Tourist Knowledge Graph Creation to Automating Travel Bookings

Tourist Knowledge
Graph Generation to
Automating Personal
Travel Bookings
Innovation Development Area
Giuseppe Rizzo

Tourist Knowledge Graph Generation to Automating Personal Travel Bookings @ Milano-Bicocca
Turning
data into
actionable
insights
INNODEV
http://innodev.ismb.it
2017-05-02
@giusepperizzo
2
RECIPE OF TODAY’S TALK
The alchemy toward automation
- data modeling
- data integration
- personalization
Using ML and AI
The Alchemist Discovering Phosphorus -- Joseph Wright of Derby
TODAY’S RECIPE:
A “journey” from data to
personalized automation
Going through:
- Knowledge Graph
- Big Data
- Data Integration
- Personalization

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
3
KNOWLEDGE GRAPH
http://searchengineland.com/laymans-visual-guide-googles-knowledge-graph-search-api-241935

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
4
dbo:Person
dbo:Agent
is a
yago:Artist109812338
is a
yago:Creator109614315
subClassOf
subClassOf
yago-res:wo
rdnet_artist_
109812338
equivalentClass
T-Box
A-Box
dbo:author

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
5
KEY FEATURE OF A
KNOWLEDGE GRAPH
It stores entity-relation-entity
data where the semantics of
entities and relations is explicit

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
6
A KNOWLEDGE GRAPH
ENABLES
Algorithms to efficiently work over
the semantics of data for a large
range of tasks such as retrieval,
extraction, classification,
clustering, ranking where
automation is put in the first place

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
7
NUMEROUS KNOWLEDGE
GRAPHS OUT THERE
http://lod-cloud.net
Some can be dumped
locally as the ones
pictured by lod-cloud,
some others are
technology assets of
popular products such
as Google Graph,
Facebook Graph and
can be accessed via
dedicated web
interfaces

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
8
THE BROADER DATA MOVEMENT
TALKS ABOUT 5Vs OUT THERE
The big
VALUE
VOLUME
VELOCITY
VERACITY VARIETY

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
9
TOURIST AND CULTURAL
DATA DELUGE

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
SILOED,
UNCONNECTED,
UNSTRUCTURED,
WEAKLY-STRUCTURED,
HIDDEN SEMANTICS
10

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
11
Automating the creation of city
knowledge graphs composed of
better linked tourist and cultural data
to answer complex queries
AMBITION #1:
BETTER DATA 4 TRAVELER

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
12
collect + link
tourist and cultural data such as
events, places, user-generated data
(reviews, ratings), transportation means

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
13
Integration
DATA INTEGRATION

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
14
entities semantically connected
Rizzo G. et al. (2015) The 3cixty Knowledge Base for Expo Milano 2015: Enabling Visitors to
Explore the City. In: 8th International Conference on Knowledge Capture (K-CAP), Best P&D

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
16
DATA SAMPLING

Turning
data into
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insights
INNODEV
2017-05-02
@giusepperizzo
17
Sample
cell
by cell

Turning
data into
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insights
INNODEV
2017-05-02
@giusepperizzo
18
Algorithm implements an ensemble
entity matching classifier, where the
ensemble of weak-classifiers is
performed via stacking
ENTITY MATCHING
Palumbo E., Rizzo G., Troncy R. (2017) STEM: Stacked Threshold-based Entity Matching for
Knowledge Base Generation. Submitted to Semantic Web Journal
Rizzo G. et al. (2015) 3cixty@Expo Milano 2015: Enabling Visitors to Explore a Smart City. In: 14th
International Semantic Web Conference (ISWC), SWC Winner
Troncy R, Rizzo G. et al. (2017) 3cixty: Building Comprehensive Knowledge Bases For City
Exploration. Submitted to Journal of Web Semantics

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
19
name address capacity ...
Pala
Alpitour
C.so
Sebastopoli
20000 spettatori
max
PalaIsozaki Sebastopoli, 123 20200
Pala Alpitour
PalaIsozaki

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
20
d(name)
Pala Alpitour C.so Sebastopoli 20000
spettatori max
PalaIsozaki Sebastopoli, 123 20200
d(address) d(capacity) ...
s(name) s(address) s(capacity) ...
p1
=p(Match|s1
) p2
=p(Match|s2
) p3
=p(Match|s3
)

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
21
BAYESIAN
THRESHOLD-BASED
CLASSIFIER
f(e1
, e2
, t) : if p > T then e1
= e2
p1
p2
… pN
p1
p2
.. pN
+ (1-p1
)(1-p2
)..(1-pN
)
p =

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
22
ENSEMBLE OF ENTITY
MATCHING CLASSIFIER
f1
(e1
, e2
, t1
)
f2
(e1
, e2
, t2
)
fn
(e1
, e2
, tn
)
...
SVM Classifier
GS of manually
annotated matches/no
matches
Match/
No Match
Palumbo E., Rizzo G., Troncy R. (2016) An Ensemble Approach to Financial Entity Matching for the FEIII 2016
Challenge. In: (SIGMOD'16), Financial Entity Identification and Information Integration Challenge, San
Francisco, US.

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
23
MANUAL ANNOTATIONS OF
PLACE-TYPE ENTITIES

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
24
MANUAL ANNOTATIONS OF
EVENT-TYPE ENTITIES

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
25
2014 - 2016 EIT Digital
funded, action line Digital
Cities
https://www.3cixty.com

Will be looking for an Indian restaurant
reachable in 10 minutes on foot from
the Madison Square Garden

Multitude of sources
with unconnected
and heterogeneous
data

Rizzo G., Troncy R., Corcho O., Jameson A., Plu J., Ballesteros Hermida J.C., Assaf A., Barbu C.,
Spirescu A., Kuhn K., Celino I., Agarwal R., Nguyen C.K., Pathak A., Scanu C., Valla M., Haaker T.,
Verga E.S., Rossi M., Redondo Garcia J.L. (2015) 3cixty@Expo Milano 2015: Enabling Visitors to
Explore a Smart City. In: 14th International Semantic Web Conference (ISWC'15), Semantic
Web Challenge
3cixty @ Milano Expo 2015

4Vs: 20 DIFFERENT DATA SOURCES
Variety
Veracity
and
authority
Velocity
Volume

https://www.youtube.com/watch?v=K6_ylq1ufH8
3cixty: Knowledge Base and
Applications for Milan and Expo 2015

https://www.3cixty.com
Other validated pilots:
Nice, London, Singapore, Madeira,
Las Palmas, Amsterdam

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
AMBITION #2:
BETTER OFFERING 4 TRAVELER
37
Automating the creation of
personalized travel packages

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
38
package = flights + hotel +
restaurants + events + ...
= a new personalized
in-destination experience

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
39
CONTEXTUAL AND EPISODIC DATA

Turning
data into
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insights
INNODEV
2017-05-02
@giusepperizzo
40
contextual, i.e.
encyclopedic
KNOWLEDGE GRAPH TYPES
episodic such as
transactions, user
purchases, user likes
?

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
41
EPISODIC DATA OUT THERE
re.
SILOED,
UNCONNECTED,
UNSTRUCTURED,
WEAKLY-STRUCTURED,
HIDDEN SEMANTICS
LOCKED
+
privacy sensitive data

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
42
TRAVELER DATA
i.e. Giuseppe, flight number, departure, destination,
….
materialized in a Knowledge Graph
complementing the contextual
knowledge of local offerings such as
restaurants, events

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
43
Algorithm implements a machine
learning recommender blending
tourist and personal data
RECOMMENDATION
Palumbo E., Rizzo G., Troncy R (2017) Learning User-Item Relatedness from Knowledge Graphs for
Top-N Item Recommendation. Submitted to 11th ACM Recommender Systems Conference (RecSys)

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
44
CONTEXTUAL AND EPISODIC
KNOWLEDGE GRAPH
u1
u2
u3
item1
item2item3
item4
item5
item6
item7
● User
● User-Item
(purchase)
● User-Item (like)
● Item
● Item-Item
● User-User
(relation)

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
45
KNOWLEDGE
GRAPH
USER-ITEM
RELATEDNES
S
LEARN-TO-
RANK ITEMS
3-STEP PROCESS
create compute perform

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
46
TURNING ENTITIES AND
RELATIONS TO FEATURE VECTORS
u1
i1
i6
purchase
purchase f(u1, i1) = rel1(u1, i1)
like f(u1, i7) = rel3(u1, i7)
i7
purchase
like
u1
u1
...
Graph
u2
Relatedness
scores
Feature
vectors
f(u1, i6) = rel2(u1, i6) u1
#i1
#i2
#i7

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
47
purchase
like
u1
u1
...
u1
#i1
#i2
#i7
Lambda
MART
purchase
like
u1
u1
...
u1
#i2
#i7
#i1
new
ranking
RECOMMENDATION AS
LEARNING TO RANK TASK
Feature
vectors

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
48
PERSONALIZATION DIMENSIONS
history Episodic, what I’ve already bought
collaborative Colleagues who I know have bought
willingness to pay Budget availability
interests Nature, culture, sports, nightlife
needs Small kids, driving licence, allergies, mobility restrictions,
animals
time When doing things, time pressure, aversion to queuing,
advance vs on site payment, life pace
travel preferences Wifi availability, meal likings, spas, noise aversion
payment method Credit card, phone, bitcoin, paypal, cash
...

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
49
2017 EIT Digital funded,
action line Digital Cities
http://pas-time.org

Frederic, CEO of a company, is going to
give a pitch to investors in SF.
He flies often over there, where he likes
staying in a comfortable house rented via
airbnb next to the sea to enjoying surfing
during his free spots. While staying, he likes
watching the Golden State Warriors once

Frederic
TRADITIONAL LINKAGE
Flights
Commuting
Hotel
(Online)
Travel
Agencies
contacts
formulate
submit for
acceptance/change

2) There is
a bunch of
accommod
ations
you’d like
the most
Frederic PasTime
1) These
flights will
suit you the
best, pick
one
3) You’ll enjoy
these
restaurants
as they
prepares your
favourite dish
and are next
to ...
4) Golden
State Warriors
are playing in
town. Would
you like to
attend?
package
“NETFLIX” FOR TRAVELERS
generates

PasTime for French Regional Committee of
Tourism
Package Persona-A
Package Persona-B
Package Persona-C
Tourist Offices
Persona-A
Persona-B
Persona-C
Tourist Personas
Tourist offices distribute and record likes of tourists. The
recommendation algorithm will exploit those likes in a learning fashion
http://www.freepik.com/free-vector/businesswo
man-working-at-the-office_799834.htm

http://www.pas-time.org
Further investigated pilots:
French RCT, Amsterdam and Las
Palmas
On both
- Per-group packaging
- Per-traveler packaging

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
55
RECAP: a “journey” from data to
personalized automation
Going through:
- Knowledge Graph
- Big Data
- Data Integration
- Personalization
Any questions?
https://www.slideshare.net/giusepperizzo

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
56
ACKNOWLEDGE
PROJECTS
3cixty: An Innovative Platform for Building
City Apps
EIT Digital, Digital Cities (2014-2016)
PasTime: Packaging Personalised Tourist
Experiences
EIT Digital, Digital Cities (2017)

Turning
data into
actionable
insights
INNODEV
2017-05-02
@giusepperizzo
57
ACKNOWLEDGE
AMAZING TEAM
Lots of contributions along the way: Raphael T., a mentor and
a great colleague; Enrico P. and Julien P., researchers
currently doing a PhD: “there is always something new to learn
from young padawans”.
Enrico F., Michele O., all ISMB BMPI jedis, all students I’ve
supervised, and all colleagues I’ve been working with

Tourist Knowledge Graph Creation to Automating Travel Bookings

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