A Semantic Model for Federated Queries Over a Normalized Corpus

Transcription

1 A Semantic Model for Federated Queries Over a Normalized Corpus Samuel Croset, Christoph Grabmüller, Dietrich Rebholz-Schuhmann 17 th March 2010, Hinxton EBI is an Outstation of the European Molecular Biology Laboratory.

2 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries References and credits are at the end of the presentation

3 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

4 Biological motivations Raw Text

5 Biological motivations Raw Text CALBCII Text-Mining Challenge

6 Biological motivations Raw Text CALBCII Text-Mining Challenge Knowledge Extraction

7 Biological motivations Raw Text CALBCII Text-Mining Challenge Knowledge Extraction Bio-Resources

8 Biological motivations Raw Text CALBCII Text-Mining Challenge Bio-Resources Integration Knowledge Extraction

9 Biological motivations Raw Text CALBCII Text-Mining Challenge Bio-Resources Integration Knowledge Extraction

10 Biological motivations Raw Text CALBCII Text-Mining Challenge Semantic Web Bio-Resources Integration Knowledge Extraction

11 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

12 Semantic Web modeling Why? Semantic??? The meaning

13 Semantic Web modeling Why? What is a cell??

14 Semantic Web modeling Why? What is a cell??

15 Semantic Web modeling Why? What is a cell??

16 Semantic Web modeling Why? What is a cell??

17 Semantic Web modeling Why? What is a cell??

18 Semantic Web modeling Why? What is a cell? Well it depends Different concepts Different definitions

19 Semantic Web modeling Why? Semantic: A shared language based on definitions

20 Semantic Web modeling Why? Semantic: A shared language based on definitions Humans can understand it Formal definition

21 Semantic Web modeling Why? Semantic: A shared language based on definitions Humans can understand it Formal definition Computers can understand it Using URI from the World Wide Web

22 Semantic Web modeling Why? Example: the concept Title Humans can understand it A name given to the resource. Computers can understand it

23 Semantic Web modeling Why? From definitions to relations Modelling logically the World Part Of Part Of Cambridge United Kingdom Europe

24 Semantic Web modeling Why? Modelling: Making sense of unorganized information Raw (dumb) text: Haemophilia is a hereditary genetic disorders. Smart text: <e wikipedia:haemophilia>haemophilia</e> <e rdf:type>is a</e> <e wikipedia:genetic_disorder>genetic disorders</e>.

25 Semantic Web modeling Why? Models help people to communicate explain and make predictions mediate among multiple viewpoints Allemang D. & Hendler J.

26 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

27 Semantic Web modeling How? What is the language of Semantic Web? H2O H O H Expressivity

28 Semantic Web modeling How? What is the language of Semantic Web? RDF RDFS OWL Expressivity

29 Semantic Web modeling How? RDF Resources Description Framework Smart text: <e wikipedia:haemophilia>haemophilia</e> <e rdf:type>is a</e> <e wikipedia:genetic_disorder>genetic disorders</e>. RDF: wikipedia:haemophilia rdf:type wikipedia:genetic_disorder. Subject Verb Object

30 Semantic Web modeling How? OWL: OWL Web Ontology Language wikipedia:haemophilia rdf:type owl:class. wikipedia:genetic_disorder rdf:type owl:class. wikipedia:haemophilia rdfs:subclassof wikipedia:genetic_disorder. Genetic Disorder Haemophilia If we say: example:kevin example:has_disease wikipedia:haemophilia. A machine can infer: example:kevin example:has_disease wikipedia:genetic_disorder.

31 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

32 Semantic Web modeling And Then? Semantic Web Machine Oriented: Data Representation RDF Data Validation Model, OWL Data Integration Same concepts are easy to mix by computers Data Query Powerful queries

33 Semantic Web modeling And Then? Semantic Web Machine Oriented: The current World Wide Web The Semantic Web Allemang D. & Hendler J.

34 Semantic Web modeling And Then? Query language: SPARQL Who are the people with Haemophilia? people? example:has_disease wikipedia:haemophilia example:kevin example:has_disease wikipedia:haemophilia.

35 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

36 A semantic model for corpora Motivations: Integration with biological data Enhanced querying over the corpus Sharing with community Modeling: Flexible Easy to reuse OWL Around instances

37 A semantic model for corpora Concepts - Classes Thing Sentence Corpus Annotated_Sentence Non_Annotated_Sentence Semantic_Type Article SPE DISO Annotation CHED PRGE

38 A semantic model for corpora Individuals CALBC entities Sentence Article Corpus Non_Annotated_Sentence CALBC Semantic_Type PRGE CHED Annotated_Sentence Article1 Sentence2 Sentence1 Annotation1 rdf:type rdfs:subclassof Annotation2 Annotation

39 A semantic model for corpora Relations - Properties Sentence X date dc:dateaccepted calbc:has_sentence URI dc:identifier Article X calbc:is_in Corpus X dc:creator dc:title calbc:pubmed_uri Literal Literal URI

40 A semantic model for corpora Relations - Properties Sentence X calbc:is_in Article X calbc:has_annotation Annotation X

41 A semantic model for corpora Relations - Properties Annotation X calbc:is_in Sentence X calbc:has_string Literal

42 A semantic model for corpora Relations - Properties Sentence X rdf:type Annotation X calbc:is_in calbc:has_string Normalization Literal

43 Outline Biological motivations Semantic Web modeling Why? How? And then? A semantic model for corpora Further integration and federated queries

44 Further integration and federated queries Query: I want the list of all annotated sentences in CALBC SELECT Sentence? WHERE { Sentence? calbc:is_in CALBC Sentence? rdf:type Annotated_Sentence }

45 Further integration and federated queries Query: Retrieve titles of articles with a PRGE annotation SELECT Title? WHERE { Article? Article? rdf:type dc:title Article Title? Article? Sentence? calbc:has_sentence calbc:has_annotation Sentence? Annotation? } Annotation? rdf:type PRGE

46 Further integration and federated queries Query: Retrieve the sentences having both an annotations of type PRGE and type CHED SELECT Sentence? WHERE { Sentence? Sentence? calbc:has_annotation calbc:has_annotation Annotation1? Annotation2? Annotation1? rdf:type PRGE } Annotation2? rdf:type CHED

47 Further integration and federated queries Query: Which are the sentences with 2 protein annotations? These proteins have to be known to interact together from Uniprot. List these annotations and the sentences. SELECT Sentence?, Annotation1?, Annotation2? WHERE { Sentence? Sentence? Annotation1? Annotation2? Annotation1? Annotation2? calbc:has_annotation calbc:has_annotation rdf:type rdf:type rdf:type rdf:type PRGE PRGE Annotation1? Annotation2? UniprotEntry1? UniprotEntry2? } UniprotEntry1? uniprot:participant UniprotEntry2?

48 Conclusion Normalization is the key for further integration Semantic queries can be run over the CALBC corpus The model flexible enough to be extended to other corpora Door open to semantic use of CALBC with other bio-resources

49 Thank you for you attention! Slides are online:

50 Credits Master headline