scientific communication infrastructure – part 2...cooperation on heterogeneous object...
TRANSCRIPT
WissKIScientific Communication Infrastructure
- Part 2 -WissKI Scientific Communication Infrastructure
– Part 2 –
Guenther Goerz
Univ. of Erlangen-Nuremberg, Comp. Sci. Dept. &
Max Planck Institute for the History of Science, Berlin
Knowledge and Reasoning– The Epistemic Level –
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
• e.g., CIDOC’s Conceptual Reference Model (ISO 21127)
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
• e.g., CIDOC’s Conceptual Reference Model (ISO 21127)
• extended with domain ontologies, thesauri, and authority files (controlled vocabularies)
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
• e.g., CIDOC’s Conceptual Reference Model (ISO 21127)
• extended with domain ontologies, thesauri, and authority files (controlled vocabularies)
• Prerequisite for semantic annotation / indexing, but...
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
• e.g., CIDOC’s Conceptual Reference Model (ISO 21127)
• extended with domain ontologies, thesauri, and authority files (controlled vocabularies)
• Prerequisite for semantic annotation / indexing, but...
• Semantics comes in with a reasoning framework
Knowledge and Reasoning– The Epistemic Level –
• Need: Infrastructure for interactive and net-based cooperation on heterogeneous object documentation
• Building conceptual models => curated knowledge
• formal (reference) ontologies
• e.g., CIDOC’s Conceptual Reference Model (ISO 21127)
• extended with domain ontologies, thesauri, and authority files (controlled vocabularies)
• Prerequisite for semantic annotation / indexing, but...
• Semantics comes in with a reasoning framework domain
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Semantic Web-----------------------------------
Linked Open Data
The WissKI Approach
4
The WissKI Approach
4
• System design: Value added open source CMS (Drupal), upgraded by semantic components
The WissKI Approach
4
• System design: Value added open source CMS (Drupal), upgraded by semantic components
• WissKI relies completely on semantic technologies
The WissKI Approach
4
• System design: Value added open source CMS (Drupal), upgraded by semantic components
• WissKI relies completely on semantic technologies
• WissKI is build on a LAMP Web-Stack
The WissKI Approach
4
• System design: Value added open source CMS (Drupal), upgraded by semantic components
• WissKI relies completely on semantic technologies
• WissKI is build on a LAMP Web-Stack
• WissKI modules are programmed in PHP
The WissKI Approach
4
• System design: Value added open source CMS (Drupal), upgraded by semantic components
• WissKI relies completely on semantic technologies
• WissKI is build on a LAMP Web-Stack
• WissKI modules are programmed in PHP
• CIDOC CRM is the semantic backbone
System Architecture
5
Software Infrastructure
6
The WissKI Stack
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Reasoning Services
17 (c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features• Consistency: find contradictions in data
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features• Consistency: find contradictions in data• Classification: compute class hierarchy
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features• Consistency: find contradictions in data• Classification: compute class hierarchy• Realization: find instances for each class
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features• Consistency: find contradictions in data• Classification: compute class hierarchy• Realization: find instances for each class
• Conjunctive query answering via SPARQL (OWL entailments)
(c) Parsia
Reasoning Services
17
• Access to information no directly retrievable from the database structure; inference with conceptual reasoning
• Inference engine: Pellet
• Standard reasoning features• Consistency: find contradictions in data• Classification: compute class hierarchy• Realization: find instances for each class
• Conjunctive query answering via SPARQL (OWL entailments)
• Explanation generation(c) Parsia
18
• http://wiss-ki.eu/
• http://www.facebook.com/wisskiproject (Information, Discussion, Tutorials)
• http://erlangen-crm.org/ (OWL implementation of the CIDOC CRM and of FRBRoo)
• http://traid.gnm.de/ Transdisciplinary Approaches in Documentation (TRAID) Working Group Website
Text Annotation
Query Patterns(Constantopoulos et al., 2009)
Query Patterns(Constantopoulos et al., 2009)
• Initial set of recurrent questions from empirical studies in the domain of cultural heritage
• Query patterns represent important questions; expose dominant information requirements
• Provide guidance to users interested in posing complex questions about objects
• Support effective user interaction and efficient implementation of query processing (Datalog rules, SPARQL queries) => Description Logics
• Access via user-friendly forms