1. Mining Semantic Descriptions of Bioinformatics Web Resources from the Literature Hammad Afzal, Robert Stevens, Goran Nenadic School of Computer Science University of Manchester G.Nenadic@manchester.ac.uk

2. Motivation  A number of bioinformatics tools and resources available for service use and composition guessimate is 3000+ Web Services publically available how to find a service, what is out there to use? provenance?  Semantic annotation of bioinformatics services annotate functional capabilities e.g. Taverna, myGrid, myExperiment, EBI, BioMOBY  Not only services and tools databases, repositories, corpora

3. Motivation  Manual curation e.g. myGrid, BioCatalogue etc. e.g. Taverna/Feta: only ~15-20% functionally described backlog – and the number of services is growing  Annotations combine textual descriptions ontological mappings

4. Example text ontological descriptions - multiple local align. - Soaplab

5. BioCatalogue  Single registration point for Web Service providers  Single search site for scientists and developers  Place where the community can find contacts and meet the experts and maintainers of these services  Community-sourced annotation, expert oversee  Mixed annotations: free text, tags, controlled vocabularies, community ontologies

6. BioCatalogue Beta version at http://beta.biocatalogue.org/ Launch June 2009 at ISMB

7. Our approach  Collect service semantic descriptions by extracting and integrating information from text resources full text bioinformatics journal publications  Approach: identify descriptors that are used for service and resource annotations locate them in text infer the annotations  textual evidence and mappings to an ontology

8. The rest of the talk  Methodology mining bioinformatics terminology extraction of service description profiles  Experiments and results semi-automated curation  What next?

9. Methodology Corpus Information Retrieval Sentence Filtering Domain Ontology (e.g. myGrid) Domain Ontology (e.g. myGrid) Semantic Description of Services Identifying Topic Related Terms Text Mining Engine (Information Extraction) Semantic Network of Services Service Discovery

10. Bioinformatics terminology 1) get a corpus 2) get all terms 3) get seed examples 4) find relevant ones using term profiling and comparison to seed examples Learn bioinformatics terms from literature

11. Bioinformatics terminology  Use seed terms to bootstrap e.g. known descriptors used in existing service descriptions, either in literature or service repositories  250 terms identified, manual pruning after automatic term recognition examples of lexical constituents and textual behaviour (pragmatics)  lexical profiling  contextual profiling

12. Bioinformatics terminology  Lexical profiling what is in the name  Contextual profiling characterise sentences in which terms appear (nouns, verbs and context-patterns)  Comparing candidate term profiles to average seed term best-match

13. Bioinformatics terminology Two domain experts evaluated the top 300 terms

14. Semantic classes – myGrid  Informatics concepts general concepts of data, data structures, databases, metadata  Bioinformatics concepts domain-specific data sources and algorithms for searching and analysing data e.g. Smith-Waterman algorithm

15. Semantic classes – myGrid  Molecular biology concepts higher level concepts used to describe bioinformatics data types, used as inputs and outputs in services e.g. protein sequence, nucleic acid sequence  Task concepts generic tasks a service operation can perform e.g. retrieving, displaying, aligning

16. Semantic classes  Engineered from MyGrid bioinformatics sub-ontology classexamples Algorithm SigCalc algorithm, CHAOS local alignment, SNP analysis, KEGG Genome-based approach, GeneMark method, K-fold cross validation procedure Application PreBIND Searcher program, Apollo2Go Web Service, FLIP application, Apollo Genome Annotation curation tool, GenePix software, Pegasys system Data GeneBank record, Genome Microbial CoDing sequences, Drug Data report Data resource PIR Protein Information Resource, BIND database, TIGR dataset, BioMOBY Public Code repository

17. Semantic classes and instances

19. Service mentions  Named-entity recognition (NER) task  Recognition of service mentions using terminological (semantic) heads of automatically recognised terms  Apollo2Go Web Service is an Application  BIND database is a Data source  assign the corresponding semantic class Hearst patterns (co-ordinations, appositions, enumerations, etc.)

20. Semantic descriptors  Recognition of phrases depicting semantic roles used to describe services  Flexible dictionary look-up terms from myGrid ontology terms/noun phrases from existing descriptions of bioinformatics resources (collected from Taverna and other Web service providers).

21. Mining service descriptions

22. Extraction/functional rules  Predicate-driven rules: each verb associated with the type of “information content” it provides

23. Extraction/functional rules  Manually designed predicate-driven rules: Subject (Arg) – Verb (Predicate) – Object (Arg)  Applied on dependency parsed sentences Stanford parser no phrase structures complex sentences information in sub-clause

24. Extraction/functional rules  Phrase structures identified and integrate with the dependency  Predicate-dependent rules applied to extract specific ‘content’ and profile the services  Profiles collated for all mentions service name variation

25. Semantic service profiles  For a given service, collection of descriptors, including parameters links to other related instances related myGrid ontology semantic labels “informative” sentences

26. Example – GeneClass  Descriptors

27. Example – GeneClass  Functions, parameters

28. Example – GeneClass  Sentences We extend the original GeneClass algorithm to use all target genes for which both motif and expression data is available. In order to study different aspects of target gene regulation we use different sets of motifs and parents with the GeneClass algorithm. The GeneClass algorithm for predicting differential gene expression starts with a candidate set of motifs; representing known or putative regulatory element sequence patterns and a candidate set of regulators or parentSS.

29. Experiments  2120 BMC Bioinformatics articles full-text articles before March 2008  Service descriptors dictionary 471 descriptors from myGrid/Feta 450 descriptors collected from other bioinformatics service/tools providers  108 predicates used

30. Experiments  Number of candidate resources

31.  Number of descriptions collected using rules Experiments

32. Evaluated for their capability to be used for semantic description of a given bioinformatics resource irrelevant partially useful useful HeatMapper The HeatMapper tool has already proven to be very useful in several studies Kalign To compare Kalign to other MSA programs, the following test sets were used. Cognitor To add a new species to the COG system, the annotated protein sequences from the respective genome were compared to the proteins in the COG database by using the BLAST program and assigned to pre-existing COGs by using the COGNITOR program Evaluation of semantic profiles

33.  Two experiments:  5 well-known resources with descriptions already available  excellent rating for sentences  average rating for semantic descriptors  predicate functions  5 new, unknown resources  excellent rating for sentences  average rating for semantic descriptors  predicate functions Evaluation of semantic profiles

34. What next?  Good recall, poor precision context needs a better model  Mining parameter values sub-language of parameters  Candidate service/resource mentions an entity whose profile looks like a service comparison of semantic profiles network of services [ISMB 2009]  Do we have good service ontologies? http://gnode1.mib.man.ac.uk/bioinf

35. Conclusion  Literature mining approach to service description and annotation  Aims reduce curation efforts provide semantic synopses of services for the Semantic Web  Potential of text mining integration with other annotation approaches extracting the entire service context is still challenging

36. Acknowledgements  gnTEAM (text extraction, analitics, mining) H. Yang, I. Spasic, H. Afzal, A. Gledson, J. Eales, M. Greenwood, F. Sarafraz  myGrid team: Franck Tanoh  BBSRC “Mining term associations from literature to support knowledge discovery in biology” (2005-2008) “pubmed2ensembl” (2009-2010) “BioCatalogue” (2008-2011)

37. Announcement  Journal of BioMedical Semantics published by BioMed Central launched at ISMB 2009  Topics include Infrastructure for biomedical semantics  semantic resources and repositories  meta-data management and resource description  knowledge representation and semantic frameworks  Biomedical Semantic Web  life-long management of semantic resources Semantic mining, annotation and analysis