Semantic Web Technologies – Ontological Engineering

Summary of week five for the course Knowledge Engineering with Semantic Web Technologies 2015 by Harald Sack at OpenHPI.

Ontology Engineering

Pyramid for knowledge management:

  • Data: raw data, facts about event
  • Information: a message to change the receiver’s perception
  • Knowledge: experience, context, information + semantics
  • Wisdom: application of knowledge in context

In general it makes sense to follow some methodologies because creating an ontology is quit complex.

Ontology Learning

Can we create ontologies automatically? Ways to do this:

  • via text mining from text
  • via linked data mining from e.g. RDF graphs
  • concept learning in Description Logics and OWL (related to linked data mining, but also)
  • crowdsourcing via Amazon Mechanical Turk or games with purposein short there are three steps: term extraction – conceptualization – evaluation. Actual challenges in Ontology Learning:
  • Heterogenity
  • Uncertainty: the quality is low, you cannot be sure whether the information is right or not
  • You need consistency because otherwise you cannot do reasoning
  • Scalability: make sure that it is scalable
  • Quality: you neet to evaluate it and make sure it is right
  • Interactity: you need to involve users to help you improve the ontologies

Ontology Alignment
What is it? You try to find similarities between ontologies in order to combine them. But: an ontology only models reality, it is NOT the reality. The problems are similar to natural language: you run into ambiguities. You can also have problems with different conventions (time in seconds vs. time in time points), different granularities and different points of views.

You have differences on the syntactical, terminological. semantical or semiotic (pragmatic) level

Ontology Evaluation

This is the quality of an ontology in respect to a particular criterion. There are two basic principles:

  • Verification: it encoding and implementation correct (more the formal side)
  • Validation: how good is the model and how well does it match reality?

Criteria for validation:

  • correctness:
    • Accuracy (precision and recall)
    • completeness
    • consistency
  • quality:
    • Adaptibility
    • Clarity
    • computational efficiency
  • organizaional fitness (how well does it integrate in my software/organisation
  • conciseness