1 Extracting RDF Data from Unstructured Sources Based on an RDF Target Schema Tim Chartrand Research Supported By NSF

2 Motivation Semantic Web – Global machine understandable knowledge base WWW – lots of information/data designed for human consumption DEG contribution – Extract data from the human readable web Proposed solution – Extract WWW data and structure it in the Semantic Web format (RDF)

3 Overview of Proposed Research Extraction Ontology RDF Schema User Extraction Engine HTML Page Relational Data RDF Data

4 RDF – What is it? Resource Description Framework Language of the Semantic Web Set of subject-predicate-object triples [tim.html, creator, tim], [tim.html, type, thesis] tim.html Tim Thesis tim.html Tim creator Thesis type OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

5 RDF Schema Basics Core Concepts rdfs:class –The usual concept of a class. Ex. Class Person rdfs:subClassOf –Specifies the generalization of a class Ex. Class Teacher is subClassOf Person rdfs:property –Can apply to a class. Has a value which. Ex. Class Person has property Name rdfs:domain – Classes to which a property can apply. Ex. Property Name has domain Person rdfs:range – Possible values of a property. Ex. Property Name has range Literal rdfs:subPropertyOf – Specifies the generalization of a property Ex. Property Nickname is subPropertyOf Name OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

6 Example RDF Schema Full Schema … … OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

7 RDF Schema Graph OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

8 Extraction Ontology Ontology Structure Classes map to object sets Properties map to binary relationship sets between classes Literal properties map to relationship sets between classes and lexical data frames Primary Object & Constraints – best guess based on heuristics\ Data Frames Need a data frame library Match properties with data frame library Specialize the property data frames OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

9 User Modification Cardinality Constraints Allow the user to edit any of the generated constraints Keep track of changes – affects database schema Data Frames Provide a data frame editor Allow user to modify the specialized data frames Usually only add key words OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

10 Input Web Page OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

11 Relational Data OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

12 Extracted RDF Data Full RDF <obit:Person rdf:ID="1001" obit:Name="Lemar K. Adamson" … > … <obit:Funeral rdf:ID="5001" obit:FuneralAddress="1540 E. Linden" obit:FuneralDate="" obit:FuneralTime="10:00 a.m."> OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

13 RDF Data Graph OntologyRDFS UserHTML Relational Data RDF Data Extraction Engine

14 Conclusions Converting RDF Schemas to Data Extraction Ontologies can be done with some user interaction. The nature and amount of user interaction necessary for good data extraction is a good topic for research Converting relational data to RDF data can be done automatically