Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development


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Presentation at the workshop on ontology quality at EKAW 2010, on using measures of agreement, disagreement, consensus and controversy to support ontology assessment in ontology engineering.

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  • Add ref to original paper.
  • Examples of what we can do from there…
  • The graph shows some notion of distance based on agreement (it is not “exact” as this sort of things cannot really be calculated this way without 20 dimensions, but it gives an idea, and shows the main viewpoints).The groups are only some of the explanations (prominent examples).. There are a lot of others.O11 o13 and 012 don’t say anything about SeaFood and MeatOthers that o9 and o11 don’t say anything about Vegans and Vegetarian There are other elements of explanations… but these are the main ones
  • Using this for ontology engineering
  • Our visualization technique (with visual examples).
  • Screenshot + warning…
  • Future work includes finalizing the tool and making some user evaluation. Everybody interested in contributed in some concrete projects?Improve time responses (cache) and interaction with the user (e.g., obtain the explanation directly in the visualization).
  • Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development

    1. 1. Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development<br />Mathieu d’Aquin and Enrico Motta<br />Knowledge Media Institute, The Open University,UK<br />
    2. 2. Ontologies are knowledge artifacts, they express opinions and beliefs and contradict each others<br />Assessing (dis)agreement in ontologies is very useful to understand how to combine knowledge from different sources<br />In [1], we defined measures of agreement, disagreement, consensus and controversy between ontologies and ontological statements<br />Here, we show how such measures can be useful in providing an overview of an ontology in development, with respect to the “opinion” of other ontologies on the Web<br />Agreement and Disagreement in Ontologies<br />[1] d’Aquin, M.: Formally measuring agreement and disagreement in ontologies. In: International Conference on Knowledge Capture - K-CAP. (2009)<br />
    3. 3. Agreement(st, O)  [0..1] and Disagreement(st, O)  [0..1] where stis a statement <subject, predicate, object> and O is an ontology<br />Based on extracting the part of the ontology that expresses a relation between subject and object<br />(Dis)agreement between ontologies: <br />Global (dis)agreement in a repository<br />Consensus: <br />Controversy:<br />Agreement and Disagreement - Measures<br />
    4. 4. What can we do with that?<br />Consensus and controversy in statements about SeaFood in Watson<br />Agreements and disagrements between ontologies containing SeaFood in Watson<br />
    5. 5. Vegan subClass Vegetarian<br />SeaFoodsubClassOf Meat<br />SeaFooddisjointWith Meat<br />
    6. 6. Agreement, disagreement, consensus and controversy for ontology engineering<br />Making use of these measures to support ontology engineering<br />By providing an overview of the developed ontology introducing the “perspective of other ontologies”<br />I.e., using Watson to measure the global agreement/disagrement and consensus/controversy with online ontologies.<br />And show that in an easy to use visualization.<br />
    7. 7. Visualization<br />We show the values of the 4 measures wrt to Watson ontologies using color coding on the edges of a graph representing the statements in the ontology<br />Node colors correspond to the average color of their incoming and outgoing edges<br />Color=agreement/disagreement <br /> blue=agreement red=disagreement green=don’t know<br />Brightness=consensus/controversy<br />bright blue=+consensus bright red=-consensus dark=controversy<br />
    8. 8. Example: AKT Support<br />Bright blue = high positive consensus<br />Example: Duration subClassOfIntangibleThing<br />Bright red= low negative consensus<br />Example: Duration subClassOfPhysicalQuantity<br />(Cyc, SUMO and ATO_Ontology disagree as they do not declare such a relation, or even say these two classes disjoint)<br />
    9. 9. Example: Local Drama Ontology<br />Node color = aggregation of edges colors<br />Example: Organization where there is agreement (Company is a subClass) and disagreement (Archive is a subClass)<br />Green = we don’t know as no other ontology declares anything about two entities<br />Character subClassOf Person<br />
    10. 10. The brighter the blue the higher the positive consensus (higher agreement)<br />The brighter the red the lower the negative consensus (higher disagreement)<br />Dark = controversy: no clear cut between disagreement and agreement<br />Example: The statement attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles)<br />Using consensus to assess an ontology(a new NeOn toolkit plugin<br />AKT Portal<br />
    11. 11. NeOn Toolkit Plugin<br />
    12. 12. Conclusion and Future Work<br />Visualizing agreement/disagreement and consensus/controversy can be very useful to identify parts of the ontology where a stronger focus is needed in ontology engineering <br />Initial results show that, even in well designed ontologies questions can emerge from such visualizations<br />The tool need a lot of technical work to make it more usable in terms of:<br />Performance (the measures are hard to compute and depend on internet connection)<br />Interaction with the user (e.g., give better explanations for the results)<br /> A complete user evaluation showing how this can guide the “refinement” of an ontology and improve its quality<br />
    13. 13. Thank You!<br /><br />@mdaquin<br />