SemTech 2011, Saltlux, Tony Lee


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Geo-Social Semantics and Hybrid Reasoning for Smart Mobile Services

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SemTech 2011, Saltlux, Tony Lee

  1. 1. Geo-Social Semanticsand Hybrid Reasoning forSmart Mobile Services SEMANTIC TECHNOLOGY6 June 2011 / SALTLUX, inc.Tony LEE /
  2. 2. Linked WorldAnd Semantics
  3. 3. Communicating Knowledge 2
  4. 4. Scarecrow : I havent got a brain... only straw. Dorothy : How can you talk if you havent got a brain? Scarecrow : I dont know... But some people without brains do an awful lot of talking... dont they? Dorothy : Yes, I guess youre right Scarecrow : The sum of the square roots of any two sides of an isosceles triangle is equal to the square root of the remaining side. Oh joy! Rapture! I got a brain! How can I ever thank you enough? Wizard of Oz: You cant.Communicating Knowledge 3
  5. 5. Tony’s Brain and Knowledge Neurons ~100B # ~2x # of Web Pages Synapse ~100T # ~2# of Web LinksCommunicating Knowledge 4
  6. 6. 650ft (~200m)650ft 1 : 1000
  7. 7. Knowledge Network
  8. 8. World Wide Web Network
  9. 9. InternetNetwork
  10. 10. PeopleNetwork
  11. 11. WordNetNetwork
  12. 12. Musical work Network
  13. 13. Communication Network
  14. 14. Linked Data Network
  15. 15. Data, Information and Knowledge DATA • symbol, a statement • facts of the world INFORMATION • collection of data, data in context • answer about who, what, where, when KNOWLEDGE • contextual organization of information • map of the world inside our brains By Gene Bellinger, Durval Castro and Anthony Mills • answer about how and whyCommunicating Knowledge 14
  16. 16. Knowledge Representations Human Natural Language Human language written in letters: “The Earth orbits the sun in an ellipse” Visual expression of knowledge in picture, structure diagram, flow chart, Visual Language and blueprint etc Knowledge expressed in keywords, symbols and images related with Tagging objects Symbolic Language Knowledge expressed in mathematical symbols : x2/a2 + y2/b2 = 1 Decision Tree Tree-shaped graph structure for complex decision making Combined expression in condition with various rules of human Rules Language knowledge Knowledge expression system composed of objects and relations in a Database System table format Knowledge expression of logical symbols and arithmetic operations: Logical Language Machine Woman ≡ Person ∩ Female Knowledge expression of values or pointers for other frames saved in Frame Language slots Knowledge expression of semantic relation between concepts in a graph Semantic Network structure Allows knowledge expression, machine learning technology combination Statistical Knowledge based on probability and statisticsCommunicating Knowledge 15
  17. 17. Knowledge Representations Natural Language “Employees working for a company are humans; the company and the employees are legal entities. The company is able to make a reservation for an employee’s trip. The trip is available by plane or train that travels in cities within Korea or the U.S.. The companies and destinations for business trip are located in the cities. Saltlux reserved OZ510 with a round trip of Seoul and New York for Hong, Kildong.” Rule Language (Rule) If someone is flying, he must be on trip. (Rule) If someone’s trip is reserved in a company, he is an employee of the company. (+ Rule) For short trip in the same country, an employee should take a train. (Deduction) Hong kil-dong whose flight is in reservation is an employee of Saltlux. (Deduction) OZ510 is a flight for the U.S. and Korea.Communicating Knowledge 16
  18. 18. Knowledge Representations Legal Entity Legal Entity Name Location Name (*) Legal Entity ID ID (*) Legal Entity kindOf DISJOINT Person Company startFrom Person Company Gender Industry Gender⊆{M,F} Industry Person Company Company Age Person books Trip Address Age > 25 City Addr⊂Seoul endsIn subclssOf subclssOf subclssOf Ontology kindOf instanceOf instanceOf instanceOf instanceOf Employee Employee Korean American Airplane Position Train Pos ≠ Exec. Employee Employee City City instanceOf instanceOf #4831 #4831 instanceOf instanceOf instanceOf Saltlux Saltlux instnaceOf instanceOf Saltlux Saltlux C98765 C98765 #3502 Software #3502 Software Seoul Seoul Kildong Seoul Kildong Kildong participatesIn P12345 P12345 Kildong OZ510 Male Male 37 37 New york Manager Manager (a) Semantic Network Semantic Frame (Slots) (b) (a) + Network (c) (b) + Logical RestrictionsCommunicating Knowledge 17
  19. 19. Five View Points for Semantic Technology • URI/RDF based “Web of Data” • Semantic annotations (RDFa) • Ontology and Logics • Reasoning, Agent system • OWL and RIF • Personalized services • Linked semantic data • Semantic Search and Mining • Data interoperability • Recommendation and DiscoveryCommunicating Knowledge 18
  20. 20. Two Keywords, Today 1. HYBRID 2. MOBILECommunicating Knowledge 19
  21. 21. Hybrid = Complementary Strength + Strength Weakness - WeaknessCommunicating Knowledge 20
  22. 22. Hybrid Reasoning : (1) Mixed Method Logical Reasoning Methods Ontology and Rules • Deductive reasoning Premise 1: All humans are mortal. Premise 2: Socrates is a human. Conclusion: Socrates is mortal. + • Inductive reasoning Premise: The sun has risen in the east every morning up until now. Conclusion: The sun will also rise in the east tomorrow. • Abductive reasoning Machine Learning • Analogical reasoningCommunicating Knowledge 21
  23. 23. Hybrid Reasoning : (2) Mixed Formalism + The relationships among different formalisms Semantic Web Architecture (Benjamin Grosof)Communicating Knowledge 22
  24. 24. Hybrid Reasoning : (2) Mixed FormalismCommunicating Knowledge 23
  25. 25. Mobile Communication Tele - Communication Geo-Location SocialityCommunicating Knowledge 24
  26. 26. Geo-Semantics
  27. 27. GEO Data, GEO Information and GEO Knowledge WHAT IS GEO- GEO-KNOWLEDGE ? GIS/ GIS.phpCommunicating Knowledge 26
  28. 28. An Evolution of Geo Ontology Geo Tagging GPS based POI processing Connecting location coordinate with the relevant information Geo Features Applying classification system by domain Referring to major geographic classification system such as GeoNames Geo Ontology Building/Applying ontology-based spatial information Expressing Point/Line/Shape information Geo Ontology + Rules Utilizing Geo Ontology and rule-based inference Applying deduction rules for intelligent spatial information processingCommunicating Knowledge 27
  29. 29. Geospatial Ontologies • GeoRSS • Geo ontology • Feature ontology • Feature type ontology • Spatial relationship ontology • Toponym ontology • Coordinate reference/spatial index ontology • Geodata set/metadata ontology • Spatial services ontology • W3C Geospatial OntologyCommunicating Knowledge 28
  30. 30. Geo Ontology Examples Core Geographical Concepts: Case Finnish Geo-Ontology by R. Henriksson, 2008 Knowledge 29
  31. 31. Ordnance Survey : OpenData • Open data for Geo- information • TBL was involved in • Supporting SPARQL EndPoint • Supporting RDF/XML, Turtle, JSON • Reference Ontologies - Spatial Relations Ontology - WGS84 Geo Positioning - Gazetteer Ontology - FOAF Knowledge 30
  32. 32. Ordnance RDF Gazetteer Knowledge 31
  33. 33. POI and Geo-data modeling by Saltlux • 28# main category and 600 sub categories for POI classification • Using SKOS for semantic classification • Including named places and events • All data set has its name space, • Coordination - - SKOS based Taxonomy GEO Data Model Knowledge 32
  34. 34. Taxonomy and Property modeling for POI NO Main Classes Sub Classes name good for 1 Arts & Entertainment Arcades Arts & Entertainment Adult Entertainment & Nightlife Adult Arcades, Casinos Archery Sports & Recreation alternate name products and services Art Galleries Adult Massage Badminton 2 Adult Entertainment & Nightlife Botanical Gardens, Arboretum Adult Novelties & Product Shop Baseball description specialities Cinema Bars & Pubs Basketball Music Concert Hall Business Clubs Billiards 3 Sports & Recreation Open air theatres, Festival Places Audult Comedy Clubs Boating street-adress brands Theatres Dance Clubs Bowling 4 Media & Broadcasting Museums Astrologers & Psychics Jazz & Blues Clubs Audalt Karaoke Boxing Cricket postal-code smoking Social & Interests Clubs Hourse Racing Curling 5 Religious Organizations Talent Agencies & Entertainers Boat Racing Cycling categories take-out Party Rentals Bycycle Racing Dance Ticket Office Car Racing Equestrian 6 Transportation Aquariums Billiard Halls Fencing url transit 7 Automotive 8 Education & Learning Video/DVD, Game Rental Comedy Clubs Circus Amusement Parks Zoos Other Adult Entertainments Fishing Fitness Clubs American Football Golf Gun/Rifle Ranges … email wireless Cartoon Rental Gymnastics tel reservations 9 Event Planning & Services Internet Café Karaoke Hockey Horse Racing & Equestrian lottery shop Hunting latitude best nights 10 Manufacturing & Industry General Exhibition, show Lacrosse Folk Village Taekwondo 11 Financial & Legal Services Other Arts Judo longtitude alcohol Other Entertainment Kendo Martial Arts 12 Health and Medical Motorsports & Racing device_lat reviews Paintball Parachuting 13 Beauty and Spas Racquetball device_long photo Rafting/Kayaking 14 Other Professional Services Ringette Rugby hours Year Established Running 15 Travel & Tours Scuba fax seating Skateboarding, inline skating 16 Home & Local Services Ice Skating Skiing Skydiving payment options outdoor seating 17 Shopping Soccer Softball parking chef 18 Government & Public Services Squash Surfing Swimming ambiance self service 19 Food & Drink Tennis Track & Field Volleyball amentities languages spoken 20 Restaurants Wrestling Aerobic 21 Other Artifacts Yoga attire music Bungee Jump 22 Other Natural Objects Camping Arenas & Stadiums price range associations Playground 23 Utility & Infrastructure Other Sports delivery part_of Taxonomy Modeling Property ModelingCommunicating Knowledge 33
  35. 35. Semantic queries for geo-data Find good restaurants for dating that serves steaks and free parking near by GAGA gallery in Insa-dong. PREFIX ns: <> PREFIX rdf: <> PREFIX dc: <> PREFIX rdfs: <> PREFIX xsd: <> PREFIX wgs: <> PREFIX f: <> SELECT * WHERE { ?res rdf:type ns:NamedPlace; ns:name ?name; wgs:lat ?lat1; wgs:long ?long1; ns:address ?addr1. OPTIONAL{?res ns:street-address ?straddr1.} ?rel rdf:type ns:NamedPlace; ns:name ?relname; wgs:lat ?lat2; wgs:long ?long2; ns:address ?addr. OPTIONAL{?rel ns:street-address ?straddr.} ?rel ns:category ?cate; ns:ambiance ns:ambiance_129; ns:parking ns:parking_9. ?cate rdfs:label ?catename. FILTER (f:distance(?lat1, ?long1, ?lat2, ?long2) <= 300 && ?cate = <> && ?name = 가가갤러리) } ORDER BY ?relnameCommunicating Knowledge 34
  36. 36. DemonstrationCommunicating Knowledge 35
  37. 37. MOBILE APIs • Supporting Mobile APIs by using SPARQL Endpoint • Working on Android (and iPhone) function description findPOIbyAll(java.lang.String name, java.lang.String id, int dist, 4 argument(name, id, distance, format) java.lang.String format) format: xml, json, id: Category ID findPOIbyAllCoordinate(java.lang.String id, int dist, double lat, 5 argument(id, distance, lat, long, format) double lon, java.lang.String format) id: Category ID, format: xml, json findPOIbyCoordinate(java.lang.String id, int dist, double lat, 4 argument(id, distance, lat, long) double lon) id: Category ID, format: xml findPOIbyDist(java.lang.String name, java.lang.String id, int dist) 3 argument(name, id, distance) name: name of PIO, id: Category ID findPOIbyName(int dist, java.lang.String name) 3 argument(distance, name) format: xml findPOIbyFormat(int dist, java.lang.String name, 3 argument(distance, name, format) java.lang.String format) format: xml, json findPOIbyID(java.lang.String id) 1 argument(id) id: 특정 상점이 갖는 URI, return: meta data of POI, xml geoQuery(java.lang.String query, java.lang.String format) 2 argument(query, format) format: xml, json query for getting MAP data query(java.lang.String query, java.lang.String format) 2 argument(query, format) format: xml, json SPARQL queryCommunicating Knowledge 36
  38. 38. MOBILE APIs protected void onStart() { super.onStart(); // Service binding Intent i = new Intent(this, SparqlEndpoint.class); boolean ret = bindService(i, mConnection, Context.BIND_AUTO_CREATE); private ServiceConnection mConnection = new ServiceConnection() { // Service binding call public void onServiceConnected(ComponentName name, IBinder service) { // converting from service into ISparqlEndpoint interface mService = ISparqlEndpoint.Stub.asInterface(service); mService.query( … ); } // Closing service public void onServiceDisconnected(ComponentName name) { mService = null; } }; … }Communicating Knowledge 37
  39. 39. Use-case : Urban Computing in LarKC project Wikipedia [DB] [Triple] [DB] DBPedia LinkedGeoData OpenStreetMap (LGD) Mapping with Triplify Making XML data to Linked Data Owl:SameAs, Jaro distance metric Name comparing Schema : 24Class Element : 320mega Nodes, 25mega WaysCommunicating Knowledge 38
  40. 40. Use-case : Urban Computing in LarKC project Inconsistency check • Some POIs suddenly disappeared at a point and reappeared at another point which make arrow directions are inconsistent. Unsuitability check • some of road signs are not properly designed and installed as specified by the regulation. • improperly located road signs such as those not installed within the specified range from junction Inconsistent road sign Unsuitable road sign points are not suitable one. Continuous planning • Some of POI are destroys and moves or appears from time to time. One of major POI movement(for example, city hall) may bring many road sign modifications. Go straight? Or turn right? New road sign covers another road signCommunicating Knowledge 39
  41. 41. Use-case : Urban Computing in LarKC project [Node Element] [Road Sign & POI Insertion] R K E R Node RS KPOI WPOI [Link Element] K E startNode endNode sameAs [Junction & POI Finding] [Way Element] R2 R1 nextLink sameAs J1 J2 K link N E link M [Road Element] K way N K E way M searching rangeCommunicating Knowledge 40
  42. 42. Use-case : Urban Computing in LarKC project Data Set Comments Linked Geo Data 1 billion triples in WGS84 coordinate (LGD) Loading LGD full and extracted part of Korea Open Street Map Extracting all way information in WGS84 coordinate (OSM) Selecting and importing 2 million triples for Seoul Point Of Interest 1 million POIs related with road signs Data in Korea Around 4 million triples. (KPOI) Diverse data of Seoul road sign in database Seoul road sign 9515 instance of direction road signs in Seoul data (RSD) Converting TM coordinate into WGS84 coordinate Converting RDB into RDF (0.5 million triples) Korean road sign Around 30 Regulations of road signs regulations (RSR) Changing to SparQL for validation check Mediate Ontology Ontology linking between OSM, KPOI, RSD or other data (MO) Expressivity : subClassOf, subPropertyOf, sameAs, inverseOf Total : about 1.1 billion triplesCommunicating Knowledge 41
  43. 43. Use-case : Urban Computing in LarKC project LGD OSM KPOI RSD Linking, Mapping, Converting, Adjusting Mediate Geo-OntologyCommunicating Knowledge 42
  44. 44. Use-case : Urban Computing in LarKC projectCommunicating Knowledge 43
  45. 45. Use-case : Urban Computing in LarKC project Finding the target POI around 500m from a node of road PREFIX rsm: <> PREFIX owl: <> PREFIX xsd: <> PREFIX sgf: <> PREFIX rdfs: <> PREFIX geo: <> PREFIX rdf: <> select distinct ?targetPOIID where { rsm:osmn_436718764 geo:lat ?endNodeLat . rsm:osmn_436718764 geo:long ?endNodeLong . rsm:kpoi_12720 geo:lat ?targetNodeLat . rsm:kpoi_12720 geo:long ?targetNodeLong . rsm:kpoi_12720 rsm:id ?targetPOIID . filter ( sgf:distance(?endNodeLat, ?endNodeLong, ?targetNodeLat, ?targetNodeLong) <= 500 ) }Communicating Knowledge 44
  46. 46. Use-case : Urban Computing in LarKC project Data: Traffic Flow and Speed Prediction: Data from Milano Milano City Sensor Map Traffic data from Milano (Italy) Data ranging from Mar. 07 to July 09 (849 days) 5 min. sampling rate for flow & speed Traffic flow & speed from 209 sensors that are able to classify vehicles, and 757 non classifying sensors Weather data provided from 1 hour sampling rate for weather data Sensors – Crossroads – Street Categories (multi-colored)Communicating Knowledge 45
  47. 47. Use-case : Urban Computing in LarKC project Problem Description: Traffic Flow and Speed Prediction Traffic Flow [12:00; 12:05] (preprocessed)Traffic Flow (# vehicles) Mar. 07 July 09 Traffic Speed [12:00; 12:05] Predict the traffic flow and (preprocessed) speed for the next 24h basedTraffic Speed (average) on a 5 min. time grid Traffic flow and speed forecasts are made on the sensor level for the whole traffic network Forecasts: inputs for optimal Mar. 07 July 09 routing algorithms Communicating Knowledge 46
  48. 48. Context Awareness and Geo-Semantics Source :, David CrowCommunicating Knowledge 47
  49. 49. Context Awareness and Geo-Semantics • physical contexts : location, time • environmental contexts : weather, light, sound levels • informational contexts : stock quotes, sports scores • personal contexts : health, mood, schedule, activity • social contexts : group activity, social relationship • application contexts : e-mail, websites visited • system contexts : network traffic, status of printersCommunicating Knowledge 48
  50. 50. Ontology for Context Awareness by Saltlux Time subClassOf subClassOf subClassOf EventRelative PersonRelative TimeInterval TimeRelation TimeWordMean subClassOf subClassOf Profile Health date before timeWord Event EventTerms privacyLevel privacyLevel disease time at before terms timeInterval name age bloodType after at actionTask person LocationSensor dateInterval birth heartBe device timeInterval during after job Person at height Person deivceName weight from serviceInterval personID Device operation Contact bodyTemperature to locationInfo order privacyLevel hasProfile etcHealthInfo 1 Session Task SubTask space cellularPhoneNum contact etcHealthInfo 2 email person taskName subClassOf subClassOf subClassOf health hasNick Nick hasTask hasSubTask SearchSubTask DeviceSubTask OperationSubTask Interest privacyLevel belongTo nickName timeInterval searchWord device targetProperty field preference calledBy Space space timeInterval deviceName propertyValue Space detailedField interest Group taskPriority delayTime deviceAccessLevel hasOperation subClassOf subClassOf groupName privacyLevel timeInstance 이미지를 표시할 수 없습니다 . 컴퓨터 메모리가 부족하여 이미지를 열 수 없거나 이미지가 손상되었습 니다 . 컴퓨터를 다시 시작한 후 파일을 다시 여십시오 . 여전히 빨간색 x가 나타나면 이미지를 삭제한 다 hasMember HomeSpace 음 다시 삽입해야 합니다 . OfficeSpace userPriority Policy subClassOf subClassOf delayTime hasSchedule spaceName registerService Level Priority Person owner subClassOf subClassOf subClassOf subClassOf LocationSensor locationSensor DeviceAccessLevel PrivacyLevel UserPriority TaskPriority etcSensor etcSensor level level level level EntityRelative Device device Actuator UserCommandRelative Sensor OperationLevel Display subClassOf subClassOf subClassOf Device operationLevel mode deviceID UserCommand LocationSensor EtcSensor Temperature brightness subClassOf IPaddress controlColor personID Schedule sensorID sensorID degree deviceName contras macID Person who coordinate coordinate humidity TaskType actuator menuDisplayTime IPAdd title sensingDataType sensingDataType Light subClassOf subClassOf subClassOf deviceDefault Sound requestTime where sensingTime sensingTime bright color volume O hasServiceTime DeviceTask RequestInformation BatchTask withWhom status status owner soundType from hasSensor Communication OhasTaskType deviceID queryLiteral taskData modeEQ to alternativeDevice deviceName nodeType taskName Power briefingContents deviceAccessLevel Player O hasOperation catagory Exclusive powerStatus TimeRelation coordinate track moviePlace device Broadcast from Operation playerStatus remainBattery station to targetProperty isWork OpenClose channel at propertyValue ocStatusCommunicating Knowledge 49
  51. 51. Concept of Context Driven Mobile Services CONTEXT MANAGER CONTEXT SENSOR / NETWORK QoC Inferred Context Model Context Rules Context CONTEXT OWNER Filter Dynamic Collector Context User Device CONTEXT-AWARE SERVICE Service Service Service Discovery Personalization Adaptation Smart Mobile ServiceCommunicating Knowledge 50
  52. 52. Context Aware Platform by SaltluxCommunicating Knowledge 51
  53. 53. Use-case : Life Logging Life Logging from Smart PhoneCommunicating Knowledge 52
  54. 54. Use-case : Life Logging Life Logging as RDF data Context Aware (Inductions: ML)Communicating Knowledge 53
  55. 55. Use-case : Analysis of Life Pattern Sat Sun Sat Sun Sat Sun Sat Sun Sat Sun Sick at HomeCommunicating Knowledge 54
  56. 56. Use-case : Trajectory Awareness Hybrid Reasoning : DL Reasoning + Induction(Machine Learning) Bus_A Workplace Bus_B Bus_C Home Bus_E Bus_DCommunicating Knowledge 55
  57. 57. Use-case : Trajectory Awareness Hybrid Reasoning : DL Reasoning + Induction(Machine Learning) Home Detection SPARQL Query and Selection Clustering Calculation DL Reasoning Workplace Detection GPS Log Selection Clustering Calculation Data Bus Stop Detection Segmentation Cleansing MatchingCommunicating Knowledge 56
  58. 58. Use-case : Trajectory Awareness Hybrid Reasoning : DL Reasoning + Induction(Machine Learning) Main Line Extracting Commute Trajectory LearningSampledMobileLog Line Line Line Trajectory Trajectory Trajectory Data Generating Thickening Thinning Graph Tracing Clustering E1 E2 E6 E7 Thu Nov 18 09:52:35 13 min Fri Nov 19 09:22:32 10 min E6 E6 Sat Nov 20 09:44;30 13min E7 V4 V4 E7 Thu Nov 23 08:18;39 6min V5 V5 E4 E2 E2 E4 V6 V6 Wed Nov 24 12:35:52 15min Thu Nov 25 09:31:23 14 min E5 E5 V3 V3 Fri Nov 26 09:38:21 10 min V2 V2 V1 E3 E3 V1 E1 E3 E5 E7 Wed Nov 17 08:06:19 9min E1 E1 Mon Nov 22 08:06:34 10min Sat Nov 27 09:28:27 14min Communicating Knowledge 57
  59. 59. Use-case : ITS of u-City 1. No Accident and Disaster 2. Finding Accident and Disaster : Recommending Detour Path 3. Finding Accident and Disaster but it could be recovered soonCommunicating Knowledge 58
  60. 60. Use-case : Ontology model for ITSCommunicating Knowledge 59
  61. 61. Use-Case : Water and Gas Pipeline Management Context-Aware Disaster Management POC by Saltlux System Overview Context Aware System • 4 System blocks Service Manager Situation Reasoning • OS : Windows Server Service Query Engine Triple Store • Platform : J2EE based POJO Interface Service (Pure Object Java Object) Reasoner Rule Store Handler • Supporting Web service Context Acquisition Context Manager Context Filter Instance Population Context Collector Instance Manager Disaster Control Center Web Center Context Service Sensor GIS Data Layer Logs Dash Data Data Aware Board SystemCommunicating Knowledge 60
  62. 62. Use-Case : Water and Gas Pipeline Management Context-Aware Disaster Management POC by Saltlux Sensor Monitoring Leakage Detection Discover Leakage Area Infer Leakage Pipe Link Automatic Alert Recom. Detour PathCommunicating Knowledge 61
  63. 63. Social Semantics
  64. 64. Characteristics of Social Network and Networking 1. Structural features • Small world : six degree of separation • Unfair world : governed by power law • Strength of weak relationship 2. Synchronization and Amplification 3. Empowerment in NetworkCommunicating Knowledge 63
  65. 65. Structure : Small World • Six degrees of separation - Experiment : Six degrees of Kevin Bacon • Does internet and SNS make it shorter? - Yes or No ? - Average degrees in FB : 5.73 - Maximum degree in FB : 12 - Average degrees in TW : 4.67 - Average degrees in WP : 4.5Communicating Knowledge 64
  66. 66. Structure : Unfair World • Scale-Free Network - Governed by power law - Like Pareto and long tail principle • Evolving to HUB network - Portal(google), SNS(FB, TW) - Airport, logistics(Fedex) • Unfair world, reality - men-women network - The rich get richer and the poor get poorerCommunicating Knowledge 65
  67. 67. Structure : Unfair World 9 6Communicating Knowledge 66
  68. 68. Strength of Weak Relationship • Strong and Closed Network - Mafia organization, trust network - no secret, no new information • Weak and Open Network VS. - a broker among heterogeneous nets - controller of network and info. flow • Strength of Weakness - multiple and cross discipline become more important - getting new job and ideas, building new business and innovationCommunicating Knowledge 67
  69. 69. Synchronization and AmplificationCommunicating Knowledge 68
  70. 70. Synchronization and Amplification : infection Infection of antismoking , fatness and etc like diseaseCommunicating Knowledge 69
  71. 71. Empowerment and democracy in Social network Power Law VS. Power DispersionCommunicating Knowledge 70
  72. 72. Semantic Social Network Analysis • Social Networks : networks based on the relation between people • Semantic Social Network : RDF representations of social network and data Abstraction stack for semantic SNA Foaf:knows Foaf:interest [Paolillo and Wright 2006] Rich graph representations reduced to simple [Semantic Social Network Analysis, untyped graphs in order to apply SNA] 71Communicating Knowledge 71
  73. 73. Semantic Social Network Analysis Semantic Network Social Network Text Mining SNA (Induction) (Deduction) Semantic Social Network AnalysisCommunicating Knowledge 72
  74. 74. Semantic Social Network Analysis People Task Org. Semantic Network Place Service Event Dijkstra’s algorithm Family, Colleagues Information Connecting vk : weighting of relation n :Community , number of relations gjk : # ofHub, Broker shortest path between j and k Experts g : total number of entity gjk(i) : # of path having i between j and k O( | E | + | V | log | V | ) 73Communicating Knowledge 73
  75. 75. SAMZZIE : Social Semantic Platform by Saltlux Web User Interface Knowledge Network Services User User & Admin Knowledge Social Knowledge Knowledge Contents K-Dic DAC Type Topic Rank Knowledge Discovery Network Trend Circulation Search Manager Schedule Pattern Env. Query Integrated API Knowledge Network Analysis Knowledge Discovery Control KNA KDC Knowledge User Type Social Network Knowledge DA DAR KNA Circulation Pattern Analysis Trend Analysis Scheduler Scheduler Scheduler Analysis Analysis Data Aggregation Data Analysis and Reasoning DA DAR TMS TRE Topic Document NE & Data Knowledge Query Feature Email Aggr. Web Aggr. Ranker Aggr. Annotation Abstraction Population Engine Extraction Engine Meta Base Knowledge Base Email Topic Topic Email U&A DA K-Dic KDQ Authority Schedule SN KT KC UTP Contents Trend Rank Abst. Env. PolicyCommunicating Knowledge 74
  76. 76. Use-case : Social Media AnalyticsCommunicating Knowledge 75
  77. 77. Use-case : VOC Sensing & Analysis (for KT) Hybrid Reasoning : Induction(Machine Learning) + Deduction(Horn Logic) Communicating Knowledge 76
  78. 78. Use-case : Intelligent Telco Platform Intra Portal Solution Intelligent Application Service (AS) Personalized Intelligent Shopping Social FIMM MagicN Local News Etc., Search Traveling Guide Recommendation Network Internet Portal, Legacy System CDE Enabler BcN, All-IP N/W MagicN VME ISMS ICDS empas IPAS ICE AAA SICS O&M DAUM PS SAS NAVER JUICE Simulation Server Internet HSS TV S(L)MSC AuC P/I/S-CSCF BGCF BcN S(L)MSC IMS-ALG All-IP IMS Infra TrGW Network PCRF • ISMS: Intelligent Subscriber information Mgnt. Server Node-B • ICDS: Intelligent Content Delivery Server RNC SGSN GGSN (WCDMA) • SICS: Subscriber Information Collection Server W-CDMA • O&M: Operation & Management ServerCommunicating Knowledge 77
  79. 79. Use-case : Mobile Social Network Analysis Major Activity Area Major Residential Area attend Profile attend • Name: Jerry Profile Obama • Age: 12 • Name: Elizabeth attend lives in • Sex: Woman Cox lives in • Age: 12 Pay for • Sex: Woman attend attend Call Profile • Name: Jane Bush • Age: 12 Call • Sex: Woman Call Call Profile Profile Call • Name: Edward • Name: Nancy Adams Obama Call • Age: 11 • Age: 42 • Sex: Woman • Sex: Woman lives in Profile Profile • Name: Jessica • Name: Tom Obama Bailey Family • Age: 16 Friends • Age: 13 • Sex: Man • Sex: WomanCommunicating Knowledge 78
  80. 80. Use-case : Personal Profile Analysis User Profiling from Network Bimodal Normal μ1 = 38 Φ= σ1 = 4.2 w = 0.83 각 나이대별 (SMS/VOICE), (성별), μ2 = 13 σ2 = 2.4 (나이대) 120 클래스 패턴을 이용한 PI 결과Communicating Knowledge 79
  81. 81. Use-case : Personal Profile Analysis - SemanticsOntology Population Legacy Data Ontology Mapping from Legacy Data 80Communicating Knowledge 80
  82. 82. Use-case : Hybrid Reasoning DL + Rules 50 Classes , 58 Relationships , 15 Properties , 57 RulesCommunicating Knowledge 81
  83. 83. Use-case : Discovering Social Relationship 82Communicating Knowledge 82
  84. 84. Communicating Knowledge 83
  85. 85. Geo-Social Semantics
  86. 86. O2 Platform : Geo-Social Data Cloud by SaltluxCommunicating Knowledge 85
  87. 87. O2 Platform : Geo-Social Data Cloud by Saltlux GEO Context Provider Social Context Provider SPARQL/XML, JSON REST/XML, JSON Semantic Geo-Social Platform HTTP Engine Query Engine Geo Context Geo Context/Social Context Social Content Engine manager Acquisition (Admin Console) Social Query Wrapper Result Context Executor Manager Formatter Searcher Data Formatter (RDF/XML) Wrapper Social Social Query Result Geo Context …. Context Context Formatter Manager Manager Indexer Weather Event Geo Context Store Social Context (Place, Event, etc) Index Traffic …. Blog NewsCommunicating Knowledge 86
  88. 88. Opinion Mining and Sentiment Analysis from TwitterCommunicating Knowledge 87
  89. 89. O2 Platform : Ontology model for twitter twd:following twd:follower sioc:UserAccount twd:TwitterUser sioc:id(xsd:string) twd:screenName(xsd:string) twd:post twd:retweetsioc:creator_of sioc:has_creator twd:discuss twd:reply twd:Tweet sioc:Post twd:messageID(xsd:string) sioc:content(xsd:string) twd:messageTimeStamp(xsd:string) twd:talksAboutPositively twd:talksAbout twd:talksAboutNeutrally twd:talksAboutNegatively geo:SpatialThing geo:NamedPlaceCommunicating Knowledge 88
  90. 90. Live DemoCommunicating Knowledge 89
  91. 91. Hybrid Reasoning and Queries tweets about a given kind of POI of people similar to me that tweeted nearby in the last x minutes; PREFIX f: <java:ext.> SELECT ?poi1 ?poi2 ?user (f:similarWithProbability(data:Alice, ?user) AS ?p) WHERE { ?user bot:posts ?t1 . ?t1 bot:talksAboutPositively ?poi1 . ?poi1 a bot:NamedPlace ; geo:lat ?lat1 ; geo:long ?long1 ; skos:subject ?category . data:Alice geo:lat ?givenLat ; geo:long ?givenLong ; bot:posts ?t2 . ?t2 bot:talksAboutPositively ?poi2 . ?poi2 a bot:NamedPlace ; geo:lat ?lat2 ; geo:long ?long2 ; skos:subject ?category . FILTER(?t1!=?t2) FILTER(f:similarWithProbability(data:Alice, ?user)>0.5) FILTER((?lat1-?givenLat)<"0.1"^^xsd:float && (?lat1-?givenLat)>"-0.1"^^xsd:float && (?long1-?givenLong)<"0.1"^^xsd:float && (?long1-?givenLong)>"-0.1"^^xsd:float ) } ORDER BY DESC(?p) LIMIT 10Communicating Knowledge 90
  92. 92. BOTTARI mobile App by Saltlux AR based Location Search Reputation Analysis Intro screen Social Recommendation Expert Search and and Dynamic Social search Real-time Q&ACommunicating Knowledge 91
  93. 93. DEMO MovieCommunicating Knowledge 92
  94. 94. Future Use-case : Disaster Management Source : BBC, ESRICommunicating Knowledge 93
  95. 95. Future Use-case : Disaster ManagementCommunicating Knowledge 94
  96. 96. Future Use-case : Disaster Management Mobile phone Sensors Social Media & Networks Satellite/CCD images Sensor Networks Stream Data Hybrid Stream Reasoning Massaging Service Collection Decision Supporting Geo-Spatial Data Collection Citizen Decision Dashboard Disaster Disaster Disaster Supporting And Knowledge Process Policy System Controller Government , Disaster Center GIS and Geo-Data Intelligent Disaster Management SystemCommunicating Knowledge 95
  97. 97. Technical Tips And the CompanyCommunicating Knowledge 96
  98. 98. 4 Dimensions of Semantic World Scalability Performace Data Dynamics ExpressivityCommunicating Knowledge 97
  99. 99. Current State of the Art of Technology Scalability UbiComp Year Performance Telco • 500M triples Social Net 2005 Enterprise • OWL DLP Search Medical • 30B triples 2010 • OWL DL Horst ExpressivityCommunicating Knowledge 98
  100. 100. Current State of the Art of TechnologyCommunicating Knowledge 99
  101. 101. Current State of the Art of Technology Scalability UbiComp Year Performance Telco • 500M triples Social Net 2005 Enterprise • 1~40S (LUBM1000) Search • 30B triples Medical 2010 • 0.01~5S (LUBM1000) PerformanceCommunicating Knowledge 100