Weighted Pseudo-distances for Categorization in Semantic Hierarchies

  • Cliff A. Joslyn
  • William J. Bruno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3596)


Ontologies, taxonomies, and other semantic hierarchies are increasingly necessary for organizing large quantities of data. We continue our development of knowledge discovery techniques based on combinatorial algorithms rooted in order theory by aiming to supplement the pseudo-distances previously developed as structural measures of vertical height in poset-based ontologies with quantitative measures of vertical distance based on additional statistical information. In this way, we seek to accommodate weighting of different portions of the underlying ontology according to this external information source. We also wish to improve on the deficiencies of existing such measures, in particular Resnik’s measure of semantic similarity in lexical databases such as Wordnet. We begin by recalling and developing some basic concepts for ordered data objects, including our pseudo-distances and the operation of probability distributions as weights on posets. We then discuss and critique Resnik’s measure before introducing our own sense of links weights and weighted normalized pseudo-distances among comparable nodes.


Gene Ontology Directed Acyclic Graph Semantic Similarity Information Gain Link Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aho, A.V., Garey, M.R., Ullman, J.D.: The Transitive Reduction of a Directed Graph. SIAM Journal of Computing 1(2), 131–137 (1972)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Bodenreider, O., Mitchell, J.A., McCray, A.T.: Evaluation of the UMLS As a Terminology and Knowledge Resource for Biomedical Informatics. In: AMIA 2002 Annual Symposium, pp. 61–65 (2002)Google Scholar
  3. 3.
    Davis, A.R.: Types and Constraints for Lexical Semantics and Linking, Cambridge, UP (2000)Google Scholar
  4. 4.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  5. 5.
    Gene Ontology Consortium: Gene Ontology: Tool For the Unification of Biology. Nature Genetics 25(1), 25–29 (2000)Google Scholar
  6. 6.
    Joslyn, C.A.: Poset Ontologies and Concept Lattices as Semantic Hierarchies. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS 2004. LNCS (LNAI), vol. 3127, pp. 287–302. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Joslyn, C., Mniszewski, S., Fulmer, A., Heaton, G.G.: The Gene Ontology Categorizer. Bioinformatics 20(s1), 169–177 (2004)CrossRefGoogle Scholar
  8. 8.
    Joslyn, C., Oliverira, J., Scherrer, C.: Order Theoretical Knowledge Discovery: A White Paper, LAUR = 04-5812 (2004),
  9. 9.
    Joslyn, C., Cohn, J.D., Verspoor, K.M., Mniszewski, S.M.: Automating Ontological Function Annotation: Towards a Common Methodological Framework. Submitted to 2005 Bio-Ontologies Meeting, ISMB 2005 (2005)Google Scholar
  10. 10.
    Klir, G., Elias, D.: Architecture of Systems Problem Solving, 2nd edn. Plenum, New York (2003)zbMATHGoogle Scholar
  11. 11.
    Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice-Hall, New York (1995)zbMATHGoogle Scholar
  12. 12.
    Knoblock, Todd, B., Rehof, J.: Type Elaboration and Subtype Completion for Java Bytecode. In: Proc. 27th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (2000)Google Scholar
  13. 13.
    Lord, P.W., Stevens, R., Brass, A., Goble, C.: Investigating Semantic Similarity Measures Across the Gene Ontology: the Relationship Between Sequence and Annotation. Bioinformatics 10, 1275–1283 (2003)CrossRefGoogle Scholar
  14. 14.
    Monjardet, B.: Metrics on Partially Ordered Sets - A Survey. Discrete Mathematics 35, 173–184 (1981)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Int. Joint Conf. on Artificial Intelligence, pp. 448–452. Morgan Kaufmann, San Francisco (1995)Google Scholar
  16. 16.
    Schröder, Bernd, S.W.: Ordered Sets. Birkhauser, Boston (2003)zbMATHGoogle Scholar
  17. 17.
    Verspoor, K., Cohn, J., Joslyn, C., Mniszewski, S.M., Rechtsteiner, A., Rocha, L.M., Simas, T.: Protein Annotation as Term Categorization in the Gene Ontology Using Word Proximity Networks. BMC Bioinformatics 6(suppl. 1) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Cliff A. Joslyn
    • 1
  • William J. Bruno
    • 2
  1. 1.Computer and Computational SciencesLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA

Personalised recommendations