1 / 32

RiMOM : A Dynamic Multistrategy Ontology Alignment Framework

RiMOM : A Dynamic Multistrategy Ontology Alignment Framework . By: Juanzi Li, Jie Tang, Yi Li and Qiong Luo Presenter: Abhijit Gali. RiMOM.

giza
Download Presentation

RiMOM : A Dynamic Multistrategy Ontology Alignment Framework

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. RiMOM: A Dynamic Multistrategy Ontology Alignment Framework By: Juanzi Li, Jie Tang, Yi Li and Qiong Luo Presenter: AbhijitGali

  2. RiMOM A systematic approach to quantitatively estimate the similarity characteristics for each alignment task and strategy selection method to automatically combine the matching strategies based on two estimated factors

  3. Problems faced by ontology alignment • Combination of different strategies for ontology alignment • When to use the combination strategies

  4. Ontologies and ontology alignment

  5. Ontology • Definition 1: An ontology is a formal specification of a shared conceptualization. We describe the ontology as a 6-tuple : O={C, P, Hc, Hp, Ao, I} • OWL provides vocabularies to define the formal semantics of ontology • owl:Class and rdfs:subClassOf define the concepts and subconcepts • rdfs:Property and rdfs:subPropertyOf define property and subproperties • rdfs:domain and rdfs:range of a property define what concepts can have the property and what instances of the concepts can be the values of the property.

  6. Concept description-Description(c): A concept c є C is described by a 4-tuple: Description (c)={Meta(c), Hier(c), Rest(c), Inst(c)} • Property description-Description(p): A property p єP is described by a 5-tuple: Description (p)= {Meta(p), Hier(p), Doma(p), Rang(p), Inst(p)}

  7. Ontology alignment • Definition: Given two ontologies O1 and O2, an alignment (or alignment task) finds, for each entity in O1, a corresponding entity in O2. O1 is called the source ontology and O2 the target ontology. Align(O1,O2)={(ei1, ei2, coni, relationi)|ei1 є O1, ei2 є O2, coniє [0,1], relationiє (exact, narrower, broader, overlap)}

  8. Fragments of three ontologies to be aligned

  9. Dynamic Multistrategy Ontology Alignment • Goal- to detect a selection strategy and how confident we should be about the strategy • Tasks : a) Definition of criteria for selection strategy b) Dynamic selection of multiple strategies

  10. Similarity Factors between Two Ontologies • Label similarity factor: similarity between two ontologies based on the entities’ names F_LS(O1, O2)=#iden_conc-label +#iden_prop_label max(|C1|+|P1|,|C2|+|P2|) • Structure similarity factor: similarity of two ontologies based on their structure information F_SS(O1, O2)= (#comm_nonl_conc+#comm_nonl_prop) (max(#nonl_C1+#nonl_P1, #nonl_C2+#nonl_P2)

  11. SIMILARITIES AND OVERVIEW OF RiMOM

  12. Entity similarity • For two concepts : sim(e1,e2)= f( sim_Meta(e1,e2), sim_Hier(e1,e2), sim_Rest(e1,e2), sim_Inst(e1,e2) ) • For two properties: sim(e1,e2)= f (sim_Meta(e1,e2), sim_Hier(e1,e2), sim_Doma(e1,e2), sim_Rang(e1,e2),sim_Inst(e1,e2))

  13. RiMOM alignment processing flow

  14. Overview of RiMOM • Preprocessing • Linguistic-based ontology alignment • Similarity combination • Similarity propagation • Alignment generation and refinement

  15. ONTOLOGY ALIGNMENT STRATEGIES IN RiMOM

  16. Linguistic-Based Strategies • Edit-Distance-Based Strategy- involving calculation of sim_Name(w1, w2) and sim_Name(e1, e2) • Vector-Distance (VD)-Based Strategy

  17. Structure-Based Strategies • Pairwise Connectivity Graph (PCG) construction and similarity propagation • Directed Labeled Graph(DLG) has edges represented by triple ( s,p,o) • Construction of DLG_O using HasSubConcept, HasSibling, HasProperty, HasRange, and HasSubProperty • Construction of SPG_O using nodes that are entity pairs from two ontologies that have some structural relationship in common

  18. Example of DLG_O and SPG_O

  19. STRATEGY SELECTION

  20. Feature Selection in Vector-Distance-BasedStrategy • Determination of Hierarchical Information Use: F_SS> threshold ε1 • Enhancement of Structure Information: Depends on the path length from the root concept, the number of properties, and the number of subconcepts of the current entity

  21. Weight Calculation of Similarity Combination • sim(e1,e2)= (wnameσ(sim_Name(e1,e2))+wvec σ(sim_Vec(e1,e2))) (wname+wvec) σ(x)= 1/(1+exp(-5(x-α))), where α=0.5 wname= F_LS/ max(F_LS, F_SS) wvec= F_SS/max(F_LS, F_SS)

  22. Selection of Similarity Propagation Strategy: • Concept-Concept(CC)- HasSubclass and HasConceptSibling relations • Concept-Property(CP)- HasRange and HasProperty relations • Property-Property(PP)- HasSubproperty and HasPropertySibling relations • Parameter Setting

  23. EVALUATION

  24. Test Sets and Evaluation Methods • Benchmark Data Set in OAEI 2006 Name, comments, specialization hierarchy, instances, properties, classes, additions of 4 real ontologies • Directory and Food Data Sets in OAEI 2006 i) SKOS version of the United Nations Food ii) SKOS version of the United States National Agricultural Library • Evaluation Metrics: i) Precision(P) ii) Recall (R)

  25. Results on Benchmark Data Set

  26. (a) F SS in VD-based strategy (b) F SS in SF (c) Combined effects of F SS

  27. Result on OAEI 2007 Graph of the precision and recall. (a) OAEI 2006. (b) OAEI 2007

  28. Summary • High performance • Effectiveness of strategy selection • Contribution of the SF strategy • Inefficiency for dealing with large-scale ontologies

  29. Related Work • Schema Matching- COMA , Rondo, and Cupid are three composite methods • Ontology alignment and the combination of multiple ontology alignment strategies • Structure-based ontology alignment • Relationship with other alignment methods

  30. Relationship with Several Classical Methods

  31. CONCLUSION • A multistrategy framework, RiMOM, to automatically and dynamically compose strategies for individual ontology alignment tasks was proposed • Experimental results on the data sets from OAEI 2006 and OAEI 2007 demonstrate that the system performs better than most of the participants and is among the top three performers on the benchmark data sets

  32. QUESTIONS ???

More Related