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Translational Medicine from a Semantic Web Perspective Eric Neumann W3C June 16, 2006 Drug Discovery and Medicine • Health • Practice • Safety • Prevention • Privacy • Knowledge Hygieia, G. Klimt 2 Data Expansion Large Data Sets Variables >> Samples Many New Data Types Combine Which Formats? 3 Where Information Advances are Most Needed • Supporting Innovative Applications in R&D – Translational Medicine (Biomarkers) – Molecular Mechanisms (Systems) – Data Provenance, Rich Annotation • Clinical Information – eHealth Records, EDC, Clinical Submission Documents – Safety Information, Pharmacovigilance, Adverse Events, Biomarker data • Standards – Central Data Sources • Genomics, Diseases, Chemistry, Toxicology – MetaData • Ontologies • Vocabularies 4 Knowledge “--is the human acquired capacity (both potential and actual) to take effective action in varied and uncertain situations.” How does this translate into using Information Systems better in support of Innovation? 5 Drug Discovery Challenges Knowledge Predictiveness • Knowledge of Target Mechanisms • Knowledge of Toxicity • Knowledge of Patient-Drug Profiles 6 Current Challenges: Drug Discovery • Business – – – – Costly, lengthy drug discovery process (12-14 years) Poor funding to find new uses for existing therapies (ie antibiotics) Insufficient economic drivers for certain disease areas Discovery and clinical trials design not well aligned with anticipating adverse effect detection • Post-launch surveillance is weak • Science & Technology – Counteracting the legacy of “Silos” – How to break away from the DD “conveyor belt model” to the “Translation model” • gaining and sharing insights throughout the process – The Benefit of New Targets for New Diseases – How to best identify safety and efficacy issues early on, so that cost and failure are reduced • A D3 Knowledge-base: Drugability and Safety 7 The Big Picture - Hard to understand from just a few Points of View 8 9 Complete view tells a very different Story 10 Distributed Nature of R&D Silos of Data… 11 Existing Web Data Throttles the R&D Potential R&D Scientist Integrating Data Manually Static, Untagged, Disjoint LIMS Bioinformatics Dolor Sit Amet Consectetuer Lacreet Dolore Euismod Volutpat Lacreet Dolore Magna Volutpat Dolor Sit Amet Consectetuer Lacreet Dolore Euismod Volutpat Lacreet Dolore Magna Volutpat Nibh Euismod Tincidunt Aliguam Erat Nibh Euismod Tincidunt Aliguam Erat Cheminformatics Public Data Sources 12 Data Integration: Biology Requirements Papers Disease Proteins Genes Retention Policy Assays Compounds Audit Trail Curation Ontology Experiment Tools 13 Semantic Web Data Integration R&D Scientist Dynamic, Linked, Searchable LIMS Bioinformatics Cheminformatics 14 Public Data Sources Raw Data MAGE ML Decision Support GO CDISC BioPAX Biomarker Qualification Translational Research Psi XML ICH ASN1. XLS SAS Tables Target Validation Semantic Bridge New Applications Safety CSV Toxicity 15 Key Technologies Pharmaceuticals use to Exchanging Knowledge 16 New Regulatory Issues Confronting Pharmaceuticals Tox/Efficacy ADME Optim from Innovation or Stagnation, FDA Report March 2004 17 Key Functionality • Ubiquity – Same identifiers for anything from anywhere • Discoverability – Global search on any entity • Interoperability – => Application independence: “Recombinant Data” 18 Additional Functionality • Provenance – Origin and history of data and annotations • Scalability – Over all potentially relevant data and content • Authentication/Security – – – – Single user and team identity and granular data security Non-repudiation of authorship Encryption of graphs Policy Awareness • Data Preservation – Long-term persistence by minimizing API needs 19 Translational Research and Personalized Medicine Biomedical Research -Two significant areas of HCLS activity - Span most areas of activity Biological Translational Medicine Clinical Clinical Research Clinical Practice Research Practice Personalized Medicine 20 HCLS Framework: Biomedical Research • Molecular, Cellular and Systems Biology/Physiology – Organism as an integrated an interacting network of genes, proteins and biochemical reactions – Human body as a system of interacting organs • Molecular Cell Biology/Genomic and Proteomic Research – Gene Sequencing, Genotyping, Protein Structures – Cell Signaling and other Pathways • Biomarker Research – Discovery of genes and gene products that can be used to measure disease progression or impacts of drug • Pharmaco-genomics – Impact of genetic inheritance on • Drug Discovery and Translational Research – Use of preclinical research to identify promising drug candidates 21 HCLS Framework: Clinical Research • Clinical Trials – Determination of efficacy, impact and safety of drugs for particular diseases • Pharmaco-vigilance/ADE Surveillance – Monitoring of impacts of drugs on patients, especially safety and adverse event related information • Patient Cohort Identification and Management – Identifying patient cohorts for drug trials is a challenging task • Translational Research – Test theories emerging from pre-clinical experimentation on disease affected human subjects • Development of EHRs/EMRs for both clinical research and practice – Currently EHRs/EMRs focussed on clinical workflow processes – Re-using that information for clinical research and trials is a challenging task 22 Translational Research • Improve communication between basic and clinical science so that more therapeutic insights may be derived from new scientific ideas - and vice versa. • Testing of theories emerging from preclinical experimentation on disease-affected human subjects. • Information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s). • http://www.translational-medicine.com/info/about • Reference NIH Digital Roadmap activity 24 Personalized Medicine • Propagation of insights from Genomic research into clinical practice • Impact of new Molecular diagnostic tests hitting the market – How can they be incorporated into clinical care? – How does one update current clinical guidelines to incorporate the use of these tests – How can one enable novel clinical decision support? • How can phenotypic characteristics and genomic markers be used to: – Stratify patient populations – “Personalize” clinical care • Genetic test results as risk factors • Therapeutic use of genomic markers 27 Ecosystem: Current State Characterized by silos with uncoordinated supply chains leading to inefficiencies in the system Patients National Institutes Of Health Patients, Public FDA Pharmaceutical Companies Hospitals Center for Disease Control Payors Universities, Academic Medical Centers (AMCs) Biomedical Research Clinical Practice Clinical Research Organizations (CROs) Hospitals Doctors Patients Clinical Trials/Research 29 Patients Clinical Practice Ecosystem: Goal State /* Need to expand this with Biomedical Research + Clinical Practice */ Biomedical Research Clinial Practice /* Need to expand this to include Healthcare and Biomedical Research Players as well… Show an integrated picture with “continuous” information flow */ 30 Use Case Flow: Drug Discovery and Development Qualified Targets Lead Generation Lead Optimization Toxicity & Safety KD Biomarkers Molecular Mechanisms Pharmacogenomics Clinical Trials 32 Drug Discovery & Development Knowledge Qualified Targets Molecular Mechanisms Lead Generation Toxicity & Safety Lead Optimization Pharmacogenomics Biomarkers Clinical Trials 33 Launch Semantic Web Drug DD Application Space Therapeutics Critical Path Chem Lib manufacturing NDA Production Genomics Clinical Studies HTS eADME Biology Compound Opt DMPK genes 35 Patent informatics Opportunities for Semantics in HealthCare • Enhanced interoperability via: – Semantic Tagging – Grounding of concepts in Standardized Vocabularies – Complex Definitions • Semantics-based Observation Capture • Inference on Diseases – Phenotypes – Genetics – Mechanisms • Semantics-based Clinical Decision Support – Guided Data Interpretation – Guided Ordering • Semantics-based Knowledge Management 36 Data Semantics in the Life Sciences Pathways, Biomarkers Publications Publications + data Image + Text Text Categorical Taxonomic Data Items Data Items Text + data items Histology Profiling Data Items genomics Systems Biology Complex Objects with Categorical/ Taxonomic Data Items Gene expression Complex Objects Clinical Findings Composite Objects with Embedded “process” Clinical trials Unstructured Data Types Structured and Complex Data Types 37 RDB => RDF Virtualized RDF 39 Use-Case: COSA Row Semantic <rdf:type Subject> Column Semantic <rdf:type Gene> Data Set 42 Use-Case: Experimental Design Definition Treatment W Cultured Cells Control Visible Microscopy Time Points Image Analysis Staining Fluorescent Microscopy Treatment Z 43 Case Study: Drug Safety ‘Safety Lenses’ • Lenses can ‘focus data in specific ways – Hepatoxicity, genotoxicity, hERG, metabolites • Can be “wrapped” around statistical tools • Aggregate other papers and findings (knowledge) in context with a particular project • Align animal studies with clinical results • Support special “Alert-channels” by regulators for each different toxicity issue • Integrate JIT information on newly published mechanisms of actions 44 Example: Knowledge Aggregation 45 Courtesy of BG-Medicine Case Study: Omics ApoA1 … … is produced by the Liver … is expressed less in Atherosclerotic Liver … is correlated with DKK1 … is cited regarding Tangier’s disease … has Tx Reg elements like HNFR1 Subject Verb Object 46 Scenario: Biomarker Qualification • Biomarker Roles – – – • Disease Toxicity Efficacy Molecular and cytological markers – Tissue-specific – High content screening derived information – Different sets associated with different predictive tools • Statistical discrimination based on selected samples – Predictive power – Alternative cluster prediction algorithms – Support qualifications from multiple studies (comparisons) • Causal mechanisms – Pathways – Population variation 48 BioMarker Semantics Disease Pathways +Samples Biomarker Set Significance & Strength 49 -Samples Scenario: Toxicity • Mechanisms – – – • Tissue-selective, Species-specific Pathways, Off-Targets Metabolites, PK sensitivity Evidence – Biomarkers • – • Drug Metabolism to toxic forms (CYP, SULT, UGT) Target interaction variability Potential vs. Demonstrated Predictions – – • Literature Population Variation – – – • In vitro assays (cell lines), Animal models, Clinical Phase 1 Data Mining Patterns Computational Modeling Working Solutions – – – Chemical modifications Dosing, Reformulation Documented animal <=> human similarity and variation 50 Knowledge Mining using Semantic Web “Gene Prioritization through Data Fusion” - Aerts et al, 2006, Nature -Use of quantitative and qualitative information for statistical ranking. -Can be used to identify novel genes involved in diseases 51 Case Study: BioPAX (Pathways) <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> <bp:step-interactions> <bp:MODULATION rdf:ID="xDshToXGSK3b"> <bp:keft rdf:resource="#xDsh"/> <bp:right rdf:resource="#xGSK-3beta"/> <bp:participants rdf:resource="#xGSK-3beta"/> <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#stri Dishevelled to GSK3beta</bp:name> <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema# IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchem INHIBITION</bp: control-type > <bp: participants rdf:resource="#xDsh"/> </bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP > QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. 52 Case Study: BioPAX (Pathways) <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> <bp:step-interactions> <bp:MODULATION rdf:ID="xDshToXGSK3b"> <bp:keft rdf:resource="#xDsh"/> <bp:right rdf:resource="#xGSK-3beta"/> <bp:participants rdf:resource="#xGSK-3beta"/> <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > <bp: participants rdf:resource="#xDsh"/> </bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP > 53 Case Study: BioPAX (Pathways) <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> Modulation <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> <bp:step-interactions> <bp:MODULATION rdf:ID="xDshToXGSK3b"> <bp:keft rdf:resource="#xDsh"/> <bp:right rdf:resource="#xGSK-3beta"/> <bp:participants rdf:resource="#xGSK-3beta"/> <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > <drug:affectedBy rdf:resource=”http://pharma.com/cmpd/CHIR99102"/> <bp: participants rdf:resource="#xDsh"/> </bp: MODULATION > </bp: step-interactions > </bp: PATHWAYSTEP > 54 affectedBy CHIR99102 Potential Linked Clinical Ontologies Clinical Obs Disease Descriptions SNOMED Applications CDISC ICD10 RCRIM (HL7) Clinical Trials Disease Models ontology Mechanisms Pathways (BioPAX) IRB Tox Extant ontologies Genomics Molecules Under development Bridge concept 55 Case Study: Drug Discovery Dashboards • Dashboards and Project Reports • Next generation browsers for semantic information via Semantic Lenses • Renders OWL-RDF, XML, and HTML documents • Lenses act as information aggregators and logic style-sheets add { ls:TheraTopic hs:classView:TopicView } 56 Drug Discovery Dashboard http://www.w3.org/2005/04/swls/BioDash Topic: GSK3beta Topic Disease: DiabetesT2 Alt Dis: Alzheimers Target: GSK3beta Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT 57 Bridging Chemistry and Molecular Biology Semantic Lenses: Different Views of the same data BioPax Components Target Model urn:lsid:uniprot.org:uniprot:P49841 Apply Correspondence Rule: if ?target.xref.lsid == ?bpx:prot.xref.lsid then ?target.correspondsTo.?bpx:prot 58 Bridging Chemistry and Molecular Biology •Lenses can aggregate, accentuate, or even analyze new result sets • Behind the lens, the data can be persistently stored as RDF-OWL • Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references 59 Pathway Polymorphisms •Merge directly onto pathway graph •Identify targets with lowest chance of genetic variance Non-synonymous polymorphisms from db-SNP •Predict parts of pathways with highest functional variability •Map genetic influence to potential pathway elements •Select mechanisms of action that are minimally impacted by polymorphisms 60 Knowledge Channels <item rdf:about="http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01"> <title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</title> <link>http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01</link> <description>Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006</description> <dc:creator>hannahr</dc:creator> <dc:date>2006-01-19T11:24:03Z</dc:date> <dc:subject>CLLSignalling&Processes</dc:subject> <connotea:uri> <dc:title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</dc:title> <dc:creator>A Sainz-Perez</dc:creator> <dc:creator>H Gary-Gouy</dc:creator> <dc:identifier> <connotea:PubMedID> <connotea:idValue>16408101</connotea:idValue> <rdf:value>PMID: 16408101</rdf:value> </connotea:PubMedID> </dc:identifier> <dc:date>2006-01-12</dc:date> <prism:publicationName>Leukemia</prism:publicationName> <prism:issn>0887-6924</prism:issn> </connotea:uri> </item> 61 Knowledge Channels <item rdf:about="http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01"> <title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</title> <link>http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01</link> <description>Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006</description> <dc:creator>hannahr</dc:creator> <dc:date>2006-01-19T11:24:03Z</dc:date> <dc:subject>CLLSignalling&Processes</dc:subject> <kn:nugget rdf:resource=“#N251”> <tn:expert>Giles Day </tn:expert> <tn:topic>pf#P38</tn:topic> <tn:kChannel>pf#Kinases</tn:kChannel > <tn:comment>This paper suggests a mechanism for P38 protection of CLL B-cells</tn:comment > </kn:nugget > <connotea:uri> <dc:title>High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia.</dc:title> <dc:creator>A Sainz-Perez</dc:creator> <dc:creator>H Gary-Gouy</dc:creator> <dc:identifier> <connotea:PubMedID> <connotea:idValue>16408101</connotea:idValue> <rdf:value>PMID: 16408101</rdf:value> </connotea:PubMedID> </dc:identifier> <dc:date>2006-01-12</dc:date> <prism:publicationName>Leukemia</prism:publicationName> <prism:issn>0887-6924</prism:issn> </connotea:uri> </item> 62 P38 paper nugget N251 expert Giles Day topic pf#P38 kChannel Pf#Kinases Case Study: Drug Safety ‘Safety Lenses’ • Lenses can ‘focus data in specific ways – Hepatoxicity, genotoxicity, hERG, metabolites • Can be “wrapped” around statistical tools • Aggregate other papers and findings (knowledge) in context with a particular project • Align animal studies with clinical results • Support special “Alert-channels” by regulators for each different toxicity issue • Integrate JIT information on newly published mechanisms of actions 63 GeneLogic GeneExpress Data • Additional relations and aspects can be defined additionally Diseased Tissue Links to OMIM (RDF) 65 Bar View of GeneExpress 66 ClinDash: Clinical Trials Browser Subjects •Values can be normalized across all measurables (rows) Clinical Obs •Samples can be aligned to their subjects using RDF rules Expression Data •Clustering can now be done over all measureables (rows) 67 68 69 70 71 W3C Launches Semantic Web for HealthCare and Life Sciences Interest Group • Interest Group formally launched Nov 2005: http://www.w3.org/2001/sw/hcls • First Domain Group for W3C - “…take SW through its paces” • An Open Scientific Forum for Discussing, Capturing, and Showcasing Best Practices • Recent life science members: Pfizer, Merck, Partners HealthCare, Teranode, Cerebra, NIST, U Manchester, Stanford U, AlzForum • SW Supporting Vendors: Oracle, IBM, HP, Siemens, AGFA, • Co-chairs: Dr. Tonya Hongsermeier (Partners HealthCare); Eric Neumann (Teranode) 76 HCLS Objectives • Share use cases, applications, demonstrations, experiences • Exposing collections • Developing vocabularies • Building / extending (where appropriate) core vocabularies for data integration 77 HCLS Activities • • • • • BioRDF - data + NLP as RDF BioONT - ontology coordination Scientific Publishing - evidence management Adaptive Clinical Protocols and Pathways Clinical Trials 78 BioRDF: NeuroCommons.org The Neurocommons project, a collaboration between Science Commons and the Teranode Corporation, is creating a free, public Semantic Web for neurological research. The project has three distinct goals: 1. To demonstrate that scientific impact and innovation is directly related to the freedom to legally reuse and technically transform scientific information. 2. To establish a legal and technical framework that increases the impact of investment in neurological research in a public and clearly measurable manner. 3. To develop an open community of neuroscientists, funders of neurological research, technologists, physicians, and patients to extend the Neurocommons work in an open, collaborative, distributed manner. 79 BioRDF: Reagents RDF resources that describes various kinds of experimental reagents, starting with antibodies: •Initial RDF that captures: Gene, the fact that this is an antibody, various kinds of pages about the antibody, such as vendor documentation, and any other properties that are explicitly captured in the source material •Work with the Ontology task force to identify appropriate ontologies and vocabularies to use in the RDF. •Write queries against the RDF to answer questions of the sort posed on the Alzforum's 80 BioRDF: NCBI • NCBI Data: URIs and as RDF • Terminology Integration: NLM’s UMLS, MESH – SNOMED • Olivier Bodensreider 81 BioRDF Neuro Tasks • Aggregate facts and models around Parkinson’s Disease • BIRN / Human Brain Project • SWAN: scientific annotations and evidence • Use RDF and OWL to describe – ’Brain Connectivity' –N euronal data in SenseLab 82 What does RDF get you? • Structure is not format-rigid (i.e. tree) – Semantics not implicit in Syntax – No new parsers need to be defined for new data • Entities can be anywhere on the web (URI) • Define semantics into graph structures (ontologies) – Use rules to test data consistency and extract important relations • Data can be merged into complete graphs • Multiple ontologies supported 89 RDF vs. XML example Wang et al., Nature Biotechnology, Sept 2005 AGML QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. 90 HUPML RDF Stripe Mode Node>Edge>Node >Edge…. 91 RDF Graph 92 gsk:KENPAL rdf:type :Compound ; dc:source http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Ab stract&list_uids=14698171 ; chemID “3820” ; clogP “2.4” ; kA “e-8” ; mw “327.17” ; ic50 { rdf:type :IC50 ; value “23” ; units :nM ; forTarget gsk:GSK3beta } ; chemStructure “C16H11BrN2O” ; rdfs:label “kenpaullone” ; synonym “bromo-paullone” ; smiles “C1C2=C(C3=CC=CC=C3NC1=O)NC4=C2C=C(C=C4)B” ; inChI “1/C16H11BrN2O/c17-9-5-6-14-11(7-9)12-8-15(20)18-13-4-2-1-3-10(13)16(12)1914/h1-7,19H,8H2,(H,18,20)/f/h18H” ; xref http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=3820 . 94 Multiple Ontologies Used Together Disease OMIM UMLS Group FOAF Disease Polymorphisms SNP Drug target ontology UniProt Protein BioPAX Person PubChem Patent ontology Extant ontologies Chemical entity 95 Under development Bridge concept Case Studies 96 Case Study: NeuroCommons.org • • • • Public Data & Knowledge for CNS R&D Forum Available for industry and academia All based on Semantic Web Standards 97 NeuroCommons.org The Neurocommons project, a collaboration between Science Commons and the Teranode Corporation, is creating a free, public Semantic Web for neurological research. The project has three distinct goals: 1. To demonstrate that scientific impact and innovation is directly related to the freedom to legally reuse and technically transform scientific information. 2. To establish a legal and technical framework that increases the impact of investment in neurological research in a public and clearly measurable manner. 3. To develop an open community of neuroscientists, funders of neurological research, technologists, physicians, and patients to extend the Neurocommons work in an open, collaborative, distributed manner. 99 HCLS Neuro Tasks • Aggregate facts and models around Parkinson’s Disease • SWAN: scientific annotations and evidence • Use RDF and OWL to describe – – – – – Brain scans in the The Whole Brain Atlas Neural entries in NCBI’s Entrez Gene Database ’Brain Connectivity' N euronal data in SenseLab Neurological Disease entries in OMIM 102 Conclusions: Key Semantic Web Principles • • • • • • • • Plan for change Free data from the application that created it Lower reliance on overly complex Middleware The value in "as needed" data integration Big wins come from many little ones The power of links - network effect Open-world, open solutions are cost effective Importance of "Partial Understanding" 104 What is the Semantic Web ? It’s Semantic Webs It’s Text Extraction It’s AI It’s Web 2.0 It’s Data Tracking It’s a Global Conspiracy • http://www.w3.org/2006/Talks/0125-hclsig-em/ 106 It’s Ontologies W3C Roadmap • Semantic Web foundation specifications – RDF, RDF Schema and OWL are W3C Recommendations as of Feb 2004 • Standardization work is underway in Query, Best Practices and Rules • Goal of moving from a Web of Document to a Web of Data The Only Open and Web-based Data Integration Model Game in Town 107 The Current Web What the computer sees: “Dumb” links No semantics - <a href> treated just like <bold> Minimal machineprocessable information 108 The Semantic Web Machine-processable semantic information Semantic context published – making the data more informative to both humans and machines 109 Google Graphs Ranking Sites based on Topology Associate Word frequencies with ranked sites 110 The Technologies: RDF • Resource Description Framework • W3C standard for making statements of fact or belief about data or concepts • Descriptive statements are expressed as triples: (Subject, Verb, Object) – We call verb a “predicate” or a “property” Subject <Patient HB2122> Property <shows_sign> 111 Object <Disease Pneumococcal_Meningitis> What RDF Gets You Universal, semantic connectivity supports the construction of elaborate structures. 112 Losing Connectedness in Tables Fast Uptake and ease of use, but loose binding to entities and terms ? 113 Casp2 Casp2 Colon Endodermal Data Integration? • Querying Databases is not sufficient • Data needs to include the Context of Local Scientists • Concepts and Vocabulary need to be associated • More about Sociology than Technology Information Knowledge 114 Standards- Why Not? • Good when there’s a majority of agreement • By vendors, for vendors? • Mainly about Data Packing-- should be more about Semantics (user-defined) • API dominated (Time trapped) • Ease and Expressivity • Too often they’re Brittle and Slow to develop • “They’re great, that’s why there are so many of them” 115 Data Integration Enables Business Integration: Efficiency and Innovation • • • • • • Searching Visualization Analysis Reporting Notification Navigation 116 Searching… #1 way for finding information in companies… 117