Semantic (Web) Technologies for Translational Research in Life Sciences


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  • Cognitive model, cognitive behavioral model
  • In parasite research, create new strains of a parasite by knocking out specific genes. So, given a cloned sample, we may need to know the gene(s) that was knocked out.Both these scenarios are real world examples of the importance of provenance. There are many research issues in provenance management. This presentation is on addressing 1) the provenance modeling issue. Specifically, provenance interoperability, consistent modeling, and reduction of terminological heterogeneity. (2) Provenance Query
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  • Semantic (Web) Technologies for Translational Research in Life Sciences

    1. 1. Semantic (Web) Technologies for Translational Research in Life Sciences<br />Ohio State University, June 16, 2011<br />Amit P. Sheth<br />Ohio Center ofExcellence in Knowledge-enabled Computing (Kno.e.sis)<br /><br />Thanks to Kno.e.sis team (Satya, Priti, Rama, and Ajith);<br />Collaborators at CTEGD UGA(Dr. Tarleton, Brent Weatherly), NLM(Olivier Bodenreider), CCRC, UGA (Will York), NCBO/Stanford, <br />CITAR/WSU<br />
    2. 2. Kno.e.sis: Ohio Center of Excellence in Knowledge-enabled Computing<br />
    3. 3. Web ofpeople<br /> - social networks, user-createdcasualcontent<br />Web of resources<br /> - data, service, data, mashups<br />Web of databases<br /> - dynamically generated pages<br /> - web query interfaces<br />Web of pages<br /> - text, manually created links<br /> - extensive navigation<br />Evolutionof Web & Semantic Computing<br />Tech assimilated in life<br />Web ofSensors, Devices/IoT<br />- 40 billionsensors, 5 billionmobile connections<br />2007<br />Situations,<br />Events<br />Web 3.0<br />Semantic TechnologyUsed<br />Objects<br />Web 2.0<br />Patterns<br />Keywords<br />1997<br />Web 1.0<br />
    4. 4. Outline<br />Semantic Web – very brief intro<br />Scenarios to demonstrate the applications and benefit of semantic web technologies<br />HealthCare<br />BiomedicalResearch<br />Translational<br />
    5. 5. Biomedical Informatics...<br />Biomedical Informatics<br />Pubmed<br />Clinical <br /><br />...needs a connection<br />Hypothesis Validation<br />Experiment design<br />Predictions<br />Personalized medicine<br />Semantic Web research aims at<br />providing this connection!<br />Etiology <br />Pathogenesis<br />Clinical findings<br />Diagnosis<br />Prognosis<br />Treatment<br />Genome<br />Transcriptome<br />Proteome<br />Metabolome<br />Physiome<br />...ome<br />More advanced capabilities for <br /> search, <br /> integration, <br /> analysis, <br /> linking to new insights <br /> and discoveries!<br />Genbank<br />Uniprot<br />Medical Informatics<br />Bioinformatics<br />
    6. 6. Decision Making, Insights, InnovationsHuman Performance<br />Data and Facts<br />Knowledge and Understanding<br />Health & Performance<br />Cognitive Science, Psychology<br />Neuroscience<br />Anatomy, Physiology<br />Cellular biology<br />Molecular Biology<br />ACATATGGGTACTATTTACTATTCATGGGTACTATTTATGGCATATGGCGTACTATTCTAATCCTATATCCGTCTAATCTATTTACTATTATCTATTACTATACCTTTTGGGGAAAAAAATTCTATACCGTCTAATCCTATAAATCAAGCCG<br />Biochemistry<br />
    7. 7. Semantic Web standards @ W3C<br />Semantic Web is built in a layered manner<br />Not everybody needs all the layers<br />…<br />Queries: SPARQL, Rules: RIF<br />Semantic Web<br />Rich ontologies: OWL<br />Simple data models & taxonomies: RDF Schema <br />Uniformmetamodel: RDF+ URI <br />Encoding structure: XML <br />Encoding characters : Unicode <br />
    8. 8. Linked Data: Semantic Web “diluted”<br />Achieve for data what Web did to documents<br />Relationship with the original Semantic Web vision: no AI, no agents, no autonomy<br />Interoperability is still very important<br />interoperability of formats<br />interoperability of semantics<br />Enables interchange of large data sets<br />(thus very useful in, say, collaborative research)<br />Semantic Web vision is largely predicated on the availability of data<br />Linked Data is a movement that gets us there<br />Thanks – OraLassila<br />
    9. 9. Opportunity: exploiting clinical and biomedical data<br />text<br />Health <br />Information <br />Services<br />Elsevier <br />iConsult<br />Scientific <br />Literature<br />PubMed<br />300 Documents <br />Published Online <br />each day<br />User-contributed <br />Content (Informal)<br />GeneRifs<br />WikiGene<br />NCBI <br />Public Datasets<br />Genome, <br />Protein DBs<br />new sequences<br />daily<br />Laboratory <br />Data<br />Lab tests, <br />RTPCR,<br />Mass spec<br />Clinical Data<br />Personal <br />health history<br />Search, browsing, complex query, integration, workflow, analysis, hypothesis validation, decision support.<br />
    10. 10. Major Community Efforts<br />W3C Semantic Web Health Care & Life Sciences Interest Group:<br />Clinical Observations Interoperability: EMR + Clinical Trials:<br />National Center for Biomedical Ontologies:<br />
    11. 11. Major SW Projects<br />OpenPHACTS: A knowledge management project of the Innovative Medicines Initiative (IMI), a unique partnership between the European Community and the European Federation of Pharmaceutical Industries and Associations (EFPIA).<br />LarKC: develop the Large Knowledge Collider, a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web.<br />NCBO: contribute to collaborative science and translational research.<br />
    12. 12. Semantic Web Enablers and Techniques<br />Ontology: Agreement with Common Vocabulary & Domain Knowledge; Schema + Knowledge base<br />Semantic Annotation (meatadata Extraction): Manual, Semi-automatic (automatic with human verification), Automatic<br />Semantic Computation: semantics enabled search, integration, complex queries, analysis (paths, subgraph), pattern finding, mining, inferencing, reasoning, hypothesis validation, discovery, visualization<br />
    13. 13. Drug Ontology Hierarchy(showing is-a relationships)<br />owl:thing<br />prescription_drug_ brand_name<br />brandname_undeclared<br />brandname_composite<br />prescription_drug<br />monograph_ix_class<br />cpnum_ group<br />prescription_drug_ property<br />indication_ property<br />formulary_ property<br />non_drug_ reactant<br />interaction_property<br />property<br />formulary<br />brandname_individual<br />interaction_with_prescription_drug<br />interaction<br />indication<br />generic_ individual<br />prescription_drug_ generic<br />generic_ composite<br />interaction_with_monograph_ix_class<br />interaction_ with_non_ drug_reactant<br />
    14. 14. N-glycan_beta_GlcNAc_9<br />N-glycan_alpha_man_4<br />GNT-Vattaches GlcNAc at position 6<br />N-acetyl-glucosaminyl_transferase_V<br />UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 <=> <br />UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2 <br />UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021 <br />N-Glycosylation metabolic pathway<br />GNT-Iattaches GlcNAc at position 2<br />
    15. 15. Maturing capabilites and ongoing research<br />Ontology Creation<br />SemanticAnnotation & Textmining: Entity recognition, Relationship extraction<br />SemanticIntegration & Provenance: <br />Integratingalltypesof data used in biomedicalresearch: text, experimetal data, curated/structured/publicandmultimedia<br />Semantic search, browsing, analysis<br />Clinical and Scientific Workflows with semantic web services<br />SemanticExplorationofscientific literature, Undiscovered publicknowledge<br />
    16. 16. Project 1: ASEMR<br />Why:Improve Quality of Care and Decision Making without loss of Efficiency in active Cardiology practice. <br />What: Use of semantic Web technologies for clinical decision support<br />Where: Athens Heart Center & its partners and labs<br />Status: In usecontinuously since 01/2006<br />
    17. 17. Operational since January 2006<br />Details:<br />
    18. 18. Active Semantic EMR<br />Annotate ICD9s<br />Annotate Doctors<br />Lexical Annotation<br />Insurance Formulary<br />Level 3 Drug Interaction<br />Drug Allergy<br />Demo at:<br />
    19. 19. Project 2: Glycomics<br />Why:To help in the treatment of certain kinds of cancer and Parkinson's Disease.<br />What: Semantic Annotation of Experiment Data<br />Where:Complex Carbohydrate Research Center, UGA<br />Status: Research prototype in use<br />Workflow with Semantic Annotation of Experimental Data already in use<br />
    20. 20. N-Glycosylation Process (NGP)<br />Cell Culture<br />extract<br />Glycoprotein Fraction<br />proteolysis<br />Glycopeptides Fraction<br />1<br />Separation technique I<br />n<br />Glycopeptides Fraction<br />PNGase<br />n<br />Peptide Fraction<br />Separation technique II<br />n*m<br />Peptide Fraction<br />Mass spectrometry<br />ms data<br />ms/ms data<br />Data reduction<br />Data reduction<br />ms peaklist<br />ms/ms peaklist<br />binning<br />Peptide identification<br />Glycopeptide identification<br />and quantification<br />Peptide list<br />N-dimensional array<br />Data correlation<br />Signal integration<br />
    21. 21. Agent <br />Agent <br />Agent <br />Agent <br />Biological Sample <br />Analysis by MS/MS<br />Raw Data to<br />Standard Format<br />Data<br />Pre- process<br />DB Search<br />(Mascot/Sequest)<br />Results Post-process<br />(ProValt)<br />O<br />I<br />O<br />I<br />O<br />I<br />O<br />I<br />O<br />Storage<br />Standard Format<br />Data<br />Raw Data<br />Filtered Data<br />Search Results<br />Final Output<br />Biological Information<br />Scientific workflow for proteome analysis<br />Semantic<br />Annotation<br />Applications<br />
    22. 22. Semantic Annotation of Experimental Data <br />parent ion charge<br />830.9570 194.9604 2<br /> 580.2985 0.3592<br /> 688.3214 0.2526<br /> 779.4759 38.4939<br /> 784.3607 21.7736<br /> 1543.7476 1.3822<br /> 1544.7595 2.9977<br /> 1562.8113 37.4790<br /> 1660.7776 476.5043<br />parent ion m/z<br />parent ionabundance<br />fragment ion m/z<br />fragment ionabundance<br />ms/ms peaklist data<br />Mass Spectrometry (MS) Data<br />
    23. 23. Semantic Annotation of Experimental Data <br /><ms-ms_peak_list><br /><parameter instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_spectrometer”<br /> mode=“ms-ms”/><br /> <parent_ionm-z=“830.9570” abundance=“194.9604” z=“2”/><br /> <fragment_ionm-z=“580.2985” abundance=“0.3592”/><br /> <fragment_ionm-z=“688.3214” abundance=“0.2526”/><br /> <fragment_ionm-z=“779.4759” abundance=“38.4939”/><br /> <fragment_ionm-z=“784.3607” abundance=“21.7736”/><br /> <fragment_ionm-z=“1543.7476” abundance=“1.3822”/><br /> <fragment_ionm-z=“1544.7595” abundance=“2.9977”/><br /> <fragment_ionm-z=“1562.8113” abundance=“37.4790”/><br /> <fragment_ionm-z=“1660.7776” abundance=“476.5043”/><br /></ms-ms_peak_list><br />OntologicalConcepts<br />Semantically Annotated MS Data<br />
    24. 24. Project 3: <br />Why: To associate genotype and phenotype information for knowledge discovery<br />What:integrated data sources to run complex queries<br />Enriching data with ontologies for integration, querying, and automation<br />Ontologies beyond vocabularies: the power of relationships<br />Where: NCRR (NIH) <br />Status:Completed<br />
    25. 25. Use data to test hypothesis<br />Gene name<br />GO<br />Interactions<br />gene<br />Sequence<br />PubMed<br />OMIM<br />Link between glycosyltransferase activity and congenital muscular dystrophy?<br />Glycosyltransferase<br />Congenital muscular dystrophy<br />Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07<br />
    26. 26. In a Web pages world…<br />(GeneID: 9215)<br />has_associated_disease<br />Congenital muscular dystrophy,type 1D<br />has_molecular_function<br />Acetylglucosaminyl-transferase activity<br />Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07<br />
    27. 27. With the semantically enhanced data<br />glycosyltransferase<br />GO:0016757<br />isa<br />GO:0008194<br />GO:0016758<br />acetylglucosaminyl-transferase<br />GO:0008375<br />has_molecular_function<br />acetylglucosaminyl-transferase<br />GO:0008375<br />EG:9215<br />LARGE<br />Muscular dystrophy, congenital, type 1D <br />MIM:608840<br />has_associated_phenotype<br />SELECT DISTINCT ?t ?g ?d {<br /> ?t is_a GO:0016757 .<br /> ?g has molecular function ?t .<br /> ?g has_associated_phenotype ?b2 .<br /> ?b2 has_textual_description ?d .<br />FILTER (?d, “muscular distrophy”, “i”) . FILTER (?d, “congenital”, “i”) }<br />From medinfo paper.<br />Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07<br />
    28. 28. Project 4: Nicotine Dependence<br />Why: For understanding the genetic basis of nicotine dependence. <br />What:Integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base.<br />How: Semantic Web technologies (especially RDF, OWL, and SPARQL) support information integration and make it easy to create semantic mashups (semantically integrated resources). <br />Where: NLM (NIH) <br />Status: Completed research<br />
    29. 29. Motivation<br />NIDA study on nicotine dependency<br />List of candidate genes in humans<br />Analysis objectives include:<br /><ul><li>Find interactions between genes
    30. 30. Identification of active genes – maximum number of pathways
    31. 31. Identification of genes based on anatomical locations</li></ul>Requires integration of genome and biological pathway information<br />
    32. 32. Genome and pathway information integration<br />KEGG<br />Reactome<br />HumanCyc<br /><ul><li>pathway
    33. 33. protein
    34. 34. pmid</li></ul>Entrez Gene<br /><ul><li>pathway
    35. 35. protein
    36. 36. pmid
    37. 37. pathway
    38. 38. protein
    39. 39. pmid</li></ul>GeneOntology<br />HomoloGene<br /><ul><li>GO ID
    40. 40. HomoloGene ID</li></li></ul><li>JBI<br />
    41. 41. Entrez<br />Knowledge<br />Model<br />(EKoM)<br />BioPAX<br />ontology<br />
    42. 42. Results: Gene Pathway network and Hub Genes involved with Nicotine Dependence<br />
    43. 43. Project 5: T. cruzi SPSE <br />Why: For Integrative Parasite Research to help expedite knowledge discovery<br />What: Semantics and Services Enabled Problem Solving Environment (PSE) for Trypanosomacruzi<br />Where: Center for Tropical and Emerging Global Diseases (CTEGD), UGA <br />Who: Kno.e.sis, UGA, NCBO (Stanford)<br />Status: Research prototype – in regular lab use<br />
    44. 44. Project Outline<br />Data Sources<br /><ul><li>Internal Lab Data</li></ul>Gene Knockout<br />Strain Creation<br />Microarray<br />Proteome<br /><ul><li>External Database</li></ul>Ontological Infrastructure<br /><ul><li>Parasite Lifecycle
    45. 45. Parasite Experiment</li></ul>Query processing<br /><ul><li>Cuebee</li></ul>Results <br />
    46. 46. Provenance in Parasite Research<br />Gene Name<br />Sequence<br />Extraction<br />Gene Knockout and Strain Creation*<br />Related Queries from Biologists<br />List all groups in the lab that used a Target Region Plasmid?<br />Which researcher created a new strain of the parasite (with ID = 66)?<br />An experiment was not successful – has this experiment been conducted earlier? What were the results? <br />3‘ & 5’<br />Region<br />Drug Resistant Plasmid<br />Gene Name<br />Plasmid<br />Construction<br />Knockout Construct Plasmid<br />T.Cruzi sample<br />?<br />Transfection<br />Transfected Sample<br />Drug<br />Selection<br />Cloned Sample<br />Selected Sample<br />Cell<br />Cloning<br />Cloned<br />Sample<br />*T.cruzi Semantic Problem Solving Environment Project, Courtesy of D.B. Weatherly and Flora Logan, Tarleton Lab, University of Georgia<br />
    47. 47. Research Accomplishments<br />SPSE<br /><ul><li>Integrated internal data with external databases, such as KEGG, GO, and some datasets on TriTrypDB
    48. 48. Developed semantic provenance framework and influence W3C community
    49. 49. SPSE supports complex biological queries that help find gene knockout, drug and/or vaccination targets. For example:
    50. 50. Show me proteins that are downregulated in the epimastigote stage and exist in a single metabolic pathway.
    51. 51. Give me the gene knockout summaries, both for plasmid construction and strain creation, for all gene knockout targets that are 2-fold upregulated in amastigotes at the transcript level and that have orthologs in Leishmania but not in Trypanosomabrucei.</li></li></ul><li>Knowledge driven query formulation<br />Complex queries can also include:<br />- on-the-fly Web services execution to retrieve additional data<br /><ul><li> inference rules to make implicit knowledge explicit</li></li></ul><li>Project 6: HPCO<br />Why:collaborative knowledge exploration over scientific literature <br />What: An up-to-date knowledge based literature search and exploration framework <br />How: Using information extraction, conventional IR, and semantic web technologies for collaborative literature exploration<br />Where: AFRL<br />Status: Completed research<br />
    52. 52. Focused KB Work Flow (Use case: HPCO)<br />HPC keywords<br />Doozer: Base Hierarchy from Wikipedia<br />Focused Pattern based extraction<br />SenseLab Neuroscience Ontologies<br />Initial KB Creation<br />Meta Knowledgebase<br />PubMed Abstracts<br />Knoesis: Parsing based NLP Triples <br /> Enrich Knowledge Base<br />NLM: Rule based BKR Triples<br />Final Knowledge Base<br />
    53. 53. Triple Extraction Approaches<br />Open Extraction<br /> No fixed number of predetermined entities and predicates<br />At Knoesis – NLP (parsing and dependency trees)<br />Supervised Extraction<br />Predetermined set of entities and predicates<br />At Knoesis – Pattern based extraction to connect entities in the base hierarchy using statistical techniques<br />At NLM – NLP and rule based approaches<br />
    54. 54. Mapping Triples to Base Hierarchy<br />Entities in both subject and object must contain at least one concept from the hierarchy to be mapped to the KB<br />Preliminary synonyms based on anchor labels and page redirects in Wikipedia<br />Prolactostatin redirects to Dopamine<br />Predicates (verbs) and entities are subjected to stemming using Wordnet<br />
    55. 55. Scooner: Full Architecture<br />
    56. 56. Scooner Features<br />Knowledge-based browsing: Relations window, inverse relations, creating trails<br />Persistent projects: Work bench, browsing history, comments, filtering<br />Collaboration: comments, dashboard, exporting (sub)projects, importing projects<br />
    57. 57. Scooner Screenshot<br />
    58. 58. New Knowledge/hypothesis Example<br />Three triples from different abstracts<br />VIP Peptide – increases – Catecholamine Biosynthesis<br />Catecholamines – induce – β-adrenergic receptor activity<br />β-adrenergic receptors – are involved – fear conditioning<br />New implicit knowledge<br />VIP Peptide – affects – fear conditioning<br />Caveat: Each triple above was observed in a different organism (cows, mice, humans), but still interesting hypothesis. Scooner’s contextual browsing makes this clear to the user.<br />
    59. 59. Project 7: Drug Abuse<br />Why: To study social trends in pharmaceutical opioid abuse<br />What: <br />Describe drug user’s knowledge, attitudes, and behaviors related to illicit use of OxyContin®<br />Describe temporal patterns of non-medical use of OxyContin® tablets as discussed on Web-based forums <br />Where: CITAR (Center for Interventions, Treatment and Addictions Research) at Wright State Univ.<br />Status: In-progress (Recently funded from NIDA)<br />
    60. 60.
    61. 61. Project 8: NMR<br />Why: Streamline the NMR data processing tasks. Processing NMR experimental data is complex and time consuming. <br />What: Providing biologists with tools to effectively process and manage Nuclear Magnetic Resonance (NMR) experimental data.<br />How: Use Domain Specific Languages (DSL) to create scientist-friendly abstractions for complex statistical workflows. Use semantics based techniques to store and manage data.<br />Where: Air Force Research Lab<br />Status: In progress<br />
    62. 62. Motivation<br /><ul><li>NMR spectroscopy data is complex and require significant statistical processing before interpreting</li></ul>- Writing these processes is hard<br />- They have to run on many different computational platforms<br />- The data collected has to be shared among multiple parties<br />A simple NMR spectrum, highlighting peaks that correspond to the presence of specific chemical compounds<br />
    63. 63. A complex NMR spectrum, marked with chemical compound identifiers by human observers.<br />
    64. 64. Project Outline<br /><ul><li>Identify fundamental operators required for common NMR processing tasks
    65. 65. Use a DSL to provide abstractions for the operators (named SCALE)
    66. 66. Build compilers to generate multiple, cloud-enabled applications </li></li></ul><li>Real time Healthcare Information<br />Matching medical requirements with availability of medical resources (Mumbai, India)<br />Project HERO Helpline for Emergency Response Operations<br />For patients seeking for immediate medical help <br />Medical awareness in rural India<br />mMitra, info. service during pregnancy and childhood emergency <br />Medical <br />Emergency<br />Medical<br />Resources <br />Information bridge<br />
    67. 67. Future Interoperability Challenge:360 degree health <br />Insurance, <br />Financial Aspects<br />Clinical Care<br />Follow up,<br />Lifestyle <br />Genetic Tests… <br />Profiles<br />Clinical Trials<br />Social Media<br />
    68. 68. For each component in 360-degree health care, we have data, processes, knowledge and experience. Interoperability solutions need to encompass all these!<br />Possibly largest growth in data will be in sensors (eg Body Area Networks, Biosensors) and social content. Extensive use of mobile phones.<br />Credit:<br />
    69. 69. Summary<br />Semantic Web is an “interoperability technology”<br />Semantic Web provides the needed interoperability, and can accommodate all necessary “points of view”<br />Linked Data as a way of sharing data is highly promising<br />Many examples of viable usage of Semantic Web technologies<br />Words of warning about deployment<br />Significant research challenges remain as Health presents the most complex domain<br />
    70. 70. Representative References<br />A. Sheth, S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, and K. Gallagher, Active Semantic Electronic Medical Record, Intl Semantic Web Conference, 2006.<br />SatyaSahoo, Olivier Bodenreider, Kelly Zeng, and AmitSheth, An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and Phenotype InformationWWW2007 HCLS Workshop, May 2007. <br />Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and AmitSheth, From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp. 1260-4<br />Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner , Amit P. Sheth, An ontology-driven semantic mash-up of gene and biological pathway information: Application to the domain of nicotine dependence, Journal of Biomedical Informatics, 2008.<br />CarticRamakrishnan, Krzysztof J. Kochut, and AmitSheth, "A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583-596<br />Satya S. Sahoo, Christopher Thomas, AmitSheth, William S. York, and SamirTartir, "Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies", 15th International World Wide Web Conference (WWW2006), Edinburgh, Scotland, May 23-26, 2006.<br />Satya S. Sahoo, Olivier Bodenreider, Pascal Hitzler, Amit Sheth and KrishnaprasadThirunarayan, 'Provenance Context Entity (PaCE): Scalable provenance tracking for scientific RDF data.’ SSDBM, Heidelberg, Germany 2010.<br />Papers:<br />Demos at:<br />