The document discusses building and navigating knowledge maps. It describes identifying concepts and relationships between them to build an ontology, which can then be stored in a triple store and used to infer new relationships. Users can navigate the knowledge map based on their context and interests to find relevant information and attractions. Tools mentioned for building knowledge maps include Protege, Fluent Editor, Apache Jena, GraphDB and MarkLogic triple stores.

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Navigating through the Knowledge
Presenter: Alex Masycheff, CEO, Intuillion

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Meet Jim
• Jim the Traveler
• Likes lakes and rivers
• Came to a place he’s never been
• Wants to visit a place with a lake

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Mental Picture

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Mental Picture
“How can I get to the City Park?”

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Reality
Oops, no lakes!

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Unknown Knowledge Gaps
• Doesn’t know about the possibility
• Won’t ask
• Will never learn about the possibility

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Example: Laptop Privacy Filter
Did you know it exists?

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Tourist Attractions Domain
Museum
City Park
City Park
Playground
Type Goal
Have fun
Learn
Walk
Eat
Category
Archaeology
History
Science
Music
Transport
Food
Location
Downtown
North
South
West
East

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Knowledge Map: Concepts and Relations
Attractions from the audience perspective
Attractions from the goal perspective
Attractions from the location perspective

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Relationships between Concepts
National Park
South Families
Lakes
z
Mountains
National Park is located on the South. It’s good for families. It contains mountains and lakes.

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Relationships between Concepts
City Park
Downtown Families
Playground
z
Bicycle track
City Park is located in the Downtown. It’s good for families. It contains playgrounds and
bicycle tracks.

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Relationships between Concepts
Jim
South Traveler
Lakes
z
History
Jim is a traveler. He is interested in lakes and history. His hotel is located on the South.

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RDF Triple Stores
• Knowledge maps (ontologies) are stored in
RDF triple stores
• RDF triple store: database for storing and
retrieving semantic triples
• SPARQL: query language for retrieving
semantic triples
• RDF triple stores provide computed
inference

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Inference: Discovering New Relations
Jim is a traveler. He is interested in lakes and history. His hotel is located on the South.
City Park is located in the Downtown. It’s good for families. It contains playgrounds and
bicycle tracks.
National Park is located on the South. It’s good for families. It contains mountains and lakes.
Computed Inference:
Jim might be interested in the National Park.

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Relationships between Concepts
The City Park is good for families.
City Park
Families

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Relationships between Concepts
City Park
Bicycle track
There’s a bicycle track in the City Park.
Families

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Relationships between Concepts
City Park
Bicycle track
There’s a bicycle track in the City Park.
Families

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Relationships between Concepts
City Park
Bicycle track
There’s a bicycle track in the City Park.
Families
Computed Inference:
The bicycle track is good for families.

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Relationships between Concepts
Confidentiality
Privacy filter is a laptop accessory.
Privacy filter ensures confidentiality.
Filter
Laptop
Laptop
Business
Travel
Laptop is good for travel.
Joanne
Business
Traveler
Joanne is a business traveler. She is concerned about confidentiality.
Confidentiality

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User’s Context
Walking Riding bicycleLike:
Travels with: Family
Stays in: Downtown
• Granular
• Determines content to be offered
• Defines questions to be asked

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Navigation through Knowledge Map
Learning Having Fun Walking Eating

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Navigation through Knowledge Map
Zoo City Park
Learning EatingHaving Fun Walking

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Navigation through Knowledge Map
Zoo City Park
Learning EatingHaving Fun Walking
Downtown
Located
South
Located

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Navigation through Knowledge Map
Zoo City Park
Learning EatingHaving Fun Walking
Downtown
Located
South
Located Playground
Running trail
Bicycle track

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Navigation through Knowledge Map
Zoo City Park
Learning EatingHaving Fun Walking
Downtown
Located
South
Located Playground
Running trail
Bicycle track
Rent-a-Bike
Canberentedfrom

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Navigation through Knowledge Map
Zoo City Park
Learning EatingHaving Fun Walking
Downtown
Located
South
Located Playground
Running trail
Bicycle track
Rent-a-Bike
Canberentedfrom
Address
PriceOffered for

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Semantic Triples and Content
Subject=“City Park” Content metadata
City Park
Downtown Families
Playground
z
Running trail
Bicycle track

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Semantic Triples and Content
Subject=“Bicycle Track” Content metadata
Inversed relationship
City Park
Downtown Families
Playground
z
Running trail
Bicycle track

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How to Build a Knowledge Map
• Identify concepts and relationships between them:
• Can be extracted from the content (corpus)
• Can be identified with competency questions
• What attractions are good for families with kids?
• What are Japanese restaurants in the area?
• What is a good place for biking?
• Top-bottom, bottom-up approaches
• Identify instances
• Ontology 101, Stanford University paper:
https://protege.stanford.edu/publications/ontology_development/ontology101.pdf

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Some Tools
• Ontology editors:
• Protégé
• Fluent Editor
• RDF triple stores:
• Apache Jena
• GraphDB
• MarkLogic
• Stardog

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Knowledge Map Value
• Can be used by different users and applications within the
organization
• Unifies and standardize knowledge
• Allows for machine processing of the corporate knowledge

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Website: http://www.intuillion.com
Email: alex@intuillion.com
Contact Me