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Ontology Alignment

Opportunities

We are not yet where we have agreement on ontologies to handle the range of heterogeneous information in the Big Data age. While we have seen numerous efforts to create domain ontologies as well as overlapping domains the vocabularies and ontologies behind various data sources remain disconnected. General methods to merge and align ontologies include such things as PROMPT, an algorithm for semi-automatic merging and alignment of ontologies [Noy and Musen, 2000]. But as noted more recently in Ontology Summits and related session [cf Ontology for Big Systems: The Ontology Summit 2012 Communique, and Stephan and Hahmann, 2016], there are issues in reconciling and aligning ontologies with different assumptions and concepts. There is work on ontology integration[Euzenat, 2004] which has produced algorithms and heuristics with some success in making such computations tractable. However, as noted by [Udrea and Getoor, 2007] the effective use of ontology formalisms (i.e., rules and axioms) as part of an integration process "remains an open question."

Challenges

One of the things that makes the ontology integration process difficult is that as part of the process we need to understand the relationship between knowledge structures (classes and properties) and instance data in target ontologies. Existing ontology matching and alignment techniques are very restricted. They find similarities, equivalences and sub-sumption relations between two (or more) ontologies given that they are:

  1. syntactically and schematically integrated.
  2. of similar scope &
  3. no more expressive than OWL.

In contrast semantic integration between existing domains like hydrology, its ontologies and schemas additionally requires:

  1. Translation between ontology languages.
  2. More rigorous specification of the semantics in each ontology.

This can currently be done only by manual integration of the ontologies, but use of a suitable reference ontology may automate this Stephen & Hahmann. In addition, integration must have the capacity to use the semantics of the ontology to model these relationships, and create a coherent and consistent integrated or aligned ontology. Another abiding source of difficulty for matching parts of ontologies is these are designed with certain background knowledge (axiomized or not) and in built in a certain context, which includes the experience of different ontologists, their preferece for particular upper level ontologies, domain vocabularies, ontology design patterns or source data to model from. These may not become part of anontology specification, and, thus, are not available to aligning tools or entity/relation matchers. This lack of background knowledge leads to ambiguities [Shvaiko & Euzenat, 2008]

Future Prospects

The Ontology Alignment Evaluation Initiative (OAEI) continues to run contests on ontology alignment. The 2016 campaign for example challenged ontology matchers with a robust set of ontology and data sources to be matched. For example in the anatomy real world case was to match the Adult Mouse Anatomy (2744 classes) and the NCI Thesaurus (3304 classes) describing the human anatomy. As in past campaigns, they use a systematic benchmark series to be matched. The work of this benchmark series has been to identify the areas in which each alignment algorithm is strong and weak.

References

  • Euzenat, Jérôme. "An API for ontology alignment." International Semantic Web Conference. Springer Berlin Heidelberg, 2004.
  • Noy, N. F., Musen, M. A., PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment, Proceedings of Seventeenth National Conference on Artificial Intelligence (AAAI-2000), Austin, TX, 2000.
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