Cultural Tour

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Cultural Tour Applied to the Cultural Heritage
Sectorfor
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(No Transcript)
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The Potential of Semantic Web Technology

• Enable a paradigm switch in searching information
• From
• Information Retrieval
• To
• Question Answering
• This work illustrates an application in this line
  for one particular domain

Forward
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Google Federico García Lorca
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Archivo Virtual Federico García Lorca
Spain member Organizations
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The Potential of Semantic Web Technology

• Enable a paradigm switch in searching information
• From
• Information Retrieval
• To
• Question Answering
• This work illustrates an application in this line
  for one particular domain

Forward
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Federico García Lorca
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A Semantic Portal for the Spanish Silver Age
How Does it Work? Ontology of Cultural
Content Knowledge Acquisition Exploitation Conclus
ions
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The Overall Process
How does it work?
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Ingredients
How does it work?

• Multiple, heterogeneous sources
• Ontology
• Knowledge Acquisition Engine
• Knowledge Parser
• R2O and ODEMapster
• Exploitation of knowledge
• Publishing the results
• Duontology
• Semantic Navigation
• Hyperlink-based navigation
• 3D navigation
• Semantic Search Engine
• Keyword based
• NLP queries
• Enriched documents with ontological information

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A Semantic Portal for the Spanish Silver Age
How Does it Work? Ontology of Cultural
Content Knowledge Acquisition Exploitation Conclus
ions
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Ontology of Cultural Content
Construction

• Constructed in collaboration with experts from
  Residencia de Estudiantes
• Inspired by IFLA and MARC, and also based on
  general ontologies like SUO and Cyc
• Ontology metrics (after population)
• 64 concepts
• 91 properties
• 60.000 instances
• 60.000 facts
• 40Mb in RDF(S) files

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Ontology of Cultural Content
Illustration
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A Semantic Portal for the Spanish Silver Age
Semantic Portal Definition Ontology of Cultural
Content Knowledge Acquisition Exploitation Conclus
ions
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The Sources
Knowledge Acquisition
Residencia de Estudiantes
ULAN
Toponyms and Persons
Supervision
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ULAN United List of Artist Names
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The Sources
Knowledge Acquisition
Residencia de Estudiantes
ULAN
Toponyms and Persons
Supervision
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Residencia de Estudiantes. Revistas
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Knowledge Parser Architecture
Knowledge Acquisition

• Source Pre-processing
• Information identification
• Ontology population

PluggableStrategies
Pre-ProcessingTypes
Intelligent Population of Ontologies
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Different Types of Pre-processing
Knowledge Acquisition

• Plain Text Model
• Regular Expression Check and Retrieval
• Offset References
• DOM/Hypertext
• HTML object identification
• HTTP control and navigation
• NLP
• Basic NLP Tokenizer, Morphology and Chunk
  Parsers
• Retrieve phrases using head driven approach
• Basic semantic relations (synonyms, hyponyms,
  etc.)
• Layout
• Rendered result of a HTML source (X,Y)
  coordinates
• Visual Operators SAME_ROW, NEAR, etc

Back
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Knowledge Parser Architecture
Knowledge Acquisition

• Source Pre-processing
• Information identification
• Ontology population

PluggableStrategies
Pre-ProcessingTypes
Intelligent Population of Ontologies
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Explicit Extraction Knowledge
Knowledge Acquisition

• Wrapping ontology
• Documents
• Pieces
• Relations
• Semantic
• Layout
• Data Types
• Meaning
• Basic Types
• HTML

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Operators and Strategies
Knowledge Acquisition

• Operators
• Check data types, relations, constraints
• Retrieve obtains piece or document
  (precondition)
• Execute navigate, select, etc
• Strategies (operators applied for hypothesis
  construction)
• Greedy quick but not optimal
• Heuristics hypothesis construction and pruning
• Optimal Backtracking covering all search space

Back
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Knowledge Parser Architecture
Knowledge Acquisition

• Source Pre-processing
• Information identification
• Ontology population

PluggableStrategies
Pre-ProcessingTypes
Intelligent Population of Ontologies
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Ontology Population
Knowledge Acquisition

• Actions
• Create new instance
• Modify existing instance
• Remove existing instance
• Relate existing instances
• Process
• Hypothesis evaluation
• Population simulation
• Lowest cost simulation algorithm

Back
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A Semantic Portal for the Spanish Silver Age
Semantic Portal Definition Ontology of Cultural
Content Knowledge Acquisition Exploitation Conclus
ions
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Publishing in a Semantic Portal
Exploitation
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Traditional Publishing
Exploitation
Semantic Web Publication Semantic Portal Need
for SW information publication on WWW (for humans)

• Inconveniences of direct publication/translation
• Semantic model is not necessary user-friendly
  (relations, control attributes)
• Interface change entails model change
• Model publication is not always desired

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Decoupled Publishing
Exploitation
Visualization is independent from the Semantic
Model
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Semantic Navigation
Exploitation

• Example Federico García Lorca
• Returns instances
• Allows reference consulting

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Semantic Navigation and Annotation. Onto-H
Exploitation

• Browsing between ontology and sources

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New Ways of Visualising Semantic Web Content
Exploitation

Visualization Ontology
Visualization
Domain Ontology
ARTGALLERY
GRAPH
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New Ways of Visualising Semantic Web Content
Exploitation
PEOPLE
PERSON
CREATION
PLACES
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New Ways of Visualising Semantic Web Content
Exploitation
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A Semantic Portal for the Spanish Silver Age
Semantic Portal Definition Ontology of Cultural
Content Knowledge Acquisition Exploitation Conclus
ions
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Conclusions

• Towards a paradigm switch in searching?
• Detailed failure analysis needed. Why does Search
  Engine fail?
• KA limitation
• Not in ontology
• Missing/wrong instances
• Query construction
• NLP result (ambiguity)
• SeRQL query construction