1 / 8

Ontology ranking

Ontology ranking. What is ontology ranking? Selecting ontologies Evaluating ontologies When ranking algorithm is developed Evaluation of the algorithm Our specific case OntoFinder /Factory Input: Set of relevant terms Output: set of ontologies

Download Presentation

Ontology ranking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Ontology ranking • What is ontology ranking? • Selecting ontologies • Evaluating ontologies • When ranking algorithm is developed • Evaluation of the algorithm • Our specific case • OntoFinder/Factory • Input: Set of relevant terms • Output: set of ontologies • Rank the ontologies according to input terms.

  2. Selection of the previous methods

  3. Park • Relation Match Measure (RMM) defined as a combination of: • Concept match (exact match, partial match, synonymous match) • Relation label match: degree of correspondence between relation between search terms, and relation between concepts matched by search terms. • Distance: minimum path length between concepts (direct match = directly connected). • Neighbourmatch: can domain and range concept be connected with the help of their neighbour nodes in addition to their original linka?

  4. Park - RMM

  5. Martinez-Romero • Martinez-Romero et al.: • Expansion of input terms with WordNet, UMLS. • Weights for each metric was recommended by experts. • Previous approaches have 4 main drawbacks: 1. not completely automatic, 2. input is restricted to a single word, 3. popularity is not considered or not correctly assessed. 4. Semantics from relations in ontologies are ignored. • Three metrics again (coverage, richness, popularity) • No word disambiguation (compared to AKTivRank).

  6. Martinez-Romero

  7. Evaluation of previous methods

  8. OntoFinder Challenges • Fact: No “perfect algorithm” for evaluation/ranking. • Which metrics to use and how to weight them? • Metrics and weights from previous works? • Define new/improve old metrics: • Coverage - Improve string similarity? • Until now: exact match, partial match, synonyms, longest only, edit distance, n-Grams (?). • We: Head word and Other string similarity measurements • Add different metrics for popularity? • Number of Views on Bio Portal? • PubMed references? • How to evaluate results - algorithm? • Human evaluators? • Automatic evaluation method? (Brank et al. 2006) • Ontology based annotation: • Select ontologies -> Annotate -> Compare result with “golden standard” (CRAFT?) – How? • ML approach (?)

More Related