The document discusses the development and potential of semantic technologies in enhancing travel experiences through context-aware applications. It presents scenarios showcasing how semantic tools can improve traveler support during disruptions and streamline advance travel purchases by leveraging user data and preferences. It emphasizes the need for creating semantic concierges that integrate various services, improve user interaction, and foster smarter travel ecosystems.

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
– Discussion
Delivered by semantic web guru and road warrior…

4. Scenario 1: Real
Time Travel Change
Change 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

5. 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 off
Much of this is freely available and with passenger data
including loyalty memberships, this could be connected easily

6. 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

7. Semantic Web: Introduction
through examples
• Semantic Sommelier: Mobile Context Aware Semantic Wine
Advisor
• SemantAqua: Semantically-Aware information aggregator &
visualizer
There are many more….
• PopSciGrid – aimed at Preventable Cancers
• DARPA Personal Assistant that Learns -> SIRI
• IARPA Analyst Workbench -> Watson
• Home Theatre Advisor Configurator

8. 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

9. Semantically-enabled advisors
utilize:
• Ontologies
• Reasoning
• Social
• Mobile
• Provenance
• Context
Patton & McGuinness.et. al
tw.rpi.edu/web/project/Wineagent

10. Semantic
Sommelier
Previous versions used ontologies
to infer descriptions of wines for
meals and query for wines
New 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… however
new models emerging

11. 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

12. SemantAqua Workflow
Publish
CSV2RDF4LOD
Direct visualize
derive archive derive
Archive
CSV2RDF4LOD
Enhance 12

13. 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

14. 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 repositories
OWL 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

16. Foundations: The Tetherless World
Constellation Linked Open Government
Data Portal
Convert TWC LOGD
Query/
Access
LOGD Community Portal
SPARQL • RDF
Endpoint • RSS
• JSON
Create • XML
• HTML
• CSV
•…
Enhance
Data.gov deployment
16

17. Ontology Spectrum
Thesauri
“narrower Formal Frames General
Catalog/ term” is-a (properties) Logical
ID 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

18. Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI …
The Virtual Solar-Terrestrial
Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19
Conf. on Innovative Applications of Artificial Intelligence (IAAI-07),
http://www.vsto.org

19. 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

20. Back to Travel
Existing mobile and web site applications allow online
browsing, status checks, mobile access, with purchase
options
However 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.

21. Remember Scenario 1 and 2

22. • 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…

23. Questions?
dlm @ cs . rpi . edu
What would you like from your semantic
travel concierge ?
Acknowledgements: Thanks to Opentravel and Thematix for
motivating discussions.

24. Extra