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To describe the semantics of the data (which we name as Meta-Data) Why do we describe the semantics? In order to provide a uniform way to make different parties to understand each other Which data? Any data (on the web, or in the existing legacy databases) Introduction : 12 October 2008 3 Introduction Formal definition on Ontology: Ontologies are knowledge bodies that provide a formal representation of a shared conceptualization of a particular domain. Introduction : 12 October 2008 4 Introduction Ontologies are widely used in the Semantic Web. Recently ontologies have become increasingly common on WWW where they provide semantics of annotations in web pages This distributed nature of ontology development has led to a large number of different ontologies covering the same or overlapping domains. Introduction : 12 October 2008 5 Introduction In order to two parties to understand each other, they should use the same formal representation for the shared conceptualization (so the same ontology) Unfortunately it is not easy to make everybody to agree on the same ontology for a domain And when you have different ontologies for the same domain the problem shows up. Parties with different ontologies do not understand each other. Here comes the ontology mapping into the play Introduction : 12 October 2008 6 Introduction This is not a new problem Same thing exists in Federated Databases and other Data Integration efforts In federated databases there are local schemas of the individual databases or database groups. Either provide bilateral mappings for each of the databases (which result in n2 mappings) or Define a global schema to include all of the others which has a mapping to each of them Generally mapping is done via views. Global as view, Local as view Overview : 12 October 2008 7 Overview Ontology Mapping (OM) Schema Matching (SM) Ontology Mapping vs Schema Matching. An example tool: MAFRA Ontology Mapping : 12 October 2008 8 Ontology Mapping Ontology Mapping is the process whereby two ontologies are semantically related at conceptual level, and the source ontology instances are transformed into the target ontology entities according to those semantic relations. Ontology Mapping : 12 October 2008 9 Ontology Mapping There are three dimensions of ontology mapping: Discovery: manually, automatically or semi-automatically defining the relations between ontologies Representation: A language to represent the relations between the ontologies Execution: Changing instance of a source ontology to an instance of target ontology Discovery : 12 October 2008 10 Discovery Mission: Find the related concepts/attributes of ontologies and the relation between them. Need for Automatic Mapping: Manually specifying schema matches is a time consuming, error-prone, and therefore an expensive process. There is a rapidly increasing number of web data sources, and e-business to integrate which in turn shows the greatness of ontologies and data to be mapped. A similar area in which lots of research done is “Schema Matching” Overview : 12 October 2008 11 Overview Ontology Mapping Schema Matching Ontology Mapping vs Schema Matching. Example tool: MAFRA Schema Matching : 12 October 2008 12 Schema Matching Aims to provide a matching between database schemas Match includes the mapping between the elements of two schemas that correspond to each other semantically Schema Matching : 12 October 2008 13 Schema Matching A particular representation: Entity Relationship (ER) model, Object Oriented (OO) model, XML, Directed graphs Mapping is a set of mapping elements each of which indicates that certain elements of schema S1 are mapped to certain elements in schema S2. Each mapping may have a mapping expression which specifies the relation which maybe: simple relations over scalar (<,>,=), functions, ER style relationships, set oriented relationships For example: Concantanate(Cust.FirstName, Cust.LastName) = Customer.Contact Schema Matching : 12 October 2008 14 Schema Matching The result of mapping operation is “match result” In general it is not possible to determine fully automatically matches since most schemas have semantics that effects the matching criteria but is not formally expressed or documented Partial Structural map and Full Structural map Matcher returns “match candidates”, which user accept, reject or change. Classification of schema matching approaches : 12 October 2008 15 Classification of schema matching approaches Instance vs Schema: consider the instances or only the schema Element vs Structure: perform match for individual schema elements (such as attributes), or for combination of elements Language vs Constraint: use textual names and descriptions, or the keys and relationships Matching Cardinality: overall match result may relate one or more elements of one schema to one or more elements of other schema. (1:1, 1:n, m:n) Classification of schema matching approaches : 12 October 2008 16 Classification of schema matching approaches Schema Level Matchers : 12 October 2008 17 Schema Level Matchers Schema Level consider schema info, such as name, description, data type, relationship types (is-a, part-of), constraints, and schema structure Element level match considers only the atomic granularity elements of the schema such as attributes in xml schema or columns in relational schema Schema Level Matchers : 12 October 2008 18 Schema Level Matchers Structure level: match refers to matching combinations of elements that appear together in a structure Linguistic based approach : 12 October 2008 19 Linguistic based approach Linguistic based approaches use names and text to find semantically similar schema elements Equality of names Equality of canonical name representations: CName = customerName Equality of synonyms: car = automobile Requires use of dictionaries (even multi-language), and taxonomies Homonyms (words that are written in the same format but meaning different) are introduce problems Constraint based approach : 12 October 2008 20 Constraint based approach Schemas has constraints to define data types, value ranges, uniqueness, optionality, relationship types and cardinalities. Similarity is based on this constraints Not so meaningful to use alone, but increases the reliability when used with other approaches. Matching Cardinality : 12 October 2008 21 Matching Cardinality An element in S1 can participate more than one match result between S1 and S2. Or within an individual match result, one or more S1 elements can match to one or more S2 elements. 1:1, 1:n, n:1, (m:n) 1:1 n:1 1:n m:n Overview : 12 October 2008 22 Overview Ontology Mapping Schema Matching Ontology Mapping vs Schema Matching An example tool: MAFRA Differences between Schema Matching and Ontology Mapping : 12 October 2008 23 Differences between Schema Matching and Ontology Mapping Database schema does not provide explicit semantics for their data, where ontologies does explicitly and formally Database schemas are not sharable or reusable, usually they are defined over a specific database, whereas ontologies are by nature reusable and sharable Ontology development is a more and more decentralized procedure Database evolution should take into account the effects of each change on the data (addition of a new class), where in ontologies, the number of the knowledge representation primitives is much higher and more complex: cardinality constraints, inverse properties, transitive properties, disjoint classes, type-checking constraints Ontology mapping is seems to be more reliable with the previous properties Mapping Representation : 12 October 2008 24 Mapping Representation MAFRA (A.Maedche, N.Silva, B.Motik, R.Volz) and RDFT (C.Bussler, D.Fensel, B.Omalayenko) are two representation initiatives for mappings. Both have similar logic to represent the mappings Both uses “Bridges” to define the mapping between two schemas Bridge establishes encapsulation of correspondences between entities from source and target ontology Both defines a meta-ontology of bridges Will be clear with MAFRA Overview : 12 October 2008 25 Overview Ontology Mapping Schema Matching Ontology Mapping vs Schema Matching Example tool: MAFRA MAFRA : 12 October 2008 26 MAFRA “A MApping FRAmework for Distributed Ontologies”, developed at Univ. Karlsruhe One of the main contributions is the definition of “Semantic Bridges” (SB) between ontologies which establishes correspondences between entities from source and target ontology. Defines “Semantic Bridge Ontology” which is an ontology of mapping constructs. Includes functionality for all of the three dimensions of ontology mapping (discovery, representation, execution) MAFRA Conceptual Architecture : 12 October 2008 27 MAFRA Conceptual Architecture Horizontal Dimension Vertical Dimension Horizontal Dimensions : 12 October 2008 28 Horizontal Dimensions LIFT & Normalization: Raise all data to be mapped to the same representation level, and normalize the strings (tokenization, expansion of acronyms…) Similarity: Mapping discovery. Calculates the similarities according to several already proposed algorithms. Semantic Bridging: Represent the mapping, will be explained in detail Execution: Transform instances from source ontology to target ontology Post-Processing: Takes the results of execution to check and improve the quality of transformation. Semantic Bridges : 12 October 2008 29 Semantic Bridges Has five dimensions: Entity dimension: bridge may relate ontology entities such as concepts, relations, and attributes. Cardinality dimension: Matching may be 1:1, 1:n and n:1. Structural dimension: The way how elementary bridges may be combined into more complex bridge (specialization, alternatives, composition, abstraction) Constraint dimension: Controls the execution of bridge. Acts as conditions that must hold in order the transformation to take place. Transformation dimension: How the instances are transformed. Semantic Bridging Ontology : 12 October 2008 30 Semantic Bridging Ontology MAFRA Mapping Example : 12 October 2008 31 MAFRA Mapping Example Slide 32: 12 October 2008 32 Slide 33: 12 October 2008 33 Thanx for Listening : 12 October 2008 34 Thanx for Listening (Further) Questions? You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.