OGI SCHOOL OF SCIENCE & ENGINEERING OREGON HEALTH & SCIENCE UNIVERSITY Knowledge Transformation for the Semantic Web at ECAI 2002 On Modeling Conformance for Flexible Transformation over Data Models Shawn Bowers & Lois Delcambre Database and Object Laboratory Department of Computer Science & Engineering OGI School of Science & Engineering, OHSU

Knowledge Transformation for the Semantic Web at ECAI 20022July 23, 2002 Information is everywhere Lots of information is available –And, information sources describe various things Lots of useful representation schemes exist –RDF, Topic Maps, XML, Relational Models, and E-R models are just a few example data models There are strong reasons to touch information sources –For data integration, searching, resource discovery, navigation, inference, and so on

Knowledge Transformation for the Semantic Web at ECAI 20023July 23, 2002 Leveraging heterogeneous sources Our goals are to: Develop generic technology that can understand various representation schemes Focus on how schemes structure information Exploit the generic technology for transformation We are also exploring navigation and query (and both mixed)

Knowledge Transformation for the Semantic Web at ECAI 20024July 23, 2002 About transformation Transforming information between representation schemes isn’t easy –Schemes are rarely one-to-one –Often many possible mappings Little support for creating transformations –Typically, special purpose programs –Making it difficult to write, maintain, and reason about mappings We believe having generic technology to understand representation schemes will help

Knowledge Transformation for the Semantic Web at ECAI 20025July 23, 2002 Observations on representation schemes Representation schemes typically: Structure information using only a small set of basic data structures, like: –Collections (Lists, Sets, Bags) –Record-like structures (Entities) –Relationships –Atomic Types Differ in their composition of basic structures and in conformance

Knowledge Transformation for the Semantic Web at ECAI 20026July 23, 2002 An example: The relational model ConstructsComposition Relation Typeset-of Field Types Field Typerecord-of Domain scalar type Tablerecord-of Tupleset-of Fields Fieldrecord-of Valuescalar value Conformance TableRel. Type 1:1 TupleRel. Type ValueDomain 1:10:n 1:n0:n We call conformance constraints like these: “Schema-First”

Knowledge Transformation for the Semantic Web at ECAI 20027July 23, 2002 An example: XML ConstructsComposition Elt Typerecord-of Nested Elt Typerelationship Elt Type Node Type Att Type record-of Node TypeElt Type or Content Type Eltrecord-of Attrecord-of NodeElt or pcdata Conformance EltElt Type 0:n AttAtt Type 0:n We call conformance constraints like these: “Schema-Optional”

Knowledge Transformation for the Semantic Web at ECAI 20028July 23, 2002 An example: Topic maps ConstructsComposition Topicrecord-of Topic-Occ relationship Topic Occurrence Occurrence record-of Associationan id Assoc-Memberrelationship Association Member Memberrecord-of Member-Topicrelationship Member Topic Conformance Topic 0:n AssociationTopic OccurrenceDomain 0:n We call this type of conformance: “Multi-Level”

Knowledge Transformation for the Semantic Web at ECAI 20029July 23, 2002 Conformance Schema-First Conformance –Schema imposes uniform structure on data (good for query) Schema-Optional Conformance –Conformance is not required (open); more natural for some types of information Multiple-Levels of Conformance –Usually schema-optional; allows rich typing information We explicitly model conformance to support a wide- range of representation schemes

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 The generic metamodel framework A construct type represents a single basic data structure A “data” instance (d-inst) instantiates a conformance definition Metamodel (Construct Types) Data Model (Schema Constructs) Data Model (Data Constructs) Schema (Instances) Data (Instances) ct-inst c-inst conf d-inst elt typeelt e1et1

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 A concrete metamodel We consider the following construct types: Set, Bag, List(For collections) Struct (For entities and relationships) Scalar(For primitive value types) Union(For convenience) This is only one choice for construct types – others are possible

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 A language for the framework We separate construct definition from composition The following formulas represent constructs: ct-inst(topic, struct-ct) conf(topic, topic) And these are for composition: comp(topic, structof(name  String, …)) comp(relType, setof(fieldType)) … comp(node, union(elt, pcdata)) ct-inst c-inst conf d-inst

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 We also separate instance definition from values The following formulas represent instances: c-inst(e, elt) d-inst(e, et) And these are for instance values: val(e, struct(tag = “person”, attset = as)) val(as, set(a1, a2, …)) … val(content1, scalar(“john smith”)) A language for the framework ct-inst c-inst conf d-inst

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 A transformation is a set of mapping rules (expressed as if-then actions) “Convert every XML Element Type into a Relation Type” add : c-inst(ET, relType) :- c-inst(ET, eltType). add : val(ET, set()) :- c-inst(ET, eltType). “Convert every Attribute Type into a Field Type” add : c-inst(AT, fieldtype) :- c-inst(AT, attType). add : val(AT, struct(name=N, …)) :- c-inst(AT, attType) & val(AT, struct(name=N). Transformation: Mapping rules eltTyperelType sourcedestination sourcedestination attTypefieldType

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 The framework supports a wide range of mappings Model-to-Model Construct mappings Schema-to-Schema Instance mappings Mixtures Model-to-schema Schema-to-instance And so on … Transformation flexibility ct-inst c-inst conf d-inst

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 In general, mapping rules come in pairs: one for basic structures and one for composition We want to exploit the structural mappings to generate the correct composition mappings –For example, from a rule: –We want to map the values ( val formulas) accordingly. Higher-level transformation source destination eltType relType conf elt bag conf tupletable conf

Knowledge Transformation for the Semantic Web at ECAI July 23, 2002 Conclusions Previous work … –A simpler metamodel based on RDF –No clear separation of instance categories –Not a “concise” description language (i.e., triples) What we are exploring now … –Specification and implementation of higher level rules (mainly structure-based rules) –A generic browsing API over the framework –Testing and evaluation of the framework Thank You! (Questions …) For more information