Titi

12/2/2013
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Touristic Intelligence Tirol (TiTi)
Dieter Fensel, Andreas Lackner,
Christian Maurer, and Bernhard Rieder
with the help of ...
1STI Innsbruck
With the help of …
Birgit JuenZaenal Akbar Dr. José María GarcíaDr. Anna Fensel
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Dr. Nelia Lasierra Ioannis Stavrakantonakis Serge TymaniukDr. Ioan Toma

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What is the talk about?
• The Challenge:
Successful value generation through Tourisms in the 21st century
• An essential means:
A shared IT infrastructure for effective and efficient marketing and sales.
• Follow-Up opportunities
• Summary
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y
The Challenge
Successful value generation through Tourisms
in the 21st century
4STI Innsbruck

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The Challenge
The Hotelier of today has to deal with many different communication channels:
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HOTEL
RECEPTION
The Challenge
- walk-in customerThe Hotelier of today has to deal with many different communication channels:
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HOTEL
RECEPTION

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- walk-in customer
- phone
The Challenge
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HOTEL
RECEPTION
The Challenge
- walk-in customer
- phone
-email
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HOTEL
RECEPTION

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The Challenge
- walk-in customer
- phone
- email
- fax
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HOTEL
RECEPTION
The Challenge
- walk-in customer
- phone
- email
- fax
- hotel website
hotel website
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HOTEL
RECEPTION

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The Challenge
- walk-in customer
- phone
- email
- fax
- hotel website
- review sites
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HOTEL
RECEPTION
The Challenge
- walk-in customer
- phone
- email
- fax
- hotel website
- review sites
- booking sites
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HOTEL
RECEPTION

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The Challenge
- walk-in customer
- phone
- email
- fax
- hotel website
hotel website
- review sites
- booking sites
- social network sites
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HOTEL
RECEPTION
The Challenge
The Hotelier of today has to deal with many different communication channels: - walk-in customer
- phone
- email
- fax
- hotel websitehotel website
- review sites
- booking sites
- blogs
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HOTEL
RECEPTION

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The Challenge
- phone
- email
- fax
- review sites
- booking sites
- blogs
- fora & destination sites
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HOTEL
RECEPTION
The Challenge
- phone
- email
- fax
- review sites
- booking sites
- blogs
- chat
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HOTEL
RECEPTION

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The Challenge
- phone
- email
- fax
- review sites
- booking sites
- blogs
- chat
- video & photo sharin
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HOTEL
RECEPTION
The Challenge
The Hotelier doesn’t
only have to deal with
an overwhelming
number of
communication
channels but also haschannels, but also has
to pay up to 15% sales
commissions to the
booking sites!
-> 100 million € sales
commission
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HOTEL
RECEPTION

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The Challenge
-> 80 million overnight stays
-> 4 billion € transaction
volume
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HOTEL
RECEPTION
The Challenge
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source: http://infographicsmania.com/online-travel-statistics-2012/

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The Challenge
• More than 55% of all tourists in Central Europe inform themselves on-
line about a certain destination before booking, and more than 27% of
fall tourists in this area use internet-based booking channels for
reserving their tourism plans. And this number is constantly growing!
• In 2008, 1 in 3 trips was booked through travel agencies whereas in
2012 it was only 1 in 5!
• Furthermore, 70% of all online bookings are influenced by social media!
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The Challenge
• Influence of Social Media to travel planning:
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http://eprints.bournemouth.ac.uk/19262/1/Fotis_et_al_2012_-_Social_media_use_and_impact_during_the_holiday_travel_planning_process.pdf

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The Challenge
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The Challenge
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The challenge
Key challenge are:
• Professional on-line marketing for attracting potential customers
requires:
– Content of high quality and originality
– Visible presented at multiple places in various formats and adaptations
• Professional on-line sales for gaining customers requires:
– On-line bookability
– Tight alignment of content and bookings opportunities (i.e., content and booking data)
– Multiple booking opportunities (fluid booking)
– Multi channel yield management and revenue optimization (swarm intelligence and big
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data)
• Obviously even more is needed to cover to full-fledged customer
journey
The solution
A shared IT infrastructure for
effective and efficient marketing and sales
26STI Innsbruck

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The kernel
Where we go …
Semantic Alignment
Semantic Annotations
Repository
Touristic Ontology TISs
CMSs
TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Semantic Search
g
Web pages
Social Media
Channels
Mobile
Channels
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The kernel
Semantic Alignment
Repository
Touristic Ontology TISs
CMSs
TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Semantic Search
g
Web pages
Social Media
Channels
Mobile
Channels
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Repository
Touristic Ontology
TISs
CMSs
.. and how we start:

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The kernel
Repository
Touristic Ontology
TISs
CMSs
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The kernel - Repository
• “A content repository is a store of digital content with an associated set of data
management, search and access methods allowing application-independent access
to the content ” (Wikipedia 2013)to the content… (Wikipedia, 2013).
• In terms of CMS a repository acts as a ground layer for long-term storage, structured
data management and digital preservation.
• Common features:
– Data storage
– Data querying
– Data management
Repository
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– Data management
• Semantic repository is a store of structured data allowing
– to store, query and manage the data,
– and automatically reason about the data by using ontologies as semantic schemata.

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• Efficient data access and collaborative data management allowing
working with the same content consistently.
• Greater focus on digital preservation by better support of archiving,
versioning, etc.
• Abstraction layer support for employing complex functionality in a
uniform way.
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• Reasoning capabilities by implementing inference layers.
• Synchronization on the repository level.
• Native: Persistent storage systems with their own implementation of
RDF databases. Provide support for transactions, own query compiler
and generally their own procedure language
– E.g., SwiftOWLIM, BigOWLIM , Sesame Native, AllegroGraph.
• Non-Native: Persistent storage systems set-up to run on third party
DBs.
– E.g. Jena SDB.
• In-Memory: RDF Graph is stored as triples in main
memory
E S iftOWLIM All G h S N ti
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– E.g. SwiftOWLIM, AllegroGraph, Sesame Native.
• Non-in-Memory: RDF Graph is stored not in main
memory
– E.g. BigOWLIM, AllegroGraph.

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• AllegroGraph is a native RDF graph database:
– Scaling to billions of triples
– Support of RDF/XML, N3, N-Triples serialization formatsSupport of RDF/XML, N3, N Triples serialization formats
– SPARQL, PROLOG queries
– Built-in reasoners (Jena, Sesame, Racerpro)
– Free and Commercial versions.
• Virtuoso is a native, RDBMS-based semantic repository:
– Scaling to billions of triples
– Support of RDF/XML, N3 serialization formats
– SPARQL/SPASQL queries
– Built-in reasoners (Jena, Sesame, Redland)
Free and Commercial versions
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– Free and Commercial versions.
• Oracle11g:
– Scaling to millions of triples
– Support of RDF/XML, N-triples serialization formats
– SQL and SPARQL queries
– Native inferencing and 3rd party reasoned support (Jena)
– Commercial version.
• OWLIM is a scalable semantic repository which allows:
– Management, integration, and analysis of heterogeneous data
– Combined with light-weight reasoning capabilities
– Available as a Storage and Inference Layer (SAIL) for Sesame Open
RDF
• Sesame’s infrastructure
• Support for multiple query language (RQL, RDQL, SeRQL)
• Support for import and export formats (RDF/XML, N-Triples, N3).
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• SwiftOWLIM: in-memory reasoning and query evaluation, fast retrieval,
query evaluation, scales to ~100M statements.
• BigOWLIM: more scalable not-in-memory enterprise class repository.

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The kernel - Ontology
• Define a domain-specific data model in the repository.
• This touristic Ontology can be used to mediate between different data models reducing the
mapping problem from n! to n .
Repository
Touristic Ontology
mapping problem from n! to n .
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The Problem 
Multiple & heterogeneous sources of information
- Different formats & structures
- Common usage purpose
- Touristic Information Systems
- Content Management Systems
- Web pages
- Social Media Channels
Mobile Channels
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- Mobile Channels
- External Data (LOD) and Content
- Hotel on-line content and data
- Content and data of the various touristic associations
- Content and data of the Tirol Werbung

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The Solution  Touristic Ontology
Integration Layer to provide a common and clear understanding to:
Touristic Ontology
- Unify data & management
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Repository
First … what is an ontology?
“An ontology is a formal explicit specification of a shared conceptualization”
An ontology is a formal, explicit specification of a shared conceptualization
Studer, Benjamin, Fensel. Knowledge Engineering: Principle and Methods. Data and knowledge engineering, 25 (1998) 161-197
In simple words….
• Ontologies represent concepts and basic relations for the purpose of
comprehension of a knowledge domain area. To develop an ontology
means to formalize a common view of a certain area.
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• Ontologies model knowledge about a specific domain.
• They provide a common vocabulary, the meaning of the terms and also the
relation among the terms in order to provide a common and shared
understanding for the comprehension of a domain.

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Specifically, in the Tourism domain…
Vocabulary and relations for describing hotel elements and characteristics.
• Example of a Tourism ontology: Accommodation Ontology Metadata
[University of Innsbruck, Martin Hepp]
– This Accommodation Ontology is an extension of GoodRelations.
– Provides the additional vocabulary elements for describing hotel rooms, hotels, camping sites, and other forms
of accommodations, their features, and modeling compound prices as frequently found in the tourism sector,
e.g. weekly cleaning fees or extra charges for electricity in vacation homes based on metered usages.
– http://ontologies.sti-innsbruck.at/acco/ns.html
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• Schema.org vocabularies
- Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that
can be understood by the major search engines.
– LodgingBusiness > Hotel , Bed &Breakfast, Hostel, Motel
– Also vocabularies for describing events, restaurants & touristic attractions
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Repository
Touristic Ontology

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Schema.org vocabularies (Example: http://schema.org/Hotel)
Property ExpectedType Description
description Text A short description of the item.
image URL URL of an image of the item.
name Text The name of the item.
review Review A review of the item.
telephone Text The telephone number
location Place or PostalAddress The location of the event, organization or action.
i H D ti Th i h f b i
Thing
Place
Organization
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openingHours Duration The opening hours for a business.
priceRange Text The price range of the business, for example $$$.
paymentAccepted Text Cash, credit card, etc.
Local
Business
Which is the added value of the ontology?
- Common knowledge model  Clear understanding
- Common knowledge model  Clear understanding
- Integration layer 
- Reduce the number of mappings between information sources (n:n  n:1) from n! to n.
- Provide a background knowledge for systems to automatize certain tasks. Can be
used to perform meaningful and inteligent queries  Semantic Search
- Allows to exchange data and to interpret the information in the data that has been
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Allows to exchange data and to interpret the information in the data that has been
exchanged in the right context  Semantic Alignment
- Allow the creation of machine readable annotations  Semantic Annotations

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Touristic Information Systems include:
• Property Management Systems,
H l I f i S
The kernel – Touristic Information Systems
• Hotel Information Systems,
• Points of Sales,
• Accounting and
Payroll Systems,
• Inventory control
Systems,
• Booking and
B ki Ch l
Repository
Touristic Ontology
TISs
CMSs
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Booking Channel
Management Systems.
Information integration and use across these systems is not a trivial task,
because:
foundations and concepts behind backend solutions for e Tourism• foundations and concepts behind backend solutions for e-Tourism
(Touristic Information Systems) vary.
• knowledge on the backend solutions for e-Tourism and the
functionalities they provide needs to be applied in a consistent manner.
• existing implementation of backend systems and their use in practice
varies.
• being able to select, in the light of concepts, functionalities and existing
solutions the appropriate backend system for a given tourism business
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solutions the appropriate backend system for a given tourism business
setting and make that easily interoperable is essential.

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Import of data from Touristic Information Systems (TISs) will facilitate multi-
platform touristic operation, such as:
• unifying principles and methods that enable bookings electronically;
• enabling advanced concepts on booking, direct and multi-channel
booking;
• understanding, anticipation and influencing tourists behavior in order to
maximize hotel yield or profits;
• facilitating getting a clear overview of the varieties in booking software
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solutions available on the market;
• making the appropriate solution choices and apply booking and yield
management practically.
Addressing the needs of various stakeholders with the ontologized layer for
integration of TISs data:
• Tourists, e.g.:
– Effective integrated availability of offers availability data
– Possibility to more efficiently combine available offers
• Hotels and tourism service providers, e.g.:
– Improved information and process management across heterogeneous TISs
– Receiving data across TISs to enrich their own data and content (e.g. linking to the
events in the area which could draw tourists)
• Touristic associations, e.g.:
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, g
– Enabled easier access to the data from hotels and other tourism service providers
– Possibility to easier combine and aggregate data, serve as a better intermediary

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The kernel – Content Management Systems
Import from and export to Content Management Systems (CMSs)
Repository
Touristic Ontology
TISs
CMSs
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• 38,4% of the websites use Web Content Management Systems and the CMS market
share is constantly increasing (W3Techs.com report, Nov. 2013)
• CMS is referred to “a computer program that allows publishing, editing and modifying
content as well as maintenance from a central interface” (Wikipedia, 2013).
• Types:
– Component Content Management System (CCMS)
• Methods and tools for managing and storing re-usable assets, concept items, topics
within documents.
– Enterprise Content Management Systems (ECMS)
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Enterprise Content Management Systems (ECMS)
• Methods and tools for managing and storing enterprise’s content and documents related
to organizational processes.
– Web Content Management System (WCMS, often referred as CMS)
• Methods and tools for online and/or offline managing and storing the website content.

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• Adding, editing, delivering, sharing, retrieving, analyzing, controlling
and administering online content.
• Uses content repository or a database for data storage.
• Supplied as off-the-shelf or custom-developed tailored to specific
needs.
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• Based on content and design separation.
• Presentation layer consists of basic CMS templates, which act as a
webpage blueprint.
• Dynamic page creation on demand through data extraction from
database/content repository.
Customization is achieved by adding plug ins with custom features
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• Customization is achieved by adding plug-ins with custom features.
• Major technologies used: Java, PHP, Python, MySQL, Perl.

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44%
Use a CMS
system
38%
Use a CMS
system
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WCMS distribution of Austrian hotels
WCMS distribution world-wide
W3Techs.com, Nov.2013
Feature 1: Sharing of Content and Data
Solving the Data and Content Bottleneck: Content and Data sharing between different touristic agents
Repository
Touristic Ontology
TISs
CMSs
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TSPs TAs Tirol
Werbung

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Feature 1: Sharing of Content and Data
Solving the Data and Content Bottleneck: Content and Data sharing between different touristic agents.
• Successful on-line marketing requires high and up-to-date quality on-line content.
• Successful on-line sales requires and up-to-date booking data describing available offers and their• Successful on-line sales requires and up-to-date booking data describing available offers and their
prices.
• Providing these at the proper level of quantity, quality, and up-to-date is rather cost-intensive and
therefore a major obstacle.
• This problem appears at the layer of the
• Touristic service provider
• Touristic association
• Tirol Werbung
• Large budgets are spent on this issue and many web presences miss a lot of potential due to the
related costs.
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• Therefore, we propose an infrastructure that makes sharing of content and data easy and cheap.
• Based on injecting content and data in our kernel it can be easily exported towards the on-line
presence of a certain agent as well as it can be shared with and reused by other touristic agents.
Feature 2: Enrichment
Solving the Data and Content Bottleneck: Enrich with external content and data
Repository
Touristic Ontology
TISs
CMSs
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TSPs TAs Tirol
Werbung
External Data (LOD)
and Content

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Web of Content
 The Web is a web of content The Web is a web of content
 Content is any textual, visual or aural information that could be found on the
websites.
 For a specific entity, e.g. a hotel, we can find on the Web a lot of content
scattered across various websites.
 Review sites
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 Sharing sites
 Wikis
 etc.
• Web of Content
Web of
content
VideosText
Review
Sites
Wikipedia
Vimeo
YouTube
etc.
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Images
Flickr
Picasa
etc.

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― Content can be crawled or
retrieved via APIs, like in
Hotelnavigator and Trust You
services
― Make it accessible through the
kernel of the architecture
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• Web of Documents • Web of Data
Hyperlinks
Typed Links
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“Documents”
“Things”

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• Characteristics:
– Links between arbitrary things
• Web of Data
y g
(e.g., persons, locations,
events, buildings)
– Structure of data on Web
pages is made explicit
– Things described on Web
pages are named and get
URIs
– Links between things are
Typed Links
5959
Links between things are
made explicit and are typed
“Things”
• The Web of Data is envisioned as a global database
– consisting of objects and their descriptionsg j p
– objects are linked with each other
– with a high degree of object structure
– with explicit semantics for links and content
• Linked Data is about the use of Semantic Web technologies to
publish structured data on the Web and set links between data
sources.
6060
Figure from C. Bizer

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Facts:
• 295 data sets
• Over 31 billion triples
• Over 504 billion RDF links between data
sources
6161
Figure from http://www4.wiwiss.fu-berlin.de/lodcloud/state/, September 2011
• Linked Open Data can be seen as a global data integration platform
– Heterogeneous data items from different data sets are linked to each other following the
Linked Data principlesLinked Data principles
– Widely deployed vocabularies (e.g. FOAF) provide the predicates to specify links between
data items
• Data integration with LOD requires:
1. Access to Linked Data
• HTTP, SPARQL endpoints, RDF dumps
• Crawling and caching
2. Normalize vocabularies – data sets that overlap in content use different vocabularies
• Use schema mapping techniques based on rules (e.g. RIF, SWRL) or query languages (e.g. SPARQL
Construct, etc.)
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3. Resolve identifies – data sets that overlap in content use different URIs for the same real
world entities
• Use manual merging or approaches such as SILK (part of Linked Data Integration Framework) or
LIMES
4. Filter data
• Use SIVE ((part of Linked Data Integration Framework)
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See: http://www4.wiwiss.fu-berlin.de/bizer/ldif/

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• Use LOD to integrate and lookup data
about
– places and routes
f– time-tables for public transport
– hiking trails
– ski slopes
– points-of-interest
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• Open Streetmap
• Google Places
• Databases of government
– TIRIS
– DVT
• Tourism & Ticketing association
• IVB (busses and trams)
• OEBB (trains)
• Ärztekammer
• Supermarket chains: listing of products
• Hofer and similar: weekly offers
• ASFINAG: Traffic/Congestion data
• Herold (yellow pages)
• Innsbruck Airport (travel times, airline
schedules)
• ZAMG (Weather)
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(y p g )
• City archive
• Museums/Zoo
• News sources like TT (Tyrol's major daily
newspaper)
• Statistik Austria
• University of Innsbruck (Curricula,
student statistics, study possibilities)
• IKB (electricity, water consumption)
• Entertainment facilities (Stadtcafe,
Cinema...)
• Special offers (Groupon)
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• Lots of public data sources
– public transportation
- Verkehrsverbund Tirol
- Innsbrucker Verkehrsbetriebe (IVB)
– public safety
- avalanche - Tyrolean Avalanche Warning Service
- traffic - Tyrolean Regional Hazard Warning Centre
- weather - Tyrolean Regional Hazard Warning Centre
– public health services
- regional hospitals/clinics
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- UMIT - health and life science university
– culture
- cultural events (Tourismusverband Innsbruck, etc.)
- http://www.tirol.gv.at/kunst-kultur/
– and many more …
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• External content from web site, audio streams, videos
and mobile devices
• New touristic services can be provided based on content
but requires first:
– Content annotation
– Linking to content
– Enhanced content consumption and delivery
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Feature 3: Semantic Search
Solving the Search Problem: Increase visibility of contents!
Repository
Touristic Ontology
TISs
CMSs
Semantic Search
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TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Solving the Search Problem: Increase visibility of contents!
• Providing the contents to be searchable is only one step to increase their visibility
A h i d t id l f t t id th t l t d f l h• A search engines need to consider several factors to provide the most relevant and useful search
queries, including:
• Intent of the search  the results should not based on the specific words used in the query,
but more on the intent of the search
• Variations of words  should consider tenses, plural, singular, etc.
• Synonyms  give same results on any synonyms of the word in query
• Generalized and specialized queries  should be able to set relations between generalized
and specialized queries
• Concept matching  should understand the broad concept of the query
• Natural language queries  should be able to process the natural language used by the
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g g q p g g y
users
• Location of search  should be able to provide results based on
the current location of the search

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• Semantic search is designed to improve the traditional web searching by improving the search
accuracy though the “meaning” of the documents made available for search.
• “Meaning” is established through a semantic model which essentially captures interrelationships• Meaning is established through a semantic model, which essentially captures interrelationships
between syntactic elements and their interpretations.
• The semantic models allow for a more precise matching of queries against the documents.
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• Typically, semantic search engines rely on certain ontology structures which are built from
concepts, properties, constraints and possibly axioms of the documents.
• Searching methodologies: RDF path traversal, keyword to concept
mapping, graph patterns, logics.
Example: Increase the visibility of contents on Mobile
Devices
• Typically, the documents are grouped into several main
categories and sub-categories as shown at the picture.
• The sub-categories are dynamically shown according to
the selection of their main category
• On desktop or personal computer, there is no problem
to navigate to those sub-categories, ...
• but on the mobile devices (with limited resources), the
sub-categories will be inaccessible  decrease the
visibility
Th f ti d l i i d t t
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• Therefore, a semantic model is required to represent
those super and sub-categories relationships, such that
performing a search to a main category will also include
its sub-categories
•  all documents will be visible to the search engine
including on the mobile devices

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Feature 4:
Semantic Alignment of Data and Content
Solving the Data and Content
Alignment Problem:
I th i t !
Semantic Alignment
Increase the conversion rate!
Repository
Touristic Ontology
TISs
CMSs
Semantic Search
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TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Feature 4:
Solving the Data and Content Alignment Problem: Increase the conversion rate!
• Achieving visibility through high-quality content, proper dissemination, interaction and
engagement with customers are essential corner stones in successful eTourismusengagement with customers are essential corner stones in successful eTourismus.
• However, what matters in the achieving booking volume in terms of numbers of bookings,
achieved pricing, and required commission fees.
• Achieving a high conversation rate in relation to successful contents is what matters.
• In consequence, it is essential to align dissemination and communication of content with bookable
touristic service offers, i.e., with data related to available booking offers.
• How can this be achieved in a scalable fashion with steadily changing content and context it is
presented?
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Feature 4:
• Semantic alignment is the automated process of determining correspondences between different
data sources based on a semantic analysis of their relationships.
• A set of correspondences is also called an alignment• A set of correspondences is also called an alignment.
• Natural language analysis and matchmaking of semantic annotations can be used to dynamically
establish such correspondences on the fly.
• Based on this different content and data sources of a related topic can be grouped and
disseminated together automatically.
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Feature 4:
Show together for: “I want to skii in Mayerhofen”
• Cool content with … booking data and service
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• Based on corresponding ontological characterization of both.

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Feature 5: Semantic Annotations
Provide SEO beyond
keywords and link farms:
S ti A t ti
Semantic Alignment
Repository
Touristic Ontology
TISs
CMSs
Semantic Search
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TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
What are Semantic Annotations?
Innsbruck is the capital city of the federal state of Tyrol (Tirol),
located at 47°16′N 11°23′E.
The name of the Place It is contained in another Place -> Tyrol
In DeutschIn English
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Refers to Geo coordinates
Talks about a Place
In DeutschIn English

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Which is the added value of Semantic Annotations?
annotate
Search Engines
Read and index
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Which is the added value of Semantic Annotations?
d t dSearch engines understand the content of the pages.
Called “rich snippet” by Google
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“These rich snippets help users recognize when your site is relevant to their search, and may
result in more clicks to your pages.” [4]
pp y g

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How do we create Semantic Annotations?
We need:We need:
• A technical way to add them
• A vocabulary with terms about the concept that we want to annotate
Vocabulary created by
Bing, Google, Yahoo!, Yandex
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to support the semantic annotations
and speak the same language with the
developers.
How could a hotel be semantically annotated?
Term Value
Name Grand Hotel Europa
Image http://www.innsbruck.info/[...]e.jpg
Logo http://www.innsbruck.info/[...]2.JPG
Address Südtiroler Platz 2, Innsbruck, 6020, AT
telephone +43 512 59 31
Fax number +43 512 58 78 00
Email info@grandhoteleuropa at
80
Email info@grandhoteleuropa.at
URL http://www.grandhoteleuropa.at/
Description The Grand Hotel Europa combines two worlds[...]
Price range $$$
Payment accepted available credit cards, cash payment

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41
What categories of concepts do exist in schema.org?
For example:For example:
• …
• Event
• Organization, Hotel, Restaurant
• Offer
• Place
• Product
81
• Review, Rating
• …
Summary
In TITi when we talk about Semantic Annotations we imply the usage of the• In TITi when we talk about Semantic Annotations we imply the usage of the
schema.org vocabulary.
• The architecture could be extended to support more vocabularies (e.g.
the Accommodation Ontology).
• The content is annotated automatically.
82

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42
Feature 6: Multi-channel Communication
Read and write in hundred
thousands of channels:
S l bl M lti h l
Semantic Alignment
Scalable Multi-channel
communication
Repository
Touristic Ontology
TISs
CMSs
Semantic Search
Web pages
Social Media
Channels
Mobile
Ch l
83
TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Channels
84

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43
Platform types
• Static Broadcasting• Static Broadcasting
• Dynamic Broadcasting
• Sharing
• Collaboration
• Group Communication
• Semantic-based Dissemination
85
Static Broadcasting
Homepages / Static Websites
Homepage Example
Static Website Example
Entry in Wikipedia for
Hotel Goldener Adler
86
Although created through a collaborative process,
Wiki websites can be considered static forms of
online broadcasting as the information contained in
them remains the same for long periods of time.

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44
Dynamic Broadcasting
• Small piece of content that is dependent upon constraints such as time andp p p
location.
• With Web 2.0 technologies have created dedicated means for publishing
streams and interacting with content generated by users.
• Examples of platforms types (organized considering first the length of message
and second – the level of interactivity):
N F d RSS
87
• News Feeds: e.g. RSS
• Newsletters
• Email / Email lists
• Microblogs: e.g. Twitter, Tumblr
• Blogs: e.g. Blogger, BuzzFeed
• Social networks: e.g. Facebook, Google+, LinkedIn, Xing
• Chat and instant messaging applications: e.g. Skype, Talk, Meebo.
Sharing
• There are a large number of Web 2.0 websites that support the sharing ofg pp g
information items such as: bookmarks, images, slides, and videos, etc.
• Provided by hosting services (images, videos, slides are stored on a server).
• Can use specialized applications (see below) of features of other platforms and
services (e.g. share photos through Facebook).
88
• Examples:
• Picture sharing: e.g. Flickr, Instagram, Picasa, Pinterest
• Slide sharing: e.g. Slideshare, MyPlick, Slideboom, Prezi
• Video sharing: e.g. YouTube, Vimeo, Videolectures
• Social Bookmark sites: e.g. Delicious, Digg, StumbleUpon
• Social News websites: e.g. Reddit.

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45
Collaboration
• Collaborative software helps to facilitate action-oriented teams working togetherp g g
over geographic distances, and by providing tools that aid communication,
collaboration and the process of problem solving.
• Examples:
• Wikis: e.g. Wikipedia.
• Collaborative tagging: adding metadata to shared content, e.g. Delicious.
• Document & Application collaboration:
89
e.g. Google Docs, EtherPad.
Group Communication
• Platforms for sharing and exchanging information but also to collecting feedbackg g g g
or discussing certain issues.
• Examples:
• Social networks: e.g. Facebook, Google+, My Space, Xing, LinkedIn.
• Internet forums: e.g. Quora, Ask.com.
• Online discussion groups: e.g. Google Groups, Facebook Groups, Yahoo!
Groups, Meetup, GroupSpaces, Windows Live Groups.
90

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46
Semantic-based Dissemination
• Scope: add machine-processable semantics to the information
 Search and aggregation engines can provide much better service in finding andgg g g p g
retrieving information.
• Search Engine Optimization
• Are potential customers finding your web site?
• Is it possible that potential customers might not be aware that your site exists?
• Do your targeted search terms have high search engine rankings?
• Does your website attract a large number of daily visitors?
Semantic Search:
91
• Semantic Search:
• Semantic search tries to understand the searcher’s intent and meaning of the query instead of
parsing the keywords like a dictionary.
Semantic-based Dissemination
• A (Semantic Web) vocabulary can be considered as a special form of (usually light-weight)
t l ti l l ll ti f URI ith ( ll i f ll ) d ib dontology, or sometimes also merely as a collection of URIs with an (usually informally) described
meaning*.
• URI = uniform resource identifier
• Semantic vocabularies include: Schema.org, FOAF, Good Relations, Dublin Core.
• Format is an explicit set of requirements to be satisfied by a material, product, or service.
• The most known examples are RDF and OWL.
• Implementation realization of an application, plan, idea, model,
Format e.g.
RDFa
92
p pp , p , , ,
or design
• Semantic repositories.
Implementation
e.g. OWLIM
Vocabulary e.g. foaf
* http://semanticweb.org/wiki/Ontology

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47
Semantic-based Dissemination: Formats
<div xmlns:dc=http://purl.org/dc/elements/1.1/
about="http://www example com/books/globaltourism">
• HTML Meta Elements
about= http://www.example.com/books/globaltourism >
<span property="dc:title">Global Tourism</span>
<span property="dc:creator">William Theonbald</span>
<span property="dc:date">2004‐10‐01</span>
</div>
• RDFa
• OWL (OWL-Lite, OWL-DL,
OWL-Full, OWL2)
• RIF
• Microformats
<ul class="vcard">
li l "f " J D /li
RDFa usage example
93
• Microformats
• Microdata
• RDF
• SPARQL
<li class="fn">Joe Doe</li>
<li class="org">The Example Company</li>
<li class="tel">604‐555‐1234</li>
<li><a class="url“
href="http://example.com/">http://example.com/</a></li>
</ul>
Microformats usage example
Semantic-based Dissemination: Vocabularies
• Schema.org
• Schemas for a large number of concepts and domains, such as creative works (e.g.
movies, music, TV, shows), places, products, organizations, lodging businesses, etc.
• FOAF
• Uses RDF to describe the relationship people have to other “things” around them.
• FOAF permits intelligent agents to make sense of the thousands of connections people
have with each other, their jobs and the items important to their lives.
• Good Relations
94
• A lightweight ontology for annotating offerings and other aspects of e-commerce on the
Web.
• The only OWL DL ontology officially supported by both Google and Yahoo.
• Dublin Core
• A set of vocabulary terms used to describe a full range of web resources: video, images,
web pages etc. and physical resources such as books and objects like artworks.

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48
Channels:
• A channel is part of a platform specified by the type of information item it
can handle.
<Platform>::<InformationItemType>::<AccountUID>
– Facebook :: Event :: STI account
– Facebook :: Image :: STI account
– Facebook :: Link :: STI account
– Facebook :: Video :: STI account
– Google+ :: MicroBlogPost :: STI account
G l I STI t
95
– Google+ :: Image :: STI account
– Twitter :: Link :: STI account
– YouTube :: Video :: STI account
Challenges:
• Scalability
– The overwhelming amount of available communication channels cannot be easily
managed by content publishers
• Costs
– Social Media experts needed to handle communication channels
– Not feasible for hoteliers
• Domain personalization
96
– Adaptation, alignment and definition of the content for several channels
– Generic and automatic solutions can be personalized to any domain
• Bilateral communication
– Feedback and engagement
– Reputation management

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Web/Blog
Conte
Content
Semantic Alignment
Collect feedback
statistics
SocialWeb
Weaver
ent Extraction and Public
t/Channel mapping & ad
Repository
Touristic Ontology
Semantic Search
Semantic Alignment
97
Web 3.0/Mobile/
Other
cation Layer
aptation layer
Distribute content
Dynamic rules:
• Content is mapped & adapted to specific channels using business rules,pp p p g ,
defined by domain experts
• Domain ontologies applied to map concepts with channels in rules
• Publication can be scheduled
• Feedback is collected to improve the engagement and adapt dissemination
rules
when
There is a new Event ev
98
There is a new Event ev
then
Publish ev in Facebook channel
Publish ev in Drupal/News channel

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50
Feedback of Dissemination
Feedback collection:
Refers to the response of an a dience to a message or acti it• Refers to the response of an audience to a message or activity.
• Giving the audience a chance to provide feedback is crucial for maintaining an open
communication climate.
• Views and clicks
• Unary feedback
• Binary feedback
99
• Binary feedback
• Ratings
• Re-publication
• Comments:
Engagement process =
Infinite loop between the listening and responding steps, interweaving
publishing and listeningpublishing and listening
Listen  Analyze  Understand  Respond
In order to retrieve the desired information, several API calls have to be
performed at the dissemination channels:
• Fetching the amount of comments of a post
• Fetching all comment of a post
100
g p
• Publish a comment to a post

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51
Outlook
101STI Innsbruck
Feature 7: Additional Services
Additional services can be provided:
• Data analytics and swarm-based yield management
D i k i• Dynamic packaging
• Data Value chain
• Governmental reporting and governance
Semantic Alignment
Service Layer
102
Repository
Touristic Ontology
TISs
CMSs
TSPs TAs Tirol
Werbung
External Data (LOD)
and Content
Semantic Search
g
Web pages
Social Media
Channels
Mobile
Channels

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52
Data analytics and swarm-based yield
management
• “is the discovery and communication of meaningful patterns in data. It
relies on the simultaneous application of statistics, computer
f fprogramming and operations research to quantify performance.
Analytics often favors data visualization to communicate insight.”
http://en.wikipedia.org/wiki/Analytics
•Large amount of data are usually
process in order to uncover hidden
patterns, unknown correlations or other
useful information
103
•That information can provide:
– Advantage over competitors
– Business benefits
– Effective marketing and increased
revenue
management
• Typical problems data analytics is used to:
– Make predictionsp
– Understand systems
– Optimize functions
• Typical techniques used to implement
data analytics include:
– Classification
– Regression
– Clustering
104
Clustering
– Reinforcement learning

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53
management
From data to knowledge:
105
http://www.slideshare.net/renuccif/data-analytics-session-1-2013-27929321?from_search=1
management in Tourism
• Used for revenue management, capacity and network planning,
inventory management, dynamic pricing, last-minute reservations
• Enables better understanding of tourists’ needs and preferences,
which can be latter used to design a highly-customized sales and
service process to meet those needs
• Helps revealing and analyzing causal relationships that explain the
attractiveness of hotels or destinations and preference patterns of
106
attractiveness of hotels or destinations and preference patterns of
tourists

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54
Data analytics and swarm-based yield management
in Tourism
• Hotels are confronted with a multitude of online booking channels.
• Hotels should provide their available rooms and their rates to most if not
all of the channels to prevent not meeting their potential customers.
• In many channels, visibility is achieved through low prices.
– However, often channels also require constraints on the price offers in other channels.
• Some channels generate costs
without guaranteeing actual
income.
107
107
Data analytics and swarm-based yield management in
Tourism
• A multi-directional multi-channel approach also must rely on Swarm
intelligence.
• Observing in real time the reaction of customers and competitors is the
key to achieving on-line marketing.
• Adopting your offer and your price dynamically in response to the
behavior of your (on-line visible) environment becomes a key for
economic success http://en.wikipedia.org/wiki/Swarm_intelligence
• Many solutions to yield management are based on complex statistical
methods and complex domain assumptions on how variation of the
price can influence the amount of bookings of a service
108
price can influence the amount of bookings of a service.
108

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55
Data analytics and swarm-based yield management in
Tourism
• Swarm intelligence support is an essential feature of the yield
management.
• Yield management could be realized utilizing reputation and usage
values collected from
– different channels, as well as
– tourism information systems.
• Key is the data and content available in Titi!
109
109
Dynamic packaging
• Dynamic packages play a more and more important role in
tourism
• Tourists want to buy not only single services but service packages
• Most touristic platforms offer the possibility to book a single
service (e.g. hotel, flight, car, events) but few provide service
packages
• Very often in order to build interesting packages, services offered
110
y g p g ,
by different online platform needs to be combined

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56
Dynamic packaging
• Data and services from from
various providers can be
integrated for bookingg g
packages containing:
– Flight
– Hotels
– Restaurants
– Cultural and entertainment events
– Sightseeing
– Shops
111111
Dynamic packaging
• Service packaging involves:
– Understanding requirements and preferences
– Finding relevant services that can fulfill part of
the overall request
– Composing relevant services from various
sources in packages
– Having a single price for service packages
– Selecting and recommending the optimal
112
Selecting and recommending the optimal
service package
– Booking of the service package
– Realize player technology with our
architecture

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57
Data Value Chain
“Your data is worth more if you give it away.”
Commission Vice President
Neelie Kroes
113
Data Value chain
• A "DATA Value” based economy driven by the open
data strategy
• It will enable and foster best possible social and commercial added
value based on intelligent use, management and re-use of data
sources in Europe.
• This will lead to
– increased business intelligence and efficiency of private and public
sectors
114
sectors
– world class applications
– new business opportunities involving SMEs - (open) data friendly
policy and business environment
Based on Márta Nagy-Rothengass – Head of Unit, EC DG Information Society and Media
keynote talk “Leveraging the data potential in Europe” at EDF2012

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58
Data Value chain
• Non tangible assets (i.e. data) play a significant role in
creation of the economic value.
Data is nowadays more important than for example• Data is nowadays more important than for example
search or advertisement.
• The value of the data, its potential to be used to create
new products and services, is more important than the
data itself.
• New businesses can be built on the back of this data.
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• Data is an essential raw material for a wide range of new
information products and services.
• Facilitating re-use of this raw data will create jobs and
thus stimulate growth.
Data Value chain
• Open Data can be integrated into new products and services
• A whole new industry implementing services on top of large data sets is
emerging.
• So…Data Value Chain in the Tourism domain refers to…
Chain of activities performed to get profitable value of the Touristic data
through different phases.
116

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59
Data Value Chain
To exploit data value chain, TiTi offers:
Service Layer
• Integrated and uniform data layer
th t b d f diff t
• To export EDF (Einheitliches
Daten Format), OTDS
Repository
Touristic Ontology
Semantic Search
Semantic Alignment
that can be used for different
purposes in the tourism domain.
• Hub functionalities: allows to
repeat through all its output
channels an input data.
(Open Travel Data
Standard).
• Semantic infrastructure to
be used under a multi-
purpose service layer for
different touristic activities.
Which can be used…
117
-To promote coordination between different players  Coordination of services
E.g Travel agency + hotel+ Tour+ Bus services
Data Value Chain
Example in the Touristic area: Tourism Association
EDF
Resell and
package
touristic offers
Semantic Search
Semantic Alignment
Service Layer
EDF
Global Typs
ODTS
118
Repository
Touristic Ontology
Hotel
Rooms availability

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60
Governmental reporting and governance
• Governments need data from strategic sectors
– To measure performance, analyze risks, foresee trends…
– To inform strategic decisions
– For policy making
• Opening up data will give allow businesses to
– comply with regulations
– sell data-related services to government
– improve collaboration with local governments
– provide insight into investment and innovation
119
– support public-private partnerships
• Example in the touristic sector
– Data from bookings can be used to analyze tourism trends, make new policies, target
specific groups…
Based on “Open Data: Driving growth, ingenuity and innovation”, Deloitte 2012. Available online at
http://www.deloitte.com/assets/dcom-unitedkingdom/local%20assets/documents/market%20insights/deloitte%20analytics/uk-
insights-deloitte-analytics-open-data-june-2012.pdf
Summary
120STI Innsbruck

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61
The Problem
• The number of people who plan and book their travel activities
online is steadily growing.
• This confronts touristic service providers with many new challenges
– To attract potential customers they have to be present in a multitude of channels
in various formats and adaptions.
– The content in the various channels should be of high quality and originality.
– There should be multiple online booking opportunities for their offers (fluid
booking).
– Multi channel yield management and revenue optimization is necessary.
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HOTEL
RECEPTION
The overall picture
Service Layer
Repository
Touristic Ontology
TISs
CMSs
Semantic Search
Semantic Alignment
Web pages
Social Media
Channels
Mobile
Channels
122
TSPs TAs Tirol
Werbung
External Data (LOD)
and Content

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62
Key elements of our solution
The Kernel:
•A repository for structured data management and digital preservation.
•An Ontology to mediate between different data models•An Ontology to mediate between different data models.
•The import from Touristic Information Systems (TISs).
•The import and export from Content Management Systems (CMS).
Touristic Ontology
TISs
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Repository
Touristic Ontology
CMSs
Key elements of our solution
The Features:
1.The sharing of content and data between different touristic agents.
2 The enrichment with external content and data2.The enrichment with external content and data.
3.Increased visibility of content using Semantic Search.
4.Increased conversion rate using Semantic Alignment of Data and
Content.
5.SEO beyond keywords and link farms using Semantic Annotations.
6.Scalable Multi-channels communication.
7.Additional Services:
D t l ti d b d i ld t
124
– Data analytics and swarm-based yield management
– Dynamic packaging
– Data Value chain
– Governmental reporting and governance

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63
Questions?
Questions?
?
125

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