Presented at the Interest Group on Agricultural Data (IGAD) ,3 April, 2017, Barcelona, Spain
Abstract: n this talk, we present the current status of our agriculture ontologies that are developed to accelerate the data use in agriculture.
The agriculture activity ontology formalizes the activities in agriculture. We have developed it for three years. Now we are developing its applications. One application is to exchange formats between different farmer management systems. Another ontology is the crop ontology that standardizes the names of crops. The structure is simple but has links to many other standards in distribution industry, food industry and so on.
This document summarizes research using the Soil and Water Assessment Tool (SWAT) to model critical source areas (CSAs) of phosphorus pollution in the Sandusky River watershed in northwest Ohio. The SWAT model was set up with detailed data on the watershed's terrain, soils, land use, crops, and agricultural management practices. The results show that identified CSAs changed over time and space due to tile drainage connectivity. Watershed-wide implementation of best management practices like reduced tillage, rather than targeting only identified CSAs, may be needed to meet pollution reduction goals. Future work includes updating the model with more recent data and climate change scenarios to further evaluate CSAs and best management strategies.
I. The project aims to evaluate the hill myna population in Kanger Valley National Park and identify factors influencing it through field surveys, population counts, and interviews with villagers.
II. Fieldwork from 2015-2016 found populations of 60-70 birds using point counts. Nesting was observed from January to April. Threats included human disturbance, trapping for pets, and habitat loss.
III. Future work includes continued monitoring, awareness campaigns, and identifying male and female plumages to aid conservation.
This study analyzes the relationship between farm management characteristics and the spatial distribution of landscape elements on farmland parcels in the Netherlands. The researchers collected data on farm management through farmer interviews and analyzed landscape element distribution using aerial imagery. They found that farm scale enlargement and migration processes, such as farms being taken over by hobby or part-time farmers, were important predictors of landscape element distribution. Understanding these relationships provides insights into threats and opportunities for conserving semi-natural habitats in agricultural landscapes.
Presented as the invited talk at International Workshop on kNowledge eXplication for Industry (kNeXI2017). In this talk, I explain the experience and lesson learnt how to build ontologies. I am currently building the agriculture activity ontology (AAO). It describes classification and properties of various activities in the agriculture domain. It is formalized with Description Logics.
The document describes the design process of the Agricultural Activity Ontology (AAO) in Japan. It involved surveying existing vocabularies, analyzing agricultural activity data, proposing an initial hierarchical structure, introducing description logics to define properties and relationships, and getting feedback from domain experts. The goal was to standardize vocabulary for agricultural IT systems to improve data sharing and integration. The AAO continues to be expanded with new terms and linkages based on additional data sources through a collaborative and iterative design process.
Now it is getting common for farmers to use IT systems to manage their activities. To realize incomparability among IT systems, we are building the vocabulary based on the agricultural activity ontology. The words in the vocabulary have logical definitions because the ontology is formalized based on description logic. As a result, the vocabulary has expendability to add new words and flexibility to generate custom vocabularies such like those for specific crops and regions.
The document discusses the CIARD (Coherence in Information for Agricultural Research for Development) initiative and how it aims to create a global infrastructure for linked open data. It describes how FAO has worked for decades to make agricultural information more accessible, including through programs like AGRIS and AIMS. The CIARD initiative now involves over 100 partners working to coordinate their efforts and promote common data formats and systems. It outlines FAO's work on vocabularies like AGROVOC and how linked open data can help link distributed data sources in agriculture through applying standards.
Agro-Know & the European agricultural research information ecosystemNikos Manouselis
The document discusses building a European data infrastructure for agricultural research information. It proposes connecting heterogeneous agricultural data sources to allow for unified querying. Semantic web technologies like linked open data would allow different communities to access the same data using their own vocabularies and ontologies. Challenges include querying very large distributed datasets and developing scalable semantic indexing. Potential collaborations are mentioned between the presenter's company, Agro-Know, and the Chinese Academy of Agricultural Sciences to share agricultural knowledge and research.
This document summarizes research using the Soil and Water Assessment Tool (SWAT) to model critical source areas (CSAs) of phosphorus pollution in the Sandusky River watershed in northwest Ohio. The SWAT model was set up with detailed data on the watershed's terrain, soils, land use, crops, and agricultural management practices. The results show that identified CSAs changed over time and space due to tile drainage connectivity. Watershed-wide implementation of best management practices like reduced tillage, rather than targeting only identified CSAs, may be needed to meet pollution reduction goals. Future work includes updating the model with more recent data and climate change scenarios to further evaluate CSAs and best management strategies.
I. The project aims to evaluate the hill myna population in Kanger Valley National Park and identify factors influencing it through field surveys, population counts, and interviews with villagers.
II. Fieldwork from 2015-2016 found populations of 60-70 birds using point counts. Nesting was observed from January to April. Threats included human disturbance, trapping for pets, and habitat loss.
III. Future work includes continued monitoring, awareness campaigns, and identifying male and female plumages to aid conservation.
This study analyzes the relationship between farm management characteristics and the spatial distribution of landscape elements on farmland parcels in the Netherlands. The researchers collected data on farm management through farmer interviews and analyzed landscape element distribution using aerial imagery. They found that farm scale enlargement and migration processes, such as farms being taken over by hobby or part-time farmers, were important predictors of landscape element distribution. Understanding these relationships provides insights into threats and opportunities for conserving semi-natural habitats in agricultural landscapes.
Presented as the invited talk at International Workshop on kNowledge eXplication for Industry (kNeXI2017). In this talk, I explain the experience and lesson learnt how to build ontologies. I am currently building the agriculture activity ontology (AAO). It describes classification and properties of various activities in the agriculture domain. It is formalized with Description Logics.
The document describes the design process of the Agricultural Activity Ontology (AAO) in Japan. It involved surveying existing vocabularies, analyzing agricultural activity data, proposing an initial hierarchical structure, introducing description logics to define properties and relationships, and getting feedback from domain experts. The goal was to standardize vocabulary for agricultural IT systems to improve data sharing and integration. The AAO continues to be expanded with new terms and linkages based on additional data sources through a collaborative and iterative design process.
Now it is getting common for farmers to use IT systems to manage their activities. To realize incomparability among IT systems, we are building the vocabulary based on the agricultural activity ontology. The words in the vocabulary have logical definitions because the ontology is formalized based on description logic. As a result, the vocabulary has expendability to add new words and flexibility to generate custom vocabularies such like those for specific crops and regions.
The document discusses the CIARD (Coherence in Information for Agricultural Research for Development) initiative and how it aims to create a global infrastructure for linked open data. It describes how FAO has worked for decades to make agricultural information more accessible, including through programs like AGRIS and AIMS. The CIARD initiative now involves over 100 partners working to coordinate their efforts and promote common data formats and systems. It outlines FAO's work on vocabularies like AGROVOC and how linked open data can help link distributed data sources in agriculture through applying standards.
Agro-Know & the European agricultural research information ecosystemNikos Manouselis
The document discusses building a European data infrastructure for agricultural research information. It proposes connecting heterogeneous agricultural data sources to allow for unified querying. Semantic web technologies like linked open data would allow different communities to access the same data using their own vocabularies and ontologies. Challenges include querying very large distributed datasets and developing scalable semantic indexing. Potential collaborations are mentioned between the presenter's company, Agro-Know, and the Chinese Academy of Agricultural Sciences to share agricultural knowledge and research.
AgroPortal is an ontology repository for agronomy, plant sciences, biodiversity and nutrition. It hosts 64 public ontologies, including 38 not present in other repositories. The objectives are to develop a reference repository, reuse the NCBO BioPortal technology, and enable use of agronomy ontologies. Key activities include publishing ontologies, searching/downloading, aligning ontologies, and recommending relevant ontologies for data. Future work includes hosting additional ontologies and developing ontology mapping capabilities.
The document discusses interoperability in agricultural information systems and the need for international collaboration. It describes how the Food and Agriculture Organization (FAO) of the United Nations is working to create standards and services like Agrovoc and the Agricultural Ontology Service to enable semantic interoperability across cultures and languages. The Coherence in Information for Agricultural Research for Development (CIARD) initiative aims to create a community of practice that promotes common standards, tools and methodologies to improve global access to agricultural information.
This document summarizes the activities of the Agricultural Data Interest Group (IGAD) at various RDA meetings between 2013-2017. It discusses the establishment of IGAD and several working groups focused on specific data types like wheat, rice, and farm data. It also outlines several deliverables produced by each working group, including standards, frameworks, and guidelines related to data management, sharing, and interoperability for different agricultural domains. Finally, it emphasizes that the RDA structure enables collaboration across geographic and topical divisions to address diverse data issues in agriculture.
The Crop Ontology is a controlled vocabulary for plant breeding data that aims to standardize terminology and enable data sharing and interoperability. It provides definitions and relationships for traits, phenotypes, experimental factors, and other relevant concepts. The ontology is being developed collaboratively by various crop centers and is accessible online. It is aligned with other related ontologies and being converted to semantic web formats to integrate with other plant data resources and enable linked open data.
This document summarizes a presentation given by Dr. Johannes Keizer of the Food and Agriculture Organization (FAO) of the United Nations. The presentation discusses the 10-year Agricultural Ontology Initiative to build a linked data infrastructure for agricultural knowledge. It provides an overview of FAO's AGROVOC concept scheme and other agricultural ontologies developed through partnerships. It also describes semantic tools and technologies used by the Agricultural Ontology Service (AOS) to link and aggregate agricultural data into a global linked open data cloud.
The document proposes creating an Agricultural Ontology Service (AOS) to better organize information in the domain of food and agriculture. The AOS would provide a centralized access point and knowledge organization framework to improve information retrieval, make relevant information sources more accessible, and allow for interoperability across domains. It discusses using ontologies and semantic web technologies to formalize relationships between concepts to help machines better understand information.
The document proposes creating an Agricultural Ontology Service (AOS) to organize agricultural knowledge. It would integrate existing thesauri and allow semantic searching. The AOS would have a registry of concepts with attributes, definitions, labels and relations. It would facilitate information sharing and reuse across organizations. Next steps include defining the structure, collaborations and maintaining the AOS through an iterative knowledge registration process.
The document summarizes the 10 year Agricultural Ontology Initiative led by Dr. Johannes Keizer of FAO. It discusses how FAO has worked to make agricultural knowledge available online through various initiatives like AGROVOC, developing agricultural ontologies, tools like the Concept Scheme Work Bench, and linking data across different sources to create a linked data infrastructure for agriculture. It provides examples of ontologies developed in domains like fisheries and partnerships with other organizations. The goal is to improve access and use of agricultural knowledge through semantic technologies and linked open data.
1. The document discusses issues with agricultural information systems like different user needs, multiple data sources, and lack of interoperability.
2. It proposes using shared vocabularies, ontologies, and application profiles like AGRIS AP and AgMES to enable semantic interoperability across systems through a common exchange layer.
3. The Agricultural Ontology Service aims to improve semantic search and access to agricultural knowledge resources by providing a registry and federated storage for vocabularies, ontologies, and other knowledge organization systems like AGROVOC.
FAO and UNESCO-IOC have collaborated to create a customized version of DSpace called AgriOcean DSpace to support open access to scientific information. It combines the OceanDocs repository network supported by IOC with the AGRIS DSpace repository used for FAO's AGRIS network. AgriOcean DSpace enhances the submission process and includes authority control features. Support and distribution is provided to members of the FAO and IOC repository communities. Future work includes developing a thesaurus plugin and integrating AgriOcean DSpace with the Virtual Open Access Agriculture & Aquaculture Repository project.
The document discusses the history and current status of the Agricultural Ontology Service (AOS) initiative launched by the Food and Agriculture Organization (FAO) in 2001. It summarizes that while the initial AOS prototypes were not realized as planned, awareness and research on ontologies and semantic technologies has grown significantly. Today, applications using ontologies are being developed for portals and specific domains. However, the AOS still requires a stronger institutional framework to coordinate standards and attract more funding and participation from various experts and organizations. The concept of a federated AOS to organize agricultural knowledge organization systems internationally remains valid.
The document discusses the vision for the Agricultural Ontology Service (AOS) which aims to improve access to agricultural information through shared semantic standards and interoperability. It proposes a consortium of data providers and information consumers to provide common ontologies, metadata schemas, and vocabularies. This would facilitate integrated access to distributed datasets and services while supporting collaborative development and promotion of semantic standards in agriculture. The FAO cannot drive this alone and seeks partners to advance this vision through agreed procedures and legal framework.
The document discusses the vision for the Agricultural Ontology Service (AOS) which aims to improve access to agricultural information through shared semantic standards and interoperability. It outlines some of the current challenges with fragmented information sources and portals. The vision is for AOS to act as a consortium and clearinghouse to agree on common ontologies, metadata schemas, and procedures to link distributed data sources and provide semantic search capabilities. Partnerships with different organizations are envisioned to further develop and promote semantic standards to better share agricultural knowledge.
The document discusses the need for international collaboration on interoperability in agricultural information systems. It describes various initiatives including Agrovoc, the Agricultural Ontology Service, and CIARD that aim to develop common standards and shared vocabularies to improve semantic interoperability across repositories and languages. The ultimate goal is to make agricultural research information and knowledge truly accessible on a global level through linked open data principles and distributed yet interconnected knowledge organizations.
This document summarizes a presentation by Dr. Johannes Keizer on information dynamics for agricultural research and development. The presentation discussed changing dynamics in how agricultural knowledge is aggregated, researched, distributed geographically, and accessed. It proposed linking previously isolated data sources to facilitate access to real-time information and encourage collaboration. Demonstrators including OpenAgris and linked open vocabularies were presented as examples of how semantic web technologies can help integrate agricultural information and knowledge.
Agroknow and FREME presentation @Linda workshop-20-11-2015Stoitsis Giannis
The document discusses how Agro-Know is using linked open data to enhance its agricultural information discovery services. It describes linking bibliographic and organization data from sources like AGRIS and ORCID to power facets for browsing and searching. Services like FREME are used to disambiguate and link author, organization and location information. The goal is to move from XML data to a linked data model and provide a more connected agricultural discovery experience.
AgroPortal is an ontology repository for agronomy, plant sciences, biodiversity and nutrition. It hosts 64 public ontologies, including 38 not present in other repositories. The objectives are to develop a reference repository, reuse the NCBO BioPortal technology, and enable use of agronomy ontologies. Key activities include publishing ontologies, searching/downloading, aligning ontologies, and recommending relevant ontologies for data. Future work includes hosting additional ontologies and developing ontology mapping capabilities.
The document discusses interoperability in agricultural information systems and the need for international collaboration. It describes how the Food and Agriculture Organization (FAO) of the United Nations is working to create standards and services like Agrovoc and the Agricultural Ontology Service to enable semantic interoperability across cultures and languages. The Coherence in Information for Agricultural Research for Development (CIARD) initiative aims to create a community of practice that promotes common standards, tools and methodologies to improve global access to agricultural information.
This document summarizes the activities of the Agricultural Data Interest Group (IGAD) at various RDA meetings between 2013-2017. It discusses the establishment of IGAD and several working groups focused on specific data types like wheat, rice, and farm data. It also outlines several deliverables produced by each working group, including standards, frameworks, and guidelines related to data management, sharing, and interoperability for different agricultural domains. Finally, it emphasizes that the RDA structure enables collaboration across geographic and topical divisions to address diverse data issues in agriculture.
The Crop Ontology is a controlled vocabulary for plant breeding data that aims to standardize terminology and enable data sharing and interoperability. It provides definitions and relationships for traits, phenotypes, experimental factors, and other relevant concepts. The ontology is being developed collaboratively by various crop centers and is accessible online. It is aligned with other related ontologies and being converted to semantic web formats to integrate with other plant data resources and enable linked open data.
This document summarizes a presentation given by Dr. Johannes Keizer of the Food and Agriculture Organization (FAO) of the United Nations. The presentation discusses the 10-year Agricultural Ontology Initiative to build a linked data infrastructure for agricultural knowledge. It provides an overview of FAO's AGROVOC concept scheme and other agricultural ontologies developed through partnerships. It also describes semantic tools and technologies used by the Agricultural Ontology Service (AOS) to link and aggregate agricultural data into a global linked open data cloud.
The document proposes creating an Agricultural Ontology Service (AOS) to better organize information in the domain of food and agriculture. The AOS would provide a centralized access point and knowledge organization framework to improve information retrieval, make relevant information sources more accessible, and allow for interoperability across domains. It discusses using ontologies and semantic web technologies to formalize relationships between concepts to help machines better understand information.
The document proposes creating an Agricultural Ontology Service (AOS) to organize agricultural knowledge. It would integrate existing thesauri and allow semantic searching. The AOS would have a registry of concepts with attributes, definitions, labels and relations. It would facilitate information sharing and reuse across organizations. Next steps include defining the structure, collaborations and maintaining the AOS through an iterative knowledge registration process.
The document summarizes the 10 year Agricultural Ontology Initiative led by Dr. Johannes Keizer of FAO. It discusses how FAO has worked to make agricultural knowledge available online through various initiatives like AGROVOC, developing agricultural ontologies, tools like the Concept Scheme Work Bench, and linking data across different sources to create a linked data infrastructure for agriculture. It provides examples of ontologies developed in domains like fisheries and partnerships with other organizations. The goal is to improve access and use of agricultural knowledge through semantic technologies and linked open data.
1. The document discusses issues with agricultural information systems like different user needs, multiple data sources, and lack of interoperability.
2. It proposes using shared vocabularies, ontologies, and application profiles like AGRIS AP and AgMES to enable semantic interoperability across systems through a common exchange layer.
3. The Agricultural Ontology Service aims to improve semantic search and access to agricultural knowledge resources by providing a registry and federated storage for vocabularies, ontologies, and other knowledge organization systems like AGROVOC.
FAO and UNESCO-IOC have collaborated to create a customized version of DSpace called AgriOcean DSpace to support open access to scientific information. It combines the OceanDocs repository network supported by IOC with the AGRIS DSpace repository used for FAO's AGRIS network. AgriOcean DSpace enhances the submission process and includes authority control features. Support and distribution is provided to members of the FAO and IOC repository communities. Future work includes developing a thesaurus plugin and integrating AgriOcean DSpace with the Virtual Open Access Agriculture & Aquaculture Repository project.
The document discusses the history and current status of the Agricultural Ontology Service (AOS) initiative launched by the Food and Agriculture Organization (FAO) in 2001. It summarizes that while the initial AOS prototypes were not realized as planned, awareness and research on ontologies and semantic technologies has grown significantly. Today, applications using ontologies are being developed for portals and specific domains. However, the AOS still requires a stronger institutional framework to coordinate standards and attract more funding and participation from various experts and organizations. The concept of a federated AOS to organize agricultural knowledge organization systems internationally remains valid.
The document discusses the vision for the Agricultural Ontology Service (AOS) which aims to improve access to agricultural information through shared semantic standards and interoperability. It proposes a consortium of data providers and information consumers to provide common ontologies, metadata schemas, and vocabularies. This would facilitate integrated access to distributed datasets and services while supporting collaborative development and promotion of semantic standards in agriculture. The FAO cannot drive this alone and seeks partners to advance this vision through agreed procedures and legal framework.
The document discusses the vision for the Agricultural Ontology Service (AOS) which aims to improve access to agricultural information through shared semantic standards and interoperability. It outlines some of the current challenges with fragmented information sources and portals. The vision is for AOS to act as a consortium and clearinghouse to agree on common ontologies, metadata schemas, and procedures to link distributed data sources and provide semantic search capabilities. Partnerships with different organizations are envisioned to further develop and promote semantic standards to better share agricultural knowledge.
The document discusses the need for international collaboration on interoperability in agricultural information systems. It describes various initiatives including Agrovoc, the Agricultural Ontology Service, and CIARD that aim to develop common standards and shared vocabularies to improve semantic interoperability across repositories and languages. The ultimate goal is to make agricultural research information and knowledge truly accessible on a global level through linked open data principles and distributed yet interconnected knowledge organizations.
This document summarizes a presentation by Dr. Johannes Keizer on information dynamics for agricultural research and development. The presentation discussed changing dynamics in how agricultural knowledge is aggregated, researched, distributed geographically, and accessed. It proposed linking previously isolated data sources to facilitate access to real-time information and encourage collaboration. Demonstrators including OpenAgris and linked open vocabularies were presented as examples of how semantic web technologies can help integrate agricultural information and knowledge.
Agroknow and FREME presentation @Linda workshop-20-11-2015Stoitsis Giannis
The document discusses how Agro-Know is using linked open data to enhance its agricultural information discovery services. It describes linking bibliographic and organization data from sources like AGRIS and ORCID to power facets for browsing and searching. Services like FREME are used to disambiguate and link author, organization and location information. The goal is to move from XML data to a linked data model and provide a more connected agricultural discovery experience.
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Presented at Journal Paper Track, The Web Conference, Lyon, France, April 15, 2018
https://doi.org/10.1145/3184558.3186234
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At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
Development and Application of Agriculture Ontologies
1. Interest Group on Agricultural Data (IGAD)
3 April, 2017, Barcelona, Spain
Development and Application of Agriculture Ontologies
Hideaki Takeda, Sungmin Joo
Daisuke Horyu, Akane Takezaki
National Agriculture and Food Research
Organization (NARO)
National Institute of Informatics (NII)
2. 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”
3. Define activity concepts
Define hierarchy
Seeding:
activity to sow seeds on fields for seed propagation.
Purpose: seed propagation
Place : field
Target : seed
Act : 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)
4. Formalization by Description Logics
Crop production activity
Crop growth activity
purpose:crop production
purpose:crop growth
Agricultural activity
Activity for control of
propagation
Activity for seed
propagation
purpose:control of propagation
purpose:seed propagation
Seeding
act : sow
target:seed
place:field
Activity for seed
propagation
Seeding
Designing of Agricultural Activity Ontology(AAO)
5. Differentiate concepts by property
purpose : seed propagation
place : paddy field
target : seed
act : sow
crop:rice
purpose : seed propagation purpose : seed propagation
place : field
target : seed
act : sow
Agricultural activity >…> Activity for seed propagation > Seeding
purpose : seed propagation
place : well-drained paddy field
target : seed
act : sow
crop:rice
Direct sowing of rice on well-drained paddy field Direct seeding in flooded paddy field
Well-drained paddy field < field paddy field < field
Designing of Agricultural Activity Ontology(AAO)
6. 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)
Designing of Agricultural Activity Ontology(AAO)
7. Polysemic concepts
[disjunction form]
[conjunction form]
Pudlling
Subsoil breaking
PulverizationLand preparation
Water retention
Activity for water
management
Land leveling
Polysemic
relationship
Pulverization by
harrow
purpose : pulverization
purpose : water retention
purpose : land leveling
Definition of agriculture activities with multiple purposes or other
properties.
Puddling
Designing of Agricultural Activity Ontology(AAO)
8. Water retention
Land leveling Pulverization
Puddling
Polysemic concepts (Protégé)
Designing of Agricultural Activity Ontology(AAO)
9. Synonym
Designing of Agricultural Activity Ontology(AAO)
Expressions in multiple languages are also represented as synonyms.
(It is important especially for non-English speaking countries)
10. 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」
?
suppression
of pest
animals
Infer the most feasible upper concept for the given constraints for a new words
11. 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)
suppression
of pest
animals
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
13. http://www.cavoc.org/ http://www.cavoc.org/aao
Web Services based on Agriculture Activity Ontology
• Version History
ver. 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.
14. 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]
15. 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
Puddling
Seeding
…
Converting
[system]
API
Puddling Activity
and sowing…
[system’]
Puddling
and seeding…
16. Working time survey on Census of agriculture and forestry
Automatic Statistical Processing Based on Agriculture Activity Ontology
Puddling
30 min.
Farmer #1
Puddling Activity
40 min.
Land preparation
50 min.
Farmer #2
Farmer #3
Puddling :
Average work time :
45min
Puddling
30 min.
Puddling
40 min.
Puddling
50 min.
Handwritten data,
Excel file ... Mapping by man
power
Census of agriculture and forestry
17. 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
Agriculture
Activity names
Accumulation and
integration
Census of
agriculture and
forestry
Core vocabulary
18. 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)
area
7.26
12.95
14.75
12.95
activity
代かき
2番耕起
代かき
3番耕起
work time
45min
125min
84min
98min
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
19. STEP 1 : Importing csv data
Automatic statistical processing system
Mapping properties for agriculture activities
Plowing #1
Plowing #2
Plowing #3
Fertilization
Topdressing
Weeding on the levee
Compost application
Rice reaping
Land grading
Levee repair
Applying herbicide
Ploughing with stubble cultivator
Plowing by plough
Rice transplanting
Seeding(etc)
Puddling
20. 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
21. 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)
22. 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
23. AGROVOC
Thesaurus
AGROVOC organizes words by synonym, narrower/broader, and related
relationship.
harvesting topping(beets)
baling
gleaning
mechanical harvesting
mowing
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.
28. 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)
baling
gleaning
mechanical harvesting
mowing
Thesaurus
(AGROVOC)
. . .
harvesting mechanical harvesting
manual harvesting
. . .
Inheritrelationship
between
siblings
Representation: ”Harvesting”
29.
30. 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
harvesting mechanical harvesting
manual harvesting
. . .
Harvest Harvest
Harvest
Inherit
byMachine
manually
+
+
Representation: ”Harvesting”
[Act]
Ontology
harvesting mechanical harvesting
manual harvesting
. . .
Representation: ”Harvesting”
[Means]
[Means]
31. 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)
32. 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
33. 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 …
35. http://cavoc.org/
Common Agricultural VOCabulary
Agriculture Activity Ontology (AAO) ver 1.31
http://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.