1. 1 Découverte de mappings entre schemas : les différentes approches Schema Matching : Different Approaches Khalid Saleem LIRMM

2. 2 Schema and Ontology Schema represents Database Community Schemas often do not provide explicit semantics of their data (ER, XML document schema). Ontology represents the AI Community Ontologies are logical systems that themselves obey some formal semantics. Designed to be interpreted by computers for reasoning (OWL) Schemas and Ontologies are similar in the sense that Both provide a vocabulary of terms that describes a domain Both constraint the meaning of terms used in vocabulary (Hierarchy/ relations) XML XML Schema RDF RDF Schema OWL

3. 3 Schema vs Ontology : examples class-def animal %plants are a class that is disjoint from animals class-def plant subclass-of NOT animal %it is necessary but not sufficient for a tree to be a plant: class-def tree subclass-of plant %branches are PART OF trees class-def branch slot-constraint is-part-of has-value tree %it is necessary and sufficient for a carnivore to be an animal: class-def defined carnivore subclass-of animal slot-constraints eats value-type animal %herbivores eat only plants OR part of plants class-def defined herbivore subclass-of animal slot-constraint eats value-type plant OR (slot-constraint is-part-of has-value plant) DAML +OIL branch is-part-of tree XML

4. 4 Match Takes two schemas/ontologies as input and produces a mapping between elements of the two schemas that correspond semantically to each other 1-1 match complex match 26,60 Harry Potter J. K. Rowling 11,50 Marie Des Intrigues Juliette Benzoni 16,50 Nous Les Dieux Bernard Werber 24 Pompei Robert Harris price book-title author-name Books Source A listed-price title a-fname a-lname Books Source B

5. 5 Schema Matching vs Ontology Matching Schema matching is usually performed with the help of techniques trying to guess the meaning encoded in the schemas Ontology matching try to exploit knowledge explicitly encoded in the ontologies.` In real world applications : Solutions from both domains are mutually beneficial

6. 6 Application Domains Traditional (Static) Schema Integration Data warehousing E-commerce Catalogue Integration New Frontiers (Dynamic) Semantic Query Processing Agent Communication Web Services Integration P2P Databases

7. 7 Basic Classification of Matchers [RB01] Schema vs Data Instance Element vs Structure Language vs Constraint String based : Prefix, Suffix e.g. auth: author Tokenization, Lemmatization, Eliminition [GSY04] Tool_Kit :(Tool,Kit), Kits:Kit, IsRelatedTo : Related Data Types, Value domain e.g. 1..12 : month Match Cardinalities - 1:1, 1:n, n:m (Tel Res, Other) : (Tel Day, Evening, Night) Auxiliary Information Global Schema, Dictionaries, Thesauri, Previous Match Decisions, User Input

8. 8 Basic Classification of Matchers [SE05] Structure Level Techniques Graph Matching Children Leaves Relations Taxonomy based Techniques e.g if super concept is same then sub concepts are same or vice versa Model Based ER, XML or XML schema, OWL, OO etc. Combinational Matchers[RB01] Hybrid Matcher Multiple/Composite Matcher

9. 9 Match Dimensions [SE05] For Match Algorithms designing We need the knowledge for its utilization i.e. Dimensions Input of the Algorithm Data or Schema, Element level or Structure Level Characteristics of the Matching Process Require exact or approximate matching Performance over quality Output of the Algorithms Output is a graded result, or part of a set of match algorithms which are combined together for a map result

10. 10 Existing Matching Tools Cupid [MBR01] COMA (COMA++) [ADMR05] Similarity Flooding SemInt Artemis DIKE TransScm AutoMed Charlie [TBBT04] Ontologies Specific NOM/ QOM OLA Anchor-PROMPT S-Match [GSY04] HICAL SKAT

11. 11 Matching Tools continued Machine Learning GLUE (LSD, CGLUE) [DMDH02] Automatch These tools do not completely fulfil the requirements for large scale schema matching because Not fully automated Emphasise less on search space optimisation

12. 12 Our Approach Motivation : Large Scale Scenario Peer-to-peer Information Systems over the XML Web b ap n t n b wf n t pi n b d a g tpr w nho t a: author b: book d: detail f: information g: general h: birth i: isbn n: name o: own-books p: publisher r: price t: title w: writer b wf n t pi n a=w b=o f=d Our Schema Matching and Integration Approach Tree Mining Techniques Name Matcher Element Level Matching Structure Level Matching Search sub-trees h

13. 13 Tree Mining Approach Our work extends these data structures for schema matching and integration process for handling large sets of XML schema trees. Employs a) Element level Name Matcher (same node label or synonym) Cluster similar/synonym labels b) Utilize the node scope values properties to extract semantics out of structure E.g. node with label name n2[2,2] is a descendent of node with label author n1[1,2] and not of node with label publisher n3[3,4] verified using descendent test Inspired from the tree mining algorithms and data structures based on node scope values (calculated by depth first pre-order traversal) Top-down [Z02] book n0 [0,5] b title n5 [5,5] t author n1 [1,2] a name n2 [2,2] n publisher n3 [3,4] p name n4 [4,4] n Descendent Node Check : Scope of Node x is [X,Y] and Scope of Descendent Node xd [Xd,Yd] then Xd>X and Yd<=Y

14. 14 Tree Mining Approach … continued Data Structure used Label List : Sorted list of all node labels in the forest of XML schema trees xGrid : Matrix in which each row represent each participating XML tree and each column represents the corresponding node label. Each cell contains the scope values, parent node number and mapping information. Output Creation of a Mediated Schema Tree, from the given forest of participating XML schema trees. Generation of Mapping Information between participating schema trees and the mediated schema tree

15. 15 Tree Mining Approach … continued Mapping Information is the column number of node Sm S1 S2 S3 S4

16. 16 Conclusion Element level Name and Linguistic Matching with the support of thesaurus is an integral part of every Match system. With systems moving towards schema/ontology based manipulation, and lack of global schemas or previous matching results, Structure Level matching is equally important for making out the semantics. Peer-to-peer environment requires new methods to be exploited for performance and quality mapping i.e. integration of Tree Mining techniques for matching purposes and search space optimisation. Machine Learning algorithms can be beneficial in the P2P environment in later stages when training examples have been created from instance data, provided the target domain remains the same.

17. 17 References [AH04] Antoniou G., Harmelen F. A Semantic Web Primer, The MIT Press, 2004 [ADMR05] Aumuller D., Do H. H., Massmann S., and Rahm E. Schema and ontology matching with COMA++. In Proceedings of the International Conference on Management of Data (SIG-MOD), 2005 [BR04] Bellahsène Z. and Roantree M. (2004) Querying Distributed Data in a Super- peer based Architecture. DEXA 2004. [BMP04] Bernstein PA., Melnik S., Petropoulos M. and Quix C. (2004) Industrial- Strength Schema Mapping. SIGMOD Record, Vol. 33, No. 4, December 2004 [DMDH02] Doan AH., Madhavan J., Domingos P. and Halvey A. (2002) Learning to Map Ontologies on the Semantic Web. WWW 2002 [MBR01] Madhavan J., Bernstein PA. and Rahm E. (2001) Generic Schema Matching with Cupid. VLDB 2001. [RB01] Rahm E. and Bernstein PA (2001) A Survey of Approaches to Automatic Schema Matching. VLDB Journal 2001 : 10(4):334-3503 [SE05] Shvaiko P. and Euzenat J. (2005) A Survey of Schema-based Matching Approaches. Journal on Data Semantics, 2005. [TBBT04] Tranier J., Baraer R., Bellahsene Z. and Teisseire M (2004) Where’s Charlie: Family Based Heuristics for Peer-to-Peer Schema Integration. IDEAS 2004, 227-235 [Z02] Zaki MJ (2002) Efficiently Mining Frequent Trees in a Forest. 8 th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining. July 2002 http://www.w3.org/TR/daml+oil-reference http://www.doc.ic.ac.uk/automed/

18. 18 Thank you