncourage varieties

Food classifi-cation

http://cavoc.org/Common Agricultural VOCabulary

Agriculture Activity Ontology (AAO) ver 1.31http://cavoc.org/aao/

Conclusion

There are no gold way to build ontologies. We adopt the bottom-up and minimum commitment approach. It requires time and effort. We believe that it is successful at least to build AAO.