Introduction

This is just a short example on how the Semantic Web technologies can be used for data integration

The example is, of course, artificial and simplified

A simplifed bookstore data (dataset “A”)

ID	Author	Title	Publisher	Year
ISBN 0-00-651409-X	id_xyz	The Glass Palace	id_qpr	2000

 

ID	Name	Home page
id_xyz	Amitav Ghosh	http://www.amitavghosh.com/

 

ID	Publisher Name	City
id_qpr	Harper Collins	London

1^st step: export your data as a set of relations

Some notes on the data export

• Data export does not necessarily mean physical conversion of the data
  • relations can be generated on-the-fly at query time
    • via SQL “bridges”
    • scraping HTML pages
    • extracting data from Excel sheets
    • etc.
• One can export part of the data

Another bookstore data (dataset “F”)

ID	Titre	Auteur	Traducteur	Original
ISBN 2020386682	Le Palais des miroirs	i_abc	i_qrs	ISBN 0-00-651409-X

 

ID	Nom
i_abc	Amitav Ghosh
i_qrs	Christiane Besse

2^nd step: export your second data into RDF

3^rd step: start merging your data

3^rd step: start merging your data (cont.)

3^rd step: merge identical resources

Start making queries…

• User of data “F” can now ask queries like:
  • « donnes-moi le titre de l’original »
  • (ie: “give me the title of the original”)
• This information is not in the dataset “F”…
• …but can be automatically retrieved by merging with dataset “A”!

However, things are not complete yet…

• We “feel” that a:author and f:auteur should be the same
• But an automatic merge doest not know that!
• Let us add some extra information to the merged data:
  • a:author same as f:auteur
  • both identify a “Person”:
    • a term that a community has already defined
    • a “Person” is uniquely identified by his/her name and, say, homepage
    • it can be used as a “category” for certain type of resources

3^rd step revisited: use the extra knowledge

Start making richer queries!

• User of dataset “F” can now query:
  • « donnes-moi la page d’acceuil de l’auteur de l’original »
  • (ie, “give me the home page of the original’s author)
• The data is not in dataset “F”…
• …but was made available by:
  • merging datasets “A” and datasets “F”
  • adding three simple extra statements as an extra “glue”
  • using existing terminologies as part of the “glue”

Combine with different datasets

• Using, e.g., the “Person”, the dataset can be combined with other sources
• For example, the data in Wikipedia can be extracted using simple (e.g., XSLT) tools
  • there is an active development to add some simple semantic “tag” to wikipedia entries
  • we tacitly presuppose their existence in our example…

Merge with Wikipedia data

Merge with Wikipedia data

Merge with Wikipedia data

Is that surprising?

• Maybe but, in fact, no…
• What happened via automatic means is done all the time, every day by the users of the Web!
• The difference: a bit of extra rigor (e.g., naming the relationships) is necessary so that machines could do this, too

What did we do?

• We combined different datasets
  • all may be of different origin somewhere on the web
  • all may have different formats (mysql, excel sheet, XHTML, etc)
  • all may have different names for relations (e.g., multilingual)
• We could combine the data because some URI-s were identical (the ISBN-s in this case)
• We could add some simple additional information (the “glue”), also using common terminologies that a community has produced
• As a result, new relations could be found and retrieved

So where is the Semantic Web?

• The Semantic Web provides the technologies to make such integration possible! For example:
  • an abstract model for the relational graphs: RDF
  • means to extract RDF information from, eg, XHTML pages: GRDDL
  • a query language adapted for the relational graphs: SPARQL
  • various technologies to characterize the relationships, categorize resources: RDFS (RDF Schemas), OWL (Web Ontology Language), SKOS
    • depending on the complexity required, applications may choose among the different technologies
  • reuse of existing “ontologies” that others have produced (FOAF in our case)

A real life example: Antibodies Demo

• Scenario: find the known antibodies for a protein in a specific species
• Combine four different data sources
  • “Entrez protein sequence” from National Center for Biotechnology Information; conversion to RDF
  • “Antibody Directory” from Alzheimer Research Forum; scraping RDF from HTML
  • Mapping data between genes and antibodies; convert spreadsheet to RDF
  • “Taxonomy information” from Wikispecies; use XSLT to extract RDF from XHTML
• Use SPARQL as an integration tool