Semantic Computing Executive Briefing
- 1. Graeme Wood General Manager Marketing Enhancing Business Insights by Liberating your Data
- 2. Agenda • General Introduction of Semantic Software • Welcome and Introduction to IoT and Big Data • Introduction to Semantic Computing concepts • Benefits of Semantic Computing to Government • Q&A • Close
- 3. Who is Semantic Software • An Australian Company with HQ in North Sydney • Owned by over one hundred and thirty private investors • Developed a Semantic Interoperability Platform • Created a strong and strategic patent portfolio that has patents & patent applications in Australia and the United States • Implemented a training division called Semantic Academy for the delivery of technical workshops, executive presentations and training modules for Tertiary Institutions
- 4. Evolution of the World Wide Web Semantic Software 4
- 5. Current Data Issues • CURRENT DATA ISSUES • Rigid Data Structures • Distributed Data Sources • Unstructured Data • Limited Context , Meaning, Sources • Growth of Social Collaboration • Resource Requirements to Manage
- 6. How many devices record information?
- 7. How much Information does the bank use to manage clients needs?
- 8. How Integrated is Data in Government ?
- 9. Internet of Things (IoT) • The IoT is a giant network of connected “things” (which also includes people). • The relationship will be between people- people, people-things, and things-things. • ISSUES • Security • Technology & Interoperability • Privacy, IP and Data Sharing • “Data lake” Storage Requirements • Unstructured data • Data interoperability • Meaningful Analysis of Data
- 10. Internet of Things (IoT)
- 11. Internet of Things & Cognitive Computing • IoT is already overwhelming data analytics • Driving the need for cognitive computing • Cognitive computing requires • A semantic knowledge base for meaning • Natural Language Processing to ingest unstructured data • Algorithmic methods to make propositions • Machine Learning to improve propositions Semantic Software 11
- 12. Agenda • General Introduction of Semantic Software • Welcome and Introduction to IoT and Big Data • Introduction to Semantic Computing concepts • Benefits of Semantic Computing to Government • Q&A • Close
- 13. Evolution of Data Mining Technologies
- 14. What is Semantic Computing • Semantic Computing is at the core of Cognitive Computing processes, and is enhanced by other Artificial Intelligence techniques such as Natural Language Processing and Machine Learning • Semantic Computing is the facilitator of key IT Tools of the future providing INTEROPERABILITY and CONTEXT • The Key elements that engage with Semantic Computing include: • Internet of Things (IoT) • Big Data & Analytics • Cloud Computing • Collaborative /Social Media
- 15. Concepts in Semantic Computing “I got a great shot of an elephant at a zoo.” Semantic Software 16
- 16. Concepts in Semantic Computing – RDF Resources • Data is transformed into granular facts describing “Resourses” • These facts are expressed in Subject–Predicate–Object form, called Triples • Subjects and Objects are Resources • The Resource Description Framework (RDF) defines the above • Example: • “I got a great shot of an elephant at a zoo.” • Nouns = I, shot, elephant, zoo • Verbs = got, shot • Preposition = of, at, are • RDF Triples generated: • I got a shot • I shot an elephant • I was at the zoo • An elephant was at the zoo • The shot occurred at the zoo Semantic Software 17
- 17. Concepts in Semantic Computing - Ontologies • An Ontology is a dataset of “rules” that describe a domain of knowledge • They use RDF formats Semantic Software 18 Zoo Ontology FOAF Ontology Photography Ontology A zoo houses animals I means a person Cameras make photos Elephants are animals People is the plural of person A shot is a photo People take photos
- 19. Semantic Web Stack • "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." -- Tim Berners-Lee
- 20. The “Intelligent” Web Semantic Software 21 Professor Sir Tim Berners-Lee (inventor of WWW and Semantic Web)” • 1999 “we don’t know how to do trust, security and proof • In 2006, TBL and colleagues: "This simple idea...remains largely unrealized". • Rust, security and proof recently reaffirmed as “unrealized technologies”
- 21. Agenda • General Introduction of Semantic Software • Welcome and Introduction to IoT and Big Data • Introduction to Semantic Computing concepts • Benefits of Semantic Computing to Government • Q&A • Close
- 22. Example of Relationship Finder Query
- 23. . 25 • Monetary: profit / loss, assets / liabilities, equity • Law and Jurisdiction • Government, regulatory environment • Contracts, agreements, commitments • Products and Services • Other e.g. geopolitical, logistics Key areas for decision making that Semantic Computing supports
- 24. Semantic Computing in Finance • customer-focus • risk management • increase flexibility • combining disparate sources • regulatory reporting • insider trading controls • data governance • accessing information quickly
- 25. Benefits of Semantic Computing to Government • Comprehensive Interaction with the Public • Supports Policy Development • Supports Integrated National Security • Manages Risk, Governance and Compliance
- 26. Semantic Computing in Government Treatment Plan/Workflo w Options Drugs/ Gene Interactions Wearable Diagnostic Support Reduce Excessive Payouts Membershi p Engagemen t Reduce Claim Fraud Correlate Diverse Data Sources Exploit Specialist Ontologies Expanded Govt Access and Sharing Tailor Holistic Treatment Providers Payers Research Expanded Analytic Tasks Personal Patient Files Consumers
- 27. Semantic Computing in Healthcare • forecast patient behaviour • provide preventive care • impact of infections on patients • identify high volume service users • determine the probability of readmission • improve response rates • identify cardiac failure risk
- 28. Semantic Computing in Healthcare Treatment Plan/Workflo w Options Drugs/ Gene Interactions Wearable Diagnostic Support Reduce Excessive Payouts Membershi p Engagemen t Reduce Claim Fraud Correlate Diverse Data Sources Exploit Specialist Ontologies Expanded Govt Access and Sharing Tailor Holistic Treatment Providers Payers Research Expanded Analytic Tasks Personal Patient Files Consumers
- 29. NAVY ENERGY Case Study : US Navy