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Ontology Alignment/Matching

Ontology Alignment/Matching. Prafulla Palwe. Agenda. Introduction Being serious about the semantic web Living with heterogeneity Heterogeneity problem I have a plan for you  Matching Problem Matching Operation Motivation Schema Matching Vs Ontology Matching Correspondence Alignment

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Ontology Alignment/Matching

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  1. Ontology Alignment/Matching Prafulla Palwe

  2. Agenda • Introduction • Being serious about the semantic web • Living with heterogeneity • Heterogeneity problem • I have a plan for you  • Matching Problem • Matching Operation • Motivation • Schema Matching Vs Ontology Matching • Correspondence • Alignment • Matching Process • Sequential composition • Parallel composition • Application Domains • Traditional • Emergent • Classification • Matching Dimensions • Basic Techniques • Element Level • Structure Level • Summary and Challenges

  3. Introduction • Being serious about the semantic web - • It is not one guy's ontology • It is not several guys' common ontology • It is many guys and girls' many ontologies • So it is a mess, but a meaningful mess 

  4. Introduction • Living with heterogeneity - • The semantic web will be: • Huge • Dynamic • Heterogeneous • These are not bugs, they are features. • We must learn to live with them.

  5. Introduction • Heterogeneity problem – • Resources being expressed in different ways must be reconciled before being used. • Mismatch between formalized knowledge can occur when: • different languages are used; • different terminologies are used; • different modeling is used.

  6. Introduction • I have a plan for you – Reconciliation

  7. Matching Problem • Matching Operation • Definition – Matching operation takes as input ontologies, each consisting of a set of discrete entities (e.g., tables, XML elements, classes, properties) and determines as output the relationships (e.g., equivalence, subsumption) holding between these entities

  8. Matching Problem • Motivation – • 2 XML Schemas • 2 Ontologies

  9. Matching Problem

  10. Matching Problem

  11. Matching Problem

  12. Matching Problem

  13. Matching Problem

  14. Matching Problem

  15. Matching Problem

  16. Matching Problem

  17. Matching Problem • Schema mapping Vs ontology mapping • Differences - • Schemas often do not provide explicit semantics for their data • Relational schemas provide no generalization • Ontologies are logical systems that constrain the meaning • Ontology definition as set of logical axioms • Commonalities - • Schemas and ontologies provide a vocabulary of terms that describes the domain of interest • Schemas and ontologies constrain the meaning of terms used in the vocabulary.

  18. Matching Problem • Correspondence • Definition – • Given 2 ontologies O and O’ , a correspondence between M between O and O’ is a 5-uple : <id,e,e’,R,n> such that: • id is a unique identifier of the correspondence. • e and e’ are entities of O and O’ (e.g. XML Elements, classes) • R is a relation (e.g. equivalence (=), disjointness (_|_)) • n is a confidence measure in some mathematical structure (typically in the [0,1] range)

  19. Matching Problem • Alignment • Definition – • Given 2 ontologies O and O’, an alignment A between O and O’: • Is a set of correspondence on O and O’ • With some cardinality: 1-1, 1-* etc. • Some additional metadata (method, date, properties etc)

  20. Matching Process

  21. Matching Process

  22. Matching Process

  23. Matching Process

  24. Matching Process

  25. Matching Process

  26. Matching Process • General Basic Matching Process

  27. Matching Process • Sequential Composition

  28. Matching Process • Parallel composition

  29. Matching Process • Similarity Filter, alignment extractor and alignment filter –

  30. Matching Process • Aggregation Operations – • There are many different ways to aggregate matcher results, usually depending on confidence/similarity: • Triangular norms (min, weighted products) useful for selecting only the best results • Multidimensional distances (Eudidean distance, weighted sum) useful for taking into account all dimensions • Fuzzy aggregation (min, weighted average) useful for aggregating competing algorithms and averaging their results • Other specific measures (e.g., ordered weighted average)

  31. Application Domains • Traditional - • Ontology evolution • Schema integration • Catalog integration • Data integration

  32. Application Domains • Ontology Evolution

  33. Application Domains • Catalog Integration

  34. Application Domains • Emergent • P2P information sharing • Agent communication • Web service composition • Query answering on the web

  35. Application Domains • P2P information sharing

  36. Application Domains • Web Service Composition

  37. Application Domains • Agent communication

  38. Classifications • Matching Dimensions • Input Dimensions • Underlying models (e.g. XML, OWL) • Schema Level Vs Instance Level • Process Dimensions • Approximate Vs Exact • Interpretation of the input • Output Dimensions • Cardinality • Equivalence Vs Diverse relations • Graded Vs Absolute Confidence

  39. Classifications • Three Layers • Upper Layer • Granularity of match • Interpretation of the input information • Middle Layer • Represents classes of elementary (basic) matching techniques • Lower Layer • Based on the kind of input which is used by elementary matching techniques

  40. Classifications • Classification of schema based techniques

  41. Basic Techniques • Element Level Techniques • String based – • Prefix - • Takes an input 2 strings and checks whether the first string starts with the second • e.g. net = network but also hot = hotel • Suffix – • Takes an input 2 strings and checks whether the first string ends with the second • e.g. ID = PID but also word = sword • Edit Distance – • Takes as input 2 strings and calculates the number of edit operations (insertion,deletion,substitution) of characters required to transform one string into other normalized by length of the max string. • editDistance(NKN, Nikon) = 0.4

  42. Basic Techniques • Language based – • Tokenization – • Parses names into tokens by recognizing punctuation, cases • Hands-Free_Kits <hands, free, kits> • Lemmatization – • Analyses morphologically tokens in order to find all their possible basic forms • Kits  Kit • Elimination – • Discards empty tokens that are articles, prepositions, conjuctions • a, the, by, type of, their, from

  43. Basic Techniques • Structure Level Techniques • Ontologies are viewed as graph-like structure containing terms and their inter-relationships. • Taxonomy based • Bounded path matching • These take 2 paths with links between classes defined by the hierarchical relations, compare terms and their positions along these paths and identify similar terms. • Super(sub)-concept rules • If super concepts are the same, the actual concepts are similar to each other

  44. Basic Techniques • Tree based • Children • 2 non leaf schema elements are structurally similar if their immediate children sets are highly similar • Leaves • 2 non leaf schema elements are structurally similar if their leaf sets are highly similar, even if their immediate children are not.

  45. Basic Techniques

  46. Basic Techniques

  47. Basic Techniques

  48. Summary and Challenges • Summary • Ontology Matching and alignment is the process of developing the common or most common structure/semantic terms out of 2 or more different ontologies/structures/schemas. • Different efficient and complex algorithms using basic techniques of matching process, can be developed for matching and alignment generation. • Challenges • Developing generic and highly efficient matching and alignment generation algorithms.

  49. Thank You

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