Semantic Web powering Enterprise and Web Applications
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Semantic Web powering Enterprise and Web Applications

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Keynote at Industry Event: Technology Landscape 2013, Dayton, OH, USA. May 26, 2010.

Keynote at Industry Event: Technology Landscape 2013, Dayton, OH, USA. May 26, 2010.

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  • Let me start by offering my appreciation for our Chancellor Dr. Fingerhut’s visionary leadership in establishing the Ohio Center of Excellence program that identifies Centers and program that generate world-class research and help draw talent and investment to the state. I would be remiss if I did not call out tremendous leadership that our President Dr. Hopkins and his entire leadership team has shown in regards to identifying and promoting these centers– my tanks to Dr. Angle, Dr. Bantle. Dr. Jang, and Dr. Sudkamp– thanks for your early and steadfast support for Kno.e.sis.
  • Let me give a technological introduction to what our center is about: we all face a fire hose of data-- Pubmed adds 2000 to 4000 citations per day, it is usual to add about 5 gig from a single run of a scientific experiment -- and just imagine how much data created by all the cameras and 40 billion mobile sensors in the world! But even with all the search and browsing tools we have, we face huge information glut. How do we make sense from the data? Just as humans apply their knowledge and experience to understand what they see– we apply domain model or knowledge to attach meaningful labels to these data. Then we can apply computational techniques to visualize, provide situational awareness, discovery nuggets of knowledge of information and insight. For example, from all that biomedical data, what a scientist may be looking for is– how can we treat Migraine? What has Magnesium to do with Migraine? Why does Magnesium deficiency cause Migraine? What is the process by which Magnesium affects Migraine?
  • So what is Kno.e.sis about– it is about stepping away from the concerns of storing and searching data, to that of improving human experience, human effectiveness, human performance, human productivity.
  • Our 15 faculty from 4 colleges are already engaged in multiple jointly funded grants, pending proposals, serving on interdisciplinary programs like Biomedical Sciences PhD program and on committees of students of colleagues.
  • This work has been developed in collaboration with my peers at Kno.e.sis, Pablo N. Mendes and Dr. Cartic Ramakrishnan who is at ISI-University Southern California. Our Advisor is IEEE Fellow, Professor Amit Sheth.
  • This work has been developed in collaboration with my peers at Kno.e.sis, Pablo N. Mendes and Dr. Cartic Ramakrishnan who is at ISI-University Southern California. Our Advisor is IEEE Fellow, Professor Amit Sheth.
  • This work has been developed in collaboration with my peers at Kno.e.sis, Pablo N. Mendes and Dr. Cartic Ramakrishnan who is at ISI-University Southern California. Our Advisor is IEEE Fellow, Professor Amit Sheth.
  • This work has been developed in collaboration with my peers at Kno.e.sis, Pablo N. Mendes and Dr. Cartic Ramakrishnan who is at ISI-University Southern California. Our Advisor is IEEE Fellow, Professor Amit Sheth.
  • This work has been developed in collaboration with my peers at Kno.e.sis, Pablo N. Mendes and Dr. Cartic Ramakrishnan who is at ISI-University Southern California. Our Advisor is IEEE Fellow, Professor Amit Sheth.
  • The last representative work we’d like to share with you is our work on making sense of social data, like those from Twitter and facebookaround news worthy events that are of interest to a populace.The goal is to offer an understanding of what people are talking about and paying attention to
  • What the social perceptions behind the data might be, the multiple narratives
  • Twitris is our effort in this direction to help users keep up with observations made around news-worthy events.. Before I hand over the microphone to Dr. Mike Raymer, I’d like to leave you with a short demo of the deployed web application.
  • Let me start by offering my appreciation for our Chancellor Dr. Fingerhut’s visionary leadership in establishing the Ohio Center of Excellence program that identifies Centers and program that generate world-class research and help draw talent and investment to the state. I would be remiss if I did not call out tremendous leadership that our President Dr. Hopkins and his entire leadership team has shown in regards to identifying and promoting these centers– my tanks to Dr. Angle, Dr. Bantle. Dr. Jang, and Dr. Sudkamp– thanks for your early and steadfast support for Kno.e.sis.

Semantic Web powering Enterprise and Web Applications Semantic Web powering Enterprise and Web Applications Presentation Transcript

  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Web powering Intelligent Enterprise and Web Applications Amit P. Sheth LexisNexis Ohio Eminent Scholar Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OH Technology Landscape 2013, Dayton OH. May 26, 2010
  • Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis)
  • 2D-3D & Immersive Visualization, Human Impacting affects Computer Interfaces bottom line Migraine Domain Magnesium Models/ Stress inhibit isa Knowledge Patient Calcium Channel Blockers Knowledge discovery Biomedical SEMANTICS, MEANING PROCESSING Knowledge Discovery, Patterns / Inference / Reasoning Meta data / Knowledge Semantic Management & Annotations Visualization Search and Metadata Extraction/Semantic Annotations browsing Massive amounts of data Structured text (Scientific Experimental Public domain publications / Clinical Trial Data knowledge white papers) Results (PubMed) 3
  • Kno.e.sis Vision Kno.e.sis’ leadership in semantic processing will contribute to basic theory about computation and cognitive systems, and address pressing practical problems associated with productive thinking in the face of an explosion of data. Kno.e.sis intends to lead a march from information age to meaning age. 4
  • Globally Competitive Careers and Economic Development WPAFB Directorates Dayton Region Companies Tech^Edge Human Sensor Woolpert REI Tech, Aptima Effectiveness SAIC LexisNexis Knowledge Workers, Products, Services and Applications Defense/Aerospace Advanced Data Human Sciences R&D Management & Health Care Application to Regional Industry Cluster Kno.e.sis+Faculty Strengths daytaOhio – a WCI • Cognitive Science & Human Factors • Data Analysis/Mining/Visualization • Visualization and Data Mgt • Info. & Knowledge Mgmt Infrastructure • Web 3.0 (Semantics, Services, Sensors) • Consulting and Technology • Virtual Worlds, Social Computing Transfer • High Performance/Cloud Computing • Bioinformatics/Biomedicine, Healthcare Academic Research and Infrastructure 5
  • 6
  • Significant Infrastructure VERITAS Whole-Body Laser Range Scanner stereoscopic 3D visualization NMR AVL 7
  • Exceptional Regional Collaboration • At least 6 active projects with AFRL/WPAFB • Human Effectiveness Directorate • Sensors Directorate 8
  • Exceptional National Collaboration • Univ. of Georgia, Stanford, Purdue, OSU, Ohio U., Indiana U. UC-Irvine, Michigan State U., Army, W3C • Microsoft, IBM, HP, Google 9
  • Exceptional International Collaboration • U. Manchester, TU-Copenhagen, TU-Delft, DERI (Ireland), Max-Planck Institute, U. Melbourne, U Queensland, NICTA- Australia,CSIRO, DA-IICT (India) 10
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Web powering Intelligent Enterprise and Web Applications Amit P. Sheth LexisNexis Ohio Eminent Scholar Ohio Center of Excellence in Knowledge enabled Computing (Kno.e.sis) Wright State University, Dayton, OH Technology Landscape 2013, Dayton OH. May 26, 2010
  • Evolution of the Web Web as an oracle / assistant / partner - “ask the Web”: using semantics to leverage text + data + services - Powerset 2007 Web of people - social networks, user-created casual content - Twine, GeneRIF, Connotea Web of resources - data = service = data, mashups - ubiquitous computing Web of databases - dynamically generated pages 1997 - web query interfaces Web of pages - text, manually created links - extensive navigation 12
  • OUTLINE • Semantic Web –key capabilities and technlologies • Real-world Applications demonstrating benefit of semantic web technologies • Exciting on-going research 13
  • Introduction 1 2 3 of Semantic Web 14
  • Introduction [1] • Ontology: Agreement with a common vocabulary/nomenclature, conceptual models and domain Knowledge • Schema + Knowledge base • Agreement is what enables interoperability • Formal description - Machine processability is what leads to automation 15
  • Introduction [2] • Semantic Annotation (Metadata Extraction): Associating meaning with data, or labeling data so it is more meaningful to the system and people. • Can be manual, semi-automatic (automatic with human verification), automatic. 16
  • From Syntax to Semantics Deep semantics Shallow semantics 17
  • Introduction [3] • Reasoning/Computation: semantics enabled search, integration, answering complex queries, connections and analyses (paths, sub graphs), pattern finding, mining, hypothesis validation, discovery, visualization 18
  • Characteristics of Semantic Web Self Easy to Describing Understand The Semantic Web:Machine & Issued by XML, RDF & Ontology a Trusted Human Authority Readable Can be Convertible Secured Adapted from William Ruh (CISCO) 19
  • SW Stack: Architecture, Standards 20
  • a little bit about ontologies
  • Many Ontologies Available e.g. Open Biomedical Ontologies Open Biomedical Ontologies, http://obo.sourceforge.net/ 22
  • From simple ontologies
  • Drug Ontology Hierarchy (showing is-a relationships) formulary_ non_drug_ interaction_ property formulary reactant property indication indication_ property owl:thing monograph property _ix_class prescription interaction_ _drug_ with_non_ brandname_ prescription brand_name drug_reactant prescription individual _drug interaction _drug_ property brandname_ brandname_ composite prescription interaction_ undeclared _drug_ with_mono interaction_ generic graph_ix_cl with_prescri cpnum_ generic_ ass ption_drug group composite generic_ individual 24
  • to complex ontologies
  • N-Glycosylation metabolic pathway GNT-I attaches GlcNAc at position 2 N-glycan_beta_GlcNAc_9 N-acetyl-glucosaminyl_transferase_V N-glycan_alpha_man_4 GNT-V attaches GlcNAc at position 6 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 UDP-N-acetyl-D-glucosamine + G00020 <=> UDP + G00021 26
  • A little bit about semantic metadata extractions and annotations
  • Metadata Creation Nexis Digital Videos UPI AP ... ... Feeds/ Data Stores Documents WWW, Enterprise Digital Maps Repositories ... Digital Images Digital Audios Create/extract as much (semantics) metadata automatically as possible; Use ontlogies to improve and enhance EXTRACTORS extraction METADATA 28
  • Automatic Semantic Metadata Extraction/Annotation 29
  • Significant presence • Life Science (biomedical) • Health Care (clinical) • Defense & Intelligence • Web
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Web in Action Financial Services Risk Management
  • Semagix Freedom Architecture (a platform for building ontology-driven information system) Knowledge Knowledge Semantic Enhancement Server Agents Sources KS Automatic Entity Extraction, Classification Enhanced KA Metadata, Ontology KS KA KS Content Content Sources Agents Metabase KA KS Databases CA XML/Feeds Websites CA Metadata Metadata Semantic Query Server Email adapter adapter Ontology and Metabase Existing Applications Main Memory Index CA Reports Documents ECM CRM EIP © Semagix, Inc.
  • Global Bank 6/3/201 33 0 • Aim • Legislation (PATRIOT ACT) requires banks to identify ‘who’ they are doing business with • Problem • Volume of internal and external data needed to be accessed • Complex name matching and disambiguation criteria • Requirement to ‘risk score’ certain attributes of this data • Approach • Creation of a ‘risk ontology’ populated from trusted sources (OFAC etc); Sophisticated entity disambiguation • Semantic querying, Rules specification & processing • Solution • Rapid and accurate KYC checks • Risk scoring of relationships allowing for prioritisation of results • Full visibility of sources and trustworthiness 2004 SEMAGIX All rights reserved.
  • The Process Ahmed Yaseer: • Appears on Watchlist ‘FBI’ Watch list Organization • Works for Company ‘WorldCom’ Hamas FBI Watchlist • Member of member of organization organization ‘Hamas’ appears on Watchlist Ahmed Yaseer works for Company WorldCom Company 2004 SEMAGIX All rights reserved.
  • Global Investment Bank Law Public World Wide BLOGS, Watch Lists Enforcement Regulators Records Web content RSS Semi-structured Government Data Un-structure text, Semi-structured Data Establishing New Account User will be able to navigate the ontology using a number of different interfaces Scores the entity based on the content and entity relationships Example of Fraud Prevention application used in financial services 2004 SEMAGIX All rights
  • Equity Research Dashboard Equity Research Dashboard with Blended Semantic Querying and Browsing Automatic 3rd party Focused content relevant integration content organized by topic (semantic categorization) Related relevant content not explicitly asked for (semantic associations) Automatic Content Aggregation from multiple Competitive content providers research and feeds inferred automatically
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Web in Action Defense & Intelligence
  • An Ontological Approach to Assessing IC Need to Know Sponsored by ARDA Work performed at LSDIS Lab, Univ. of Georgia March2005
  • Security and Terrorism Part of SWETO Ontology 6/21/2004
  • Schematic of Ontological Approach to the Legitimate Access Problem Semagix Freedom Semagix Freedom 6/21/2004
  • Graph-based creation: A Context of Investigation 26,489 entities 34,513 (explicit) relationships Add relationship to context 6/21/2004
  • Show me the stuff … See demonstration at: http://knoesis.org/library/demos 6/21/2004
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Web in Action Supporting Clinical Decision Making
  • Clinical Decision Making • Status: In use today • Where: Athens Heart Center • What: Use of Semantic Web technologies for clinical decision support
  • Operational Since January 2006
  • Active Semantic Electronic Medical Records (ASEMR) 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 paper
  • ASEMR - Demonstration See demonstration at: http://knoesis.org/library/demos
  • ASMER Efficiency Chart Completion before the preliminary deployment 600 500 400 Charts Same Day 300 Back Log 200 100 0 Chart Completion after the preliminary deployment Se 4 5 04 05 04 05 04 05 04 04 l0 l0 n n ay ay pt ar ar ov Ju Ju 700 Ja Ja M M M M N 600 500 Month/Year Charts 400 Same Day 300 Back Log 200 100 0 Sept Nov 05 Jan 06 Mar 06 05 Month/Year
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Scooner: Semantic Browser A tool for knowledge discovery with examples from Scientific Literature
  • OVERVIEW 1. Novel Information Exploration Paradigm  Text Exploration on the context of relationships  Not hyperlinks 2. Demonstrate use of background knowledge  Named Entities, Relationships 3. Prototype Implementation  Semantic annotations for navigation 4. Aggregation Utilities  Saving, bookmarking, publishing etc 50
  • WHY SCOONER?  Query Reformulations  Impatient users  Recognition over Recall  Constrained navigation  Hyperlink dependent - apriori Fuzzy User Interests  Haiti Earthquake – Recovery, Relief, Political Climate, Crime Current approaches are not as effective for Exploratory Search (Search-and-Sift) Amit P. Sheth, Cartic Ramakrishnan: Relationship Web: Blazing Semantic Trails between Web Resources. IEEE Internet Computing 11(4): 77-81 (2007)
  • MOTIVATION  Users are information seekers Information is embedded in documents  A priori hyperlink dependent  Semantic Web Standards  Entity Identification (Semantic Annotations)  Relationshipand Triple Identification  Explore documents/information via relationships 52
  • Use Case Scenario Search Phrase: Magnesium 53
  • Use Case Scenario 54
  • Use Case Scenario 55
  • SUMMARY  Novel Information Exploration Paradigm  Semantic Browser support Contextual Navigation  Identify Named Entities and Relationships  Provide Semantic Annotations  Utilities for Aggregation  Semantic Trails to Knowledge Discovery See demonstration at: http://knoesis.org/library/demos 56
  • 48th ACM Southeast Conference. ACMSE 2010. Oxford, Mississippi. April 15-17, 2010. Semantic Sensor Web Kno.e.sis Center Wright State University http://knoesis.org/projects/sensorweb
  • Semantic Sensor Web Sensors are now ubiquitous, and constantly generating observations about our world
  • Semantic Sensor Web However, these systems are often stovepiped, with strong tie between sensor network and application
  • Semantic Sensor Web We want to set this data free
  • Semantic Sensor Web With freedom comes new responsibilities ….
  • Semantic Sensor Web (1) How to discover, access and search the data? Web Services - OGC Sensor Web Enablement (SWE)
  • Semantic Sensor Web (2) How to integrate this data together, when it comes from many different sources? Shared knowledge models, or Ontologies - syntactic models – XML (SWE) - semantic models – OWL/RDF (W3C SSN-XG)
  • Semantic Sensor Web Sensor Observation Ontology
  • Semantic Sensor Web The SSN-XG Deliverables • Ontology for semantically describing sensors • Illustrate the relationship to OGC Sensor Web Enablement standards • Semantic annotation of OGC Sensor Web Enablement standards
  • Semantic Sensor Web Linked Open Data: a community-led effort to create openly accessible, and interlinked, semantic (RDF) data on the Web.
  • Semantic Sensor Web Sensors Dataset • RDF descriptions of ~20,000 weather stations in the United States. • Observation dataset linked to sensors descriptions. • Sensors link to locations in Geonames (in LOD) that are nearby. near weather station
  • Observations Dataset • RDF descriptions of hurricane and blizzard observations in the United States. • The data originated at MesoWest (University of Utah) • Observation types: temperature, visibility, precipitation, pressure, wind speed, humidity, etc. 69
  • Linked Datasets procedure location Observation Location KB Sensor KB KB (Geonames) Example procedure location 720F Thermometer Dayton Airport • ~2 billion triples • 20,000+ systems • 230,000+ locations • MesoWest • MesoWest • Geonames • Dynamic • ~Static • ~Static 70
  • Semantic Sensor Web (3) How to make numerical sensor data meaningful to web applications and naïve users? Symbols more meaningful than numbers - active perception
  • Active Perception: • is an iterative, bi-directional feedback loop for collecting and explaining sensor data Explanation Observation Expectation Attention 72
  • Overall Architecture 73
  • DEMOS Semantic Sensor Web Demos at http://wiki.knoesis.org/index.php/SSW •Sensor Discovery On Linked Data •Semantic Sensor Observation Service (MesoWest) •Video on the Semantic Sensor Web 74
  • Ohio Center of Excellence Knowledge-Enabled Computing (Kno.e.sis) SEMANTIC SOCIAL WEB
  • Everyone Wants to talk …and be heard! Hundreds and thousands of tweets, facebook posts, blogs about a single event, multiple narratives, strong opinions, breaking news.. 76
  • TWITRIS : Twitter+Tetris • Our attempt to help you keep up with citizen observations on Twitter – WHAT are people saying, WHEN, from WHERE • Puts citizen reports in context for you by overlaying it with news, wikipedia articles! 77
  • See demo and live system at http://twitris.knoesis.org 78
  • How we work with industry Interns, Training SBIR/STTR Joint contracts Tech Transfer/licensing 79
  • More of Web 3.0 Semantics enhanced Web, Social, Sensor and Services Computing, and their applications to health care, life sciences, DoD, IT/Data management, … at http://knoesis.org