1. Knowledge Enabled Information and Services Science Semantic Web: Promising technologies, Current Applications & Future Directions Australia, July-August 2008 Amit P. Sheth amit.sheth@wright.edu Thanks Kno.e.sis team and collaboratorsKno.e.sis

2. Knowledge Enabled Information and Services Science Outline Semantic Web – very brief intro of key capabilities and technlologies Real-world Applications demonstrating benefit of semantic web technologies Exciting on-going research

3. Knowledge Enabled Information and Services Science Evolution of the Web Web of pages - text, manually created links - extensive navigation 2007 1997 Web of databases - dynamically generated pages - web query interfaces Web of services - data = service = data, mashups - ubiquitous computing Web of people - social networks, user-created content - GeneRIF, Connotea Web as an oracle / assistant / partner - “ask to the Web” - using semantics to leverage text + data + services + people

4. Knowledge Enabled Information and Services Science What is Semantic Web? Associating meaning with data: labeling data so it is more meaningful to the system and people. Formal description increases automation. Common interpretation increases interoperability. TBL – focus on data: Data Web (“In a way, the Semantic Web is a bit like having all the databases out there as one big database.”) Others focus on reasoning and intelligent processing

5. Knowledge Enabled Information and Services Science Ontology: Agreement with Common Vocabulary & Domain Knowledge; Schema + Knowledge base Semantic Annotation (metadata Extraction): Manual, Semi-automatic (automatic with human verification), Automatic Reasoning/computation: semantics enabled search, integration, complex queries, analysis (paths, subgraph), pattern finding, mining, hypothesis validation, discovery, visualization Semantic Web Enablers and Techniques

6. Knowledge Enabled Information and Services Science Maturing capabilites and ongoing research Text mining: Entity recognition, Relationship extraction Integrating text, experimetal data, curated and multimedia data Clinical and Scientific Workflows with semantic web services Hypothesis driven retrieval of scientific literature, Undiscovered public knowledge

7. Knowledge Enabled Information and Services Science Open Biomedical Ontologies Open Biomedical Ontologies, http://obo.sourceforge.net/ Many ontologies exist

8. Knowledge Enabled Information and Services Science Drug Ontology Hierarchy (showing is-a relationships) owl:thingprescription _drug_ brand_name brandname_ undeclared brandname_ composite prescription _drug monograph _ix_class cpnum_ group prescription _drug_ property indication_ property formulary_ property non_drug_ reactant interaction_ property propertyformularybrandname_ individual interaction_ with_prescri ption_drug interactionindicationgeneric_ individual prescription _drug_ generic generic_ composite interaction_ with_non_ drug_reactant interaction_ with_mono graph_ix_cl ass

9. Knowledge Enabled Information and Services Science N-Glycosylation metabolic pathway GNT-I attaches GlcNAc at position 2 UDP-N-acetyl-D-glucosamine + alpha-D-Mannosyl-1,3-(R1)-beta-D-mannosyl-R2 UDP + N-Acetyl-$beta-D-glucosaminyl-1,2-alpha-D-mannosyl-1,3-(R1)-beta-D-mannosyl-$R2 GNT-V attaches GlcNAc at position 6 UDP-N-acetyl-D-glucosamine + G00020 UDP + G00021 N-acetyl-glucosaminyl_transferase_V N-glycan_beta_GlcNAc_9 N-glycan_alpha_man_4

10. Knowledge Enabled Information and Services Science WWW, Enterprise Repositories METADATA EXTRACTORS Digital Maps Nexis UPI AP Feeds/ Documents Digital Audios Data Stores Digital Videos Digital Images... Create/extract as much (semantics) metadata automatically as possible; Use ontlogies to improve and enhance extraction Information Extraction for Metadata Creation

11. Knowledge Enabled Information and Services Science Metadata and Ontology: Primary Semantic Web enablers Shallow semantics Deep semantics Expressiveness, Reasoning

12. Knowledge Enabled Information and Services Science Automatic Semantic Metadata Extraction/Annotation

13. Knowledge Enabled Information and Services Science Characteristics of Semantic Web Self Describing Machine & Human Readable Issued by a Trusted Authority Easy to Understand Convertible Can be Secured The Semantic Web: XML, RDF & Ontology Adapted from William Ruh (CISCO)

14. Knowledge Enabled Information and Services Science Application Example 1: Status: In use today Where: Athens Heart Center What: Use of semantic Web technologies for clinical decision support

15. Knowledge Enabled Information and Services Science Operational since January 2006

16. Knowledge Enabled Information and Services Science Goals: Increase efficiency with decision support formulary, billing, reimbursement real time chart completion automated linking with billing Reduce Errors, Improve Patient Satisfaction & Reporting drug interactions, allergy, insurance Improve Profitability Technologies: Ontologies, semantic annotations & rules Service Oriented Architecture Thanks -- Dr. Agrawal, Dr. Wingeth, and others. ISWC2006 paperISWC2006 paper Active Semantic Electronic Medical Records (ASEMR)

17. Knowledge Enabled Information and Services Science Demonstration

18. Knowledge Enabled Information and Services Science Chart Completion before the preliminary deployment ASMER Efficiency Chart Completion after the preliminary deployment

19. Knowledge Enabled Information and Services Science Opportunity: exploiting clinical and biomedical data text Health Information Services Elsevier iConsult Scientific Literature PubMed 300 Documents Published Online each day User-contributed Content (Informal) GeneRifs NCBI Public Datasets Genome, Protein DBs new sequences daily Laboratory Data Lab tests, RTPCR, Mass spec Clinical Data Personal health history Search, browsing, complex query, integration, workflow, analysis, hypothesis validation, decision support. binary

20. Knowledge Enabled Information and Services Science Application Example 2 Status: Completed research Where: NIH/NIDA What: Understanding the genetic basis of nicotine dependence. How: Semantic Web technologies (especially RDF, OWL, and SPARQL) support information integration and make it easy to create semantic mashups (semantically integrated resources).

21. Knowledge Enabled Information and Services Science Entrez Gene Reactome KEGG HumanCyc GeneOntology HomoloGene Genome and pathway information integration pathway protein pmid pathway protein pmid pathway protein pmid GO ID HomoloGene ID

22. Knowledge Enabled Information and Services Science BioPAX ontology Entrez Knowledge Model (EKoM)

23. Knowledge Enabled Information and Services Science Biological Significance Biological Significance: Understand the role of genes in nicotine addiction Treatment of drug addiction based on genetic factors Identify important genes and use for pharmaceutical productions Gene-Pathway Data Integration– Understanding the Genetic-basis of Nicotine Dependence Collaborators Gene-Pathway Data Integration– Understanding the Genetic-basis of Nicotine Dependence Collaborators: NIDA, NLM

24. Knowledge Enabled Information and Services Science Scenario 3 Status: Completed research Where: NIH What: queries across integrated data sources –Enriching data with ontologies for integration, querying, and automation –Ontologies beyond vocabularies: the power of relationships

25. Knowledge Enabled Information and Services Science Use data to test hypothesis gene GO PubMed Gene name OMIM Sequence Interactions Glycosyltransferase Congenital muscular dystrophy Link between glycosyltransferase activity and congenital muscular dystrophy? Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07

26. Knowledge Enabled Information and Services Science In a Web pages world… Congenital muscular dystrophy, type 1D (GeneID: 9215) has_associated_disease has_molecular_function Acetylglucosaminyl- transferase activity Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07

27. Knowledge Enabled Information and Services Science With the semantically enhanced data MIM:608840 Muscular dystrophy, congenital, type 1D GO:0008375 has_associated_phenotype has_molecular_function EG:9215 LARGE acetylglucosaminyl- transferase GO:0016757 glycosyltransferase GO:0008194 isa GO:0008375 acetylglucosaminyl- transferase GO:0016758 From medinfo paper. Adapted from: Olivier Bodenreider, presentation at HCLS Workshop, WWW07 SELECT DISTINCT ?t ?g ?d { ?t is_a GO:0016757. ?g has molecular function ?t. ?g has_associated_phenotype ?b2. ?b2 has_textual_description ?d. FILTER (?d, “muscular distrophy”, “i”). FILTER (?d, “congenital”, “i”) }

28. Knowledge Enabled Information and Services Science Scenario 5 Status: Research prototype and in progress Workflow withSemantic Annotation of Experimental Data already in use Where: UGA What: –Knowledge driven query formulation –Semantic Problem Solving Environment (PSE) for Trypanosoma cruzi (Chagas Disease)

29. Knowledge Enabled Information and Services Science Knowledge driven query formulation Complex queries can also include: - on-the-fly Web services execution to retrieve additional data - inference rules to make implicit knowledge explicit

30. Knowledge Enabled Information and Services Science T.Cruzi PSE Query Interface Figure 4: Semantic annotation of ms scientific data

31. Knowledge Enabled Information and Services Science N-GlycosylationProcessNGP N-Glycosylation Process (NGP) Cell Culture Glycoprotein Fraction Glycopeptides Fraction extract Separation technique I Glycopeptides Fraction n*m n Signal integration Data correlation Peptide Fraction ms datams/ms data ms peaklist ms/ms peaklist Peptide listN-dimensional array Glycopeptide identification and quantification proteolysis Separation technique II PNGase Mass spectrometry Data reduction Peptide identification binning n 1

32. Knowledge Enabled Information and Services Science Storage Standard Format Data Raw Data Filtered Data Search Results Final Output Agent Biological Sample Analysis by MS/MS Raw Data to Standard Format Data Pre- process DB Search (Mascot/ Sequest) Results Post- process (ProValt) OIOIOIOIO Biological Information Semantic Annotation Applications Semantic Web Process to incorporate provenance

33. Knowledge Enabled Information and Services Science 830.9570 194.9604 2 580.2985 0.3592 688.3214 0.2526 779.4759 38.4939 784.3607 21.7736 1543.7476 1.3822 1544.7595 2.9977 1562.8113 37.4790 1660.7776 476.5043 parent ion m/z fragment ion m/z ms/ms peaklist data fragment ion abundance parent ion abundance parent ion charge ProPreO: Ontology-mediated provenance Mass Spectrometry (MS) Data

34. Knowledge Enabled Information and Services Science <parameter instrument=“micromass_QTOF_2_quadropole_time_of_flight_mass_spectrometer” mode=“ms-ms”/> Ontological Concepts ProPreO: Ontology-mediated provenance Semantically Annotated MS Data

35. Knowledge Enabled Information and Services Science Problem – Extracting relationships between MeSH terms from PubMed Biologically active substance Lipid Disease or Syndrome affects causes affects causes complicates Fish Oils Raynaud’s Disease ??????? instance_of UMLS Semantic Network MeSH PubMed 9284 documents 4733 documents 5 documents

36. Knowledge Enabled Information and Services Science Background knowledge used UMLS – A high level schema of the biomedical domain –136 classes and 49 relationships –Synonyms of all relationship – using variant lookup (tools from NLM) –49 relationship + their synonyms = ~350 mostly verbs MeSH –22,000+ topics organized as a forest of 16 trees –Used to query PubMed PubMed –Over 16 million abstract –Abstracts annotated with one or more MeSH terms T147—effect T147—induce T147—etiology T147—cause T147—effecting T147—induced

37. Knowledge Enabled Information and Services Science Method – Parse Sentences in PubMed SS-Tagger (University of Tokyo) SS-Parser (University of Tokyo) (TOP (S (NP (NP (DT An) (JJ excessive) (ADJP (JJ endogenous) (CC or) (JJ exogenous) ) (NN stimulation) ) (PP (IN by) (NP (NN estrogen) ) ) ) (VP (VBZ induces) (NP (NP (JJ adenomatous) (NN hyperplasia) ) (PP (IN of) (NP (DT the) (NN endometrium) ) ) ) ) ) ) Entities (MeSH terms) in sentences occur in modified forms “adenomatous” modifies “hyperplasia” “An excessive endogenous or exogenous stimulation” modifies “estrogen” Entities can also occur as composites of 2 or more other entities “adenomatous hyperplasia” and “endometrium” occur as “adenomatous hyperplasia of the endometrium”

38. Knowledge Enabled Information and Services Science Method – Identify entities and Relationships in Parse Tree TOP NP VP S NP VBZ induces NP PP NP IN of DT the NN endometrium JJ adenomatous NN hyperplasia NP PP IN by NN estrogen DT the JJ excessive ADJP NN stimulation JJ endogenous JJ exogenous CC or MeSHID D004967 MeSHID D006965 MeSHID D004717 UMLS ID T147 Modifiers Modified entities Composite Entities

39. Knowledge Enabled Information and Services Science What can we do with the extracted knowledge? Semantic browser demo

40. Knowledge Enabled Information and Services Science PubMed Complex Query Supporting Document sets retrieved Migraine Stress Patient affects isa Magnesium Calcium Channel Blockers inhibit Keyword query: Migraine[MH] + Magnesium[MH] Evaluating hypotheses

41. Knowledge Enabled Information and Services Science Workflow Adaptation: Why and How Volatile nature of execution environments –May have an impact on multiple activities/ tasks in the workflow HF Pathway –New information about diseases, drugs becomes available –Affects treatment plans, drug-drug interactions Need to incorporate the new knowledge into execution –capture the constraints and relationships between different tasks activities

42. Knowledge Enabled Information and Services Science Workflow Adaptation Why? New knowledge about treatment found during the execution of the pathway New knowledge about drugs, drug drug interactions

43. Knowledge Enabled Information and Services Science Workflow Adaptation: How Decision theoretic approaches –Markov Decision Processes Given the state S of the workflow when an event E occurs –What is the optimal path to a goal state G –Greedy approaches rely on local optimization Need to choose actions based on optimality across the entire horizon, not just the current best action –Model the horizon and use MDP to find the best path to a goal state

44. Knowledge Enabled Information and Services Science Conclusion semantic web technologies can help with: –Fusion of data: semi-structured, structured, experimental, literature, multimedia –Analysis and mining of data, extraction, annotation, capture provenance of data through annotation, workflows with SWS –Querying of data at different levels of granularity, complex queries, knowledge-driven query interface –Perform inference across data sets

45. Knowledge Enabled Information and Services Science Take home points Shift of paradigm: from browsing to querying Machine understanding: –extracting knowledge from text –Inference, software interoperation Semantic-enabled interfaces towards hypothesis validation

46. Knowledge Enabled Information and Services Science References 1.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.Active Semantic Electronic Medical Record, Intl Semantic Web Conference 2.Satya Sahoo, Olivier Bodenreider, Kelly Zeng, and Amit Sheth, An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and Phenotype Information WWW2007 HCLS Workshop, May 2007.An Experiment in Integrating Large Biomedical Knowledge Resources with RDF: Application to Associating Genotype and Phenotype Information WWW2007 HCLS Workshop 3.Satya S. Sahoo, Kelly Zeng, Olivier Bodenreider, and Amit Sheth, From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology, Amsterdam: IOS, August 2007, PMID: 17911917, pp. 1260-4From "Glycosyltransferase to Congenital Muscular Dystrophy: Integrating Knowledge from NCBI Entrez Gene and the Gene Ontology 4.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, submitted, 2007. 5.Cartic Ramakrishnan, Krzysztof J. Kochut, and Amit Sheth, "A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference, 2006, pp. 583- 596A Framework for Schema-Driven Relationship Discovery from Unstructured Text", Intl Semantic Web Conference 6.Satya S. Sahoo, Christopher Thomas, Amit Sheth, William S. York, and Samir Tartir, "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.Knowledge Modeling and Its Application in Life Sciences: A Tale of Two Ontologies Demos at: http://knoesis.wright.edu/library/demos/

47. Knowledge Enabled Information and Services Science More about the Relationship Web Relationship Web takes you away from “which document” could have information I need, to “what’s in the resources” that gives me the insight and knowledge I need for decision making. Amit P. ShethAmit P. Sheth, Cartic Ramakrishnan: Relationship Web: Blazing Semantic Trails between Web Resources. IEEE Internet Computing July 2007 (to appear) [.pdf]Cartic Ramakrishnan[.pdf]

48. Knowledge Enabled Information and Services Science Events: 3 Dimensions – Spatial, Temporal and Thematic Spatial Temporal Thematic

49. Knowledge Enabled Information and Services Science Events and STT dimensions Powerful mechanism to integrate content –Describes the Real-World occurrences –Can have video, images, text, audio all of the same event –Search and Index based on events and STT relations Many relationship types –Spatial: What events happened near this event? What entities/organizations are located nearby? –Temporal: What events happened before/after/during this event? –Thematic: What is happening? Who is involved? Going further –Can we use What? Where? When? Who? to answer Why? / How? –Use integrated STT analysis to explore cause and effect

50. Knowledge Enabled Information and Services Science 50 High-level Sensor Low-level Sensor How do we determine if the three images depict … the same time and same place? the same entity? a serious threat? Example Scenario: Sensor Data Fusion and Analysis

51. Knowledge Enabled Information and Services Science Sensor Data Pyramid Raw Sensor (Phenomenological) Data Feature Metadata Entity Metadata Ontology Metadata Expressiveness Data Information Knowledge Data Pyramid

52. Knowledge Enabled Information and Services Science 52 What is Sensor Web Enablement? http://www.opengeospatial.org/projects/groups/sensorweb

53. Knowledge Enabled Information and Services Science GeographyML (GML) TransducerML (TML) Observations & Measurements (O&M) Information Model for Observations and Sensing Sensor and Processing Description Language Real Time Streaming Protocol Common Model for Geographical Information SensorML (SML) Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007. SWE Components - Languages

54. Knowledge Enabled Information and Services Science Catalog Service SOSSASSPS Clients Sensor Observation Service: Access Sensor Description and Data Sensor Planning Service: Command and Task Sensor Systems Sensor Alert Service Dispatch Sensor Alerts to registered Users Discover Services, Sensors, Providers, Data Accessible from various types of clients from PDAs and Cell Phones to high end Workstations Sam Bacharach, “GML by OGC to AIXM 5 UGM,” OGC, Feb. 27, 2007. SWE Components – Web Services

55. Knowledge Enabled Information and Services Science 55 Semantic Sensor Web

56. Knowledge Enabled Information and Services Science 56 Data Raw Phenomenological Data Semantic Sensor Data-to-Knowledge Architecture Information Entity Metadata Feature Metadata Knowledge Object-Event Relations Spatiotemporal Associations Provenance/Context Feature Extraction and Entity Detection Data Storage (Raw Data, XML, RDF) Semantic Analysis and Query Sensor Data Collection Ontologies Space Ontology Time Ontology Domain Ontology Semantic Annotation

57. Knowledge Enabled Information and Services Science 57 Ontology & Rules Weather Time Space Oracle SensorDB Get Observation Describe Sensor Semantic Sensor Observation Service Collect Sensor Data BuckeyeTraffic.org Get Capabilities Semantic Annotation Service S-SOS Client SWEAnnotated SWE HTTP-GET Request O&M-S or SML-S Response Semantic Sensor Observation Service

58. Knowledge Enabled Information and Services Science Standards Organizations OGC Sensor Web Enablement SensorML O&M TransducerML GeographyML Web Services Web Services Description Language REST National Institute for Standards and Technology Semantic Interoperability Community of Practice Sensor Standards Harmonization W3C Semantic Web Resource Description Framework RDF Schema Web Ontology Language Semantic Web Rule Language SAWSDL* SA-REST SML-S O&M-S TML-S Sensor Ontology * SAWSDL is now a W3C Recommendation Sensor Ontology

59. Knowledge Enabled Information and Services Science Current Research Towards STT Relationship Analysis Modeling Spatial and Temporal data using SW standards (RDF(S)) 1 –Upper-level ontology integrating thematic and spatial dimensions –Use Temporal RDF 3 to encode temporal properties of relationships –Demonstrate expressiveness with various query operators built upon thematic contexts Graph Pattern queries over spatial and temporal RDF data 2 –Extended ORDBMS to store and query spatial and temporal RDF –User-defined functions for graph pattern queries involving spatial variables and spatial and temporal predicates –Implementation of temporal RDFS inferencing –Extended SPARQL for STT queries 1.Matthew Perry, Farshad Hakimpour, Amit Sheth. "Analyzing Theme, Space and Time: An Ontology-based Approach", Fourteenth International Symposium on Advances in Geographic Information Systems (ACM-GIS '06), Arlington, VA, November 10 - 11, 2006 2.Matthew Perry, Amit Sheth, Farshad Hakimpour, Prateek Jain. “Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data", Second International Conference on Geospatial Semantics (GeoS ‘07), Mexico City, MX, November 29 – 30, 2007 3.Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”, ESWC 2005: 93-107

60. Knowledge Enabled Information and Services Science Example Domain Ontology

61. Knowledge Enabled Information and Services Science Temporal RDF: Incorporating Temporal Information Student Undergraduate Graduate rdfs:subClassOf Student1 rdf:type : [2004, 2008] rdf:type : [2002, 2004] rdf:type [?, ?] Temporal Inferencing Interval Union : (Student1, rdf:type, Student) : [2002, 2008] 1. Claudio Gutiérrez, Carlos A. Hurtado, Alejandro A. Vaisman. “Temporal RDF”. ESWC 2005: 93-107 Associate temporal label with a statement that represents the valid time of the statement (Student1, rdf:type, Graduate) : [2004, 2008]

62. Knowledge Enabled Information and Services Science E1:Soldier E2:Soldier E3:Soldier E5:Battle E4:Address E6:Address E7:Battle occurred_at located_at lives_at assigned_to E8:Military_Unit assigned_to participates_in Georeferenced Coordinate Space (Spatial Regions) Dynamic EntitiesSpatial OccurrentsNamed Places Contexts Linking Non-Spatial Entities to Spatial Entities Residency Battle Participation E1:Soldier

63. Knowledge Enabled Information and Services Science Querying in the STT dimensions Define a notion of context based on a graph pattern –Query about entities w.r.t. a given context Associate spatial region with an entity w.r.t. a context Associate temporal interval with an entity w.r.t. a context How are entities related in space and time w.r.t. a given context

64. Knowledge Enabled Information and Services Science An Example: Battlefield Intelligence ?Person ?Symptom Chemical_X ?Military_Event ?Location_1 Enemy_Group_Y ?Location_2 ?Enemy participated_in has_symptom induces located_at spotted_at member_of How close are these locations in space? How are these events related in time? SELECT ?p FROM TABLE(spatial_eval(‘(?p has_symptom ?s)(Chemical_X induces ?s) (?p participated_in ?m)(?m located_at ?l1)’, ‘?l1’, ‘(?e member_of Enemy_Group_y)’); )(?e spotted_at ?l2)’, ‘?l2’, ‘geo_distance(distance=2 unit=mile)’);

65. Knowledge Enabled Information and Services Science SPARQL-ST – Spatio-Temporal SPARQL Politician_123 Committee_456 District_789 Polygon_1 Linear_Ring_1 NAD83 -85.32 34.1, -85.33 34.2, …, -85.32 34.1 on_committee : [1990, 2000] represents : [1984, 1992] located_at : [1990, 2000] uses_crs : [-∞, + ∞] exterior : [-∞, + ∞] lrPosList : [-∞, + ∞] SELECT ?c, %s, #t1 WHERE { on_committee ?c #t1. represents ?d #t2. ?d located_at %s #t3 } Maps to single URI Maps to a set of triples Maps to a time interval

66. Knowledge Enabled Information and Services Science The Machine Factor Formal representation of knowledge –RDF(S), OWL, etc. Statistical analysis –Similarity –Cooccurrence –Clustering Intelligent aggregation of knowledge –Collaboration/Problem Solving Environments –Decision support tools

67. Knowledge Enabled Information and Services Science Putting the man back in Semantics The Semantic Web focuses on artificial agents “Web 2.0 is made of people” (Ross Mayfield) “Web 2.0 is about systems that harness collective intelligence.” (Tim O’Reilly) The relationship web combines the skills of humans and machines

68. Knowledge Enabled Information and Services Science Putting the man back in Semantics “Web 2.0 is made of people” (Ross Mayfield) “Web 2.0 is about systems that harness collective intelligence.” (Tim O’Reilly) The Semantic Web focuses on artificial agents The relationship web combines the skills of humans and machines

69. Knowledge Enabled Information and Services Science Going places … Formal Social, Informal Implicit Powerful