Margherita Sini, FAO 1 / 33 http://www.agropedia.net Knowledge Management in Agropedia Indica Pantnagar Workshop Margherita Sini (FAO) 31 July ‘08 – 2 August ‘08
Margherita Sini, FAO 1 / 33
http://www.agropedia.net/
Knowledge Management in Agropedia Indica
Pantnagar Workshop
Margherita Sini (FAO)
31 July ‘08 – 2 August ‘08
Margherita Sini, FAO 2 / 33
http://www.agropedia.net/
Outline• Knowledge Management & Knowledge Modeling
• Knowledge Maps
• Representation languages
• Metadata
• AOS
• Agropedia Indica
• What’s next
• Take home messages
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Before we start... (1/3)• Data: numbers, words, images symbols as
measurements or observations of a set of variables.• Information: data that are processed to be useful.
Contextualized data. Information includes human participation in the organization of raw data.
• Knowledge: (Oxford Dictionary) “expertise, and skills acquired by a person through experience or education”. Involve expressing, sharing, processing. Intuitions! Confident understanding of a subject with the ability to use it for a specific purpose if appropriate.
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INFORMATIONDATA
Before we start... (2/3)
105.7
white Variety Color Size of grain
Botan Rice White 2.5mm length
Jasmine Rice
Brown Rice 6mm length
KNOWLEDGEIf I want to cook an Idli, I need to use a white Short-Grain rice such as Rambhog Rice.
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Before we start... (3/3)• Knowledge
– Explicit knowledge: Explicit knowledge is knowledge that has been or can be articulated, codified, and stored in certain media (@Wikipedia)
– Tacit knowledge: resides in one’s mind!
• The AAA paradigm– Anybody and say Anything about Any topic.
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Knowledge Management• Create, capture, analyse, apply, and reuse
knowledge;
• The process responsible for gathering, analysing, storing and sharing knowledge and information;
• It involves the design, review and implementation of both social and technological processes;
• Making it available to others;
K from Peoples!K from Peoples!
K for Computers!K for Computers!
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Knowledge Modeling• Create knowledge models
is white
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Some definitions (1/3)• Concepts / Classes: idea; thought;
• Individuals / Instances: objects of some type;
• Relations / Relationships / Properties / Links: ways that classes and objects can be related to one another;
• Attributes: properties, features, characteristics, or parameters that objects (classes or instances) can have;
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Some definitions (2/3)
Concept
Instance
Rambhog Rice
Link / Relationship
Rambhog Rice is a type of Rice
Attributes
• color• size• trade name• hasDaysOfApplicationAfterSowing• hasDaysOfApplicationBeforeSowing
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Some definitions (3/3)• Relationships types:
– linking concepts:• Hierarchical: subClassOf (are)
• Associative: objectProperty
– linking concepts to instances:• Is a type of (is a)
– linking concept to values:• dataTypeProperty
Rambhog Rice
Rice
is a (type of)
white
Rice
hasColor (dataTypeProperty)
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Thesaurus and other KOS• Knowledge Organization Systems
– Classification schemes– Subject lists– Gazetteers– Glossaries– Thesauri
• Thesaurus: is a structured controlled vocabulary
• Term based
• Maybe multilingual
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Knowledge Maps / Concept Maps• Different types
• Should address a specific problem
• In one or multiple languages
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Taxonomy Map (are)
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Partitivity Map (hasPart or isPartOf )
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Attribute Map (hasAttribute or hasValue)
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Process Map (usesProcess)
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Concept Map
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Concept Map
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Topic Map• topics - representing any concept (e.g. people,
countries, organizations, software modules, individual files, events)
• associations - which represent the relationships between topics
• occurrences - which represent relationships between topics and information resources relevant to them
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Ontologies• More rich KM
• Rules, axioms, ...
• Ontology editors: Protégé, NeOn Toolkit, Swoop
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Representation languages• Technologies for Knowledge Management
– machine interpretability (processed by applications)– standardized– bases of semantic web
• Types– RDF and RDFS– OWL: add formal semantics – Topic Map: ISO standard (XML)
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Metadata• When you describe a given
object (a book, a person, a product, etc.) with some information, you are actually using metadata: any type of description of an object or resource can be considered metadata.
• A set of elements used to describe an object is called the metadata element set.
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Knowledge Modeling in Agriculture
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Knowledge Modeling in Agriculture• AOS
• AGROVOC
• AGROVOC Concept Server
• Agropedia KMs
• Agropedia Indica
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AOS
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AGROVOC Thesaurus
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AGROVOC Concept Server WB
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Agropedia KMs (1/2)
CMap Editor.
Generic model(one only)
Specific models
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Agropedia KMs (2/2)
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Agropedia Indica
this is a documentabout rice and its
pests.....
Once the rice ap-pear
in the world .....
Mad Cow Disea-se is the commonly
used name for Bovine
Spongiform Encephalopathy
(BSE) ....
results.....
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What’s next• Now it’s up to you...:
– Build more KMs!– Add content! (indexing)
• Agropedia Team:– Translate KMs in Hindi, Telegu, and others– Complete the infrastructure– Enrich services
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The original problem...
KNOWLEDGEIf I want to cook an Idli, I need to use a white Short-Grain rice such as Rambhog Rice.
color
rice
recipesize
usedToCook
hasColor
hasSize Jasmine
white brown
Idli
6mm 2.5mm
hasInstance
usedToCook
hasSize
Rambhog
hasColor
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Take home messages• Knowledge Maps to express your knowledge• Concepts, Instances, Relationships, Attributes• Relationships: hierarchical, associative
(objectProperties), for values (dataTypeProperty)• Ontology languages for computer consumption• Metadata to describe resources• AOS as pool of services• Agropedia Indica to publish and share your
knowledge and resources
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References• KM: http://www.epistemics.co.uk/Notes/90-0-0.htm
• CMap:http://cmap.ihmc.us/Publications/ResearchPapers/TheoryCmaps/TheoryUnderlyingConceptMaps.htm
• RDF / RDFS: http://www.w3.org/TR/rdf-schema/
• OWL: http://www.w3.org/TR/owl-features/
• Metadata:ftp://
ftp.fao.org/gi/gil/gilws/aims/references/flyers/metadata_en.pdf
• AGROVOC and AOS: http://www.fao.org/agrovoc/
• AGROPEDIA: http://www.agropedia.net