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The Semantic Travel Concierge - a vision of the potential of semantic technologies for the travel industry. Deborah L. McGuinness Keynote at the Opentravel Alliance Advisory Forum - Miami, Fla, April …

The Semantic Travel Concierge - a vision of the potential of semantic technologies for the travel industry. Deborah L. McGuinness Keynote at the Opentravel Alliance Advisory Forum - Miami, Fla, April 11, 2012.

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  • 1. Towards the Semantic Concierge Deborah L. McGuinness Tetherless World Senior Constellation Chair Professor of Computer and Cognitive Science Rensselaer Polytechnic Institute, Troy, NY & CEO McGuinness Associates, Latham, NY OpenTravel 2012 Advisory Forum April 11, 2012
  • 2. Background– Semantic Technologies – technological support for encoding meaning in a form computers can understand and manipulate – are maturing and increasing in usage– Computational encodings of meaning can be used to help integrate, validate, filter,…. Essentially to make smarter, more context-aware applications– This can provide competitive advantage in todays increasingly networked and competitive environments– Motivating Vision– Semantic Web intro through examples: Ontologies, Mobile Advisor, Linked Data– DiscussionDelivered by semantic web guru and road warrior…
  • 3. Scenario 1: Real Time Travel ChangeChange and the disrupted traveler • Weather or mechanical issues mean leaving a traveler stranded at an intermediate destination • Default coping strategy is not desirable to traveler (i.e., local intermediate hotel and transportation the next day) • Transportation to an alternative airport somewhat near desired destination along with confirmed new last one way leg (e.g. one way car rental available at the arrival time) would be vastly superior
  • 4. Scenario 1 Information Requirements• Location: – Current airport – Destination airport (and/or final destination) – Airports within x hour driving radius• Schedules – Flights to alternative airports in correct time period• Connections to other ecosystem partners – Other transportation options to final destination (train, bus, car with a local starting point) – Other transportation options from alternative destination to final destination – Critical parameters: availability, one-way rental, operating hours for pickup/drop offMuch of this is freely available and with passenger dataincluding loyalty memberships, this could be connected easily
  • 5. Scenario 2: Advance Travel purchase• Known departure and destination airport• With air loyalty program information, could offer air approp. options (confirmed upgrades, lay back seat, reclining seat, discounted business vs. full fare economy….)• With destination address and hotel loyalty programs, could offer hotel rooms near address with loyalty brands (including benefits of loyalty program customer tier – e.g., upgrades, discounts, etc.)• With car loyalty programs, could offer car options• If additional tickets purchased, could offer larger car• If with spouse (often in air systems), could offer more leisure packages• If connected to restaurant booking (foursquare, open table, …) or preferences, could offer dinner bookings• If connected to online calendar (e.g., Google), could pick up conference information
  • 6. Semantic Web: Introduction through examples• Semantic Sommelier: Mobile Context Aware Semantic Wine Advisor• SemantAqua: Semantically-Aware information aggregator & visualizerThere are many more….• PopSciGrid – aimed at Preventable Cancers• DARPA Personal Assistant that Learns -> SIRI• IARPA Analyst Workbench -> Watson• Home Theatre Advisor Configurator
  • 7. Notes• Examples are from other domains because travel domains have not been built out to the same level. Most travel examples are at a syntactic level (or light weight semantics) such as aggregators or natural language interfaces with some semantics (such as Siri) but less about actually “thinking” or acting as a personal assistant and more about finding information• One take home message after these examples will be that the time is now to build the same kind of applications described in the next few slides in travel…. Creating customized semantic concierges
  • 8. Semantically-enabled advisorsutilize: • Ontologies • Reasoning • Social • Mobile • Provenance • ContextPatton & McGuinness.et. altw.rpi.edu/web/project/Wineagent
  • 9. Semantic SommelierPrevious versions used ontologiesto infer descriptions of wines formeals and query for winesNew version uses Context: GPS location, local restaurants and wine lists, user preferences Social input: Twitter, Facebook, Wiki, mobile, …Source variability in quality,contradictions exist,Maintenance is an issue… howevernew models emerging
  • 10. SemantEco/SemantAqua• Enable/Empower citizens & scientists to explore pollution sites, facilities, regulations, and health impacts along with 5 4 provenance. 2 3• Demonstrates semantic monitoring possibilities.• Map presentation of analysis 1• Explanations and http://was.tw.rpi.edu/swqp/map.html and http://aquarius.tw.rpi.edu/projects/semantaqua Provenance available 1. Map view of analyzed results 2. Explanation of pollution 3. Possible health effect of contaminant (from EPA) 4. Filtering by facet to select type of data 5. Link for reporting problems
  • 11. SemantAqua Workflow PublishCSV2RDF4LOD Direct visualize derive archive derive Archive CSV2RDF4LOD Enhance 12
  • 12. Why did I present wine and water applications?• Wine advisor shows semantic technology in action making actionable recommendations• Water application shows a “typical” semantic integration web 3.0 application• Both of these styles are needed for a semantic concierge and these features are realizable today• Next – they depend on – Semantic web stack – Data availability – linked data cloud is growing – Ontologies – Semantic methodologies – Understandable and Actionable applications
  • 13. Foundations: Web Layer Cake Visualization APIs S2S Govt Data Inference Web, Proof Markup Language, W3C Inference Web IW Trust, Provenance Working Air + Trust group formal model, W3C incubator group, DL, KIF, CL, N3Logic … Ontology repositoriesOWL 1 & 2 WG Edited main OWL (ontolinguag), Docs, quick reference, Ontology Evolution env: OWL profiles (OWL RL), Chimaera, Earlier languages: DAML, Semantic eScience DAML+OIL, Classic Ontologies, MANY other ontologie RIF WG AIR accountability tool SPARQL WG, earlier QL – OWL-QL, Classic’ QL, … Govt metadata search Linked Open Govt Data SPARQL to Xquery translator RDFS materialization (Billion triple winner) Transparent Accountable Datamining Initiative (TAM
  • 14. Foundations: The Tetherless World Constellation Linked Open Government Data Portal Convert TWC LOGD Query/ Access LOGD Community Portal SPARQL • RDF Endpoint • RSS • JSONCreate • XML • HTML • CSV •… Enhance Data.gov deployment 16
  • 15. Ontology Spectrum Thesauri “narrower Formal Frames GeneralCatalog/ term” is-a (properties) LogicalID relation constraints Terms/ Informal Formal Disjoint- Value ness, glossary is-a instance Restrs. Inverse, part-of… From 99 AAAI panel with Gruninger, Lehmann, McGuinness, Uschold, Welty. , 2000 Dagstuhl talk by McGuinness
  • 16. Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI … The Virtual Solar-TerrestrialObservatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19Conf. on Innovative Applications of Artificial Intelligence (IAAI-07), http://www.vsto.org
  • 17. Inference Web: Making Data Transparent and Actionable Using Semantic Technologies• How and when does it make sense to use smart system results & how do we interact with them? (Mobile) Knowledge Intelligent Provenance in Virtual Agents NSF Interops: Observatories SONET SSIII – Sea Ice Intelligence Analyst Tools Hypothesis Investigation / Policy Advisors 19
  • 18. Back to TravelExisting mobile and web site applications allow onlinebrowsing, status checks, mobile access, with purchaseoptionsHowever they often• Are not well connected into travel ecosystems• Do not use sensor-based context – e.g., GPS• Are not connected to user context – previous queries, and actions, google calendar, loyalty connections, status levels etc.
  • 19. Remember Scenario 1 and 2
  • 20. • Semantic Technologies: ready for use• The Semantic Web Tools & tutorials available; deep apps enables… future planning may benefit from consultants• • New models of intelligent services Context-aware, semantic apps are the future • E-commerce solutions • M-commerce • Web assistants • … New forms of web assistants/agents that act on a human’s behalf requiring less from humans and their communication devices…
  • 21. Questions?dlm @ cs . rpi . eduWhat would you like from your semantictravel concierge ?Acknowledgements: Thanks to Opentravel and Thematix formotivating discussions.
  • 22. Extra

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