Scientific Communication Infrastructure – Part 2...cooperation on heterogeneous object...

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  

Semantic Web ...!"#$%&'()*"+)!&$(,)

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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

7

8

9

10

11

12

13

14

15

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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