Integrating Semantic Web in the Real World: A Journey between Two Cities

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Integrating
Semantic
Web
in
the

Real
World:

A
journey
between
two
cities
Juan
F.
Sequeda
Keynote
at
The
9th
International
Conference
on
Knowledge
Capture
(K-‐CAP2017)
December
6,
2017

Take
Away
Message
• Reflect
on
our
journey
to
commercialize
semantic

web
technology
to
address
data
integration
and

business
intelligence
needs.
Question
• Why
is
it
so
hard
to
deploy
Semantic
Web
technologies
in

the
real
world?
• Answer:
1. History
2. Knowledge
Engineer
3. Ontology/mapping
engineering

Data
Logic
RDBMS
Semantic

Web
Workshop
on

Logic
and
Data
Bases,

Toulouse
1977
Gallaire,
Nicolas
&

Minker
SQL99
Recursion
KL-‐ONE
Description

Logic RDF OWL
Views Triggers
Semantic
Networks
Japanese
5th
Generation
Project
MCC
Austin,
TX
Today1970s
Relational

Algebra
Workshops
on
Expert
Systems
Deductive
Databases
KRDB
1980s 1990s 2000s
Let’s
put

in
Today’s
Context
4
History

Where
we
started
in
2007…
What
is
the
relationship
between
Relational
Model
Table
Definition
ConstraintsS
Q
L
Relational
Databases
RDF
RDFS
OWL
S
P
A
R
Q
L
TIME
Triggers Rules
Semantic
Web
Sequeda
et
al.
SQL
Databases
are
a
Moving
Target.
W3C
Workshop
on
RDF
Access
on
RDB.
2007
Progra
mmer
type
2 “Bob”
name
ITEmployee
subClassOf
SELECT
?s
?n
{
?s
type
ITEmployee.
?s
name
?n
}
Literal
name

10
years
ago
• D2R
(Map,Q,Server),
Virtuoso
RDF
Views,
SquirrelRDF,
R2D2,

Relational.OWL,
DB2OWL,
R2O,
Triplify,
Dartgrid,
RDBToOnto,

METAmorphoses,…

“Comparing the overall performance […] of the fastest rewriter with
the fastest relational database shows an overhead for query
rewriting of 106%. This is an indicator that there is still room for
improvingthe rewritingalgorithms”.
[Bizer and Schultz. BerlinSPARQL Benchmark 2009]
Current
rdb2rdf
systems
are
not
capable
of
providing
the
query

execution
performance
required
[...]
it
is
likely
that
with
more
work

on
query
translation,
suitable
mechanisms
for
translating
queries

could
be
developed.
These
mechanisms
should
focus
on
exploiting

the
underlying
database
system’s
capabilities
to
optimize
queries

and
process
large
quantities
of
structure
data
[Gray
et
al.
2009]
Some
Issues
early
on

https://sourceforge.net/p/d2rq-‐map/mailman/message/28055191/
Sept
2011

Why
was
this
happening
if
…
ISWC
2008

(1)
Relational
Databases
à Semantic
Web:
Direct
Mapping
10
I
R,
Σ

• Formalization
in
Datalog
• Databases
with
NULLs
• Correctness
of
a
Direct
Mapping
• Information
Preservation
• Query
Preservation
• Monotonicity
• Semantics
Preservation
DM(R,
Σ,
I)
• No
monotone
direct

mapping
is
semantics

preserving
On
Directly
Mapping
Relational
Databases
to
RDF
and
OWL.
Sequeda,
Arenas,
Miranker.
WWW
2012

(2)Relational
Databases
ß Semantic
Web
:
Ultrawrap
11
Relational

Database
Tripleview
Mapping
Compiler
SPARQL
to
SQL

on
Views
SQL
Optimizer
Mapping
as

Views
Direct
Mapping
Results
Ultrawrap:
SPARQL
Execution
on
Relational
Data.
Sequeda
&
Miranker.
J.
Web
Semantics
2013
• Chakravarthy,
Grant
and
Minker.
Logic-‐
Based
Approach
to
Semantic
Query

Optimization.
TODS1990
• Cheng
et
al.
(1990)
Implementation
of

Two
Semantic
Query
Optimization

Techniques
in
DB2
Universal

Database.
VLDB1999
• Semantic
Query
Optimization
• Detection
of
Unsatisfiable

Conditions
• Self
Join
Elimination
• Commercial
RDB H

(3)Relational
Databases
ßàSemantic
Web:
UltrawrapOBDA
12
Relational

Database
Tripleview
Mapping
Compiler
SPARQL
to
SQL

on
Views
SQL
Optimizer
Mapping
as

Views
Saturated
Mapping
Results
Mapping
OBDA:
Query
Rewriting
or
Materialization?
In
practice,
Both! Sequeda,
Arenas,
Miranker.
ISWC
2014
(Best
Paper)
OWL
SQL
EL
RL
QL
DL
• Gallaire et
al.
Logic
and
Databases:
A
Deductive

Approach.
ACM
Survey
1984
• Chaudhuri et
al.
Optimizing
queries
with

materialized
views.
ICDE95
Harinarayanet
al.
Implementing
Data
Cubes

Efficiently.
SIGMOD96
• Halevy.
Answering
queries
using
views:
A
survey.

VLDBJ2001
• Mami &
Bellahsene.
A
Survey
of
View
Selection

Methods.
SIGMOD
Record
2012
• Commercial
RDB
• Answering
Queries

using
Views
• Rewriting
using

materialized
views
• Recursion
in
SQL
H

HOW and
to
what
EXTENT can
RDB
be
integrated
with
the
SW?
13
RDB
can
be
automatically
directly

mapped
to
RDF
and
OWL
RDB
can
evaluate
and
optimize

SPARQL
1.0
queries
RDB
can
act
as
a
reasoner
for

Ontologies
with
inheritance
and

transitivity
Direct
Mappings
can
be
Monotone,
Information

Preserving
and
Query
Preserving.
Monotonicity

is
an
obstacle
for
Semantics
Preservation
Existing
Semantic
Query
Optimization
in

commercial
RDBMS
Saturated
Mappings,

Query
rewriting
using
Materialized
Views
and

Recursion

Where
did
our
research
journey
take
us?
14
Oracle
SQL

Server
Postgres
MySQL
IBM
DB2
Enterprise
Knowledge
Graph
• Sheth&
Larson.
Federated
database
systems
for
managing
distributed,
heterogeneous,
and
autonomous
databases.
ACM
Survey.
1990
• Carnot92,
Infosleuth92,
SIMS93,
Information
Manifold96,
Lore96, TSIMMIS97,
Kleisli99,
Nimble01,
Clio01,
Sphinx04
H

Our
Journey
15
https://constituteproject.org/

SEMANTIC
CITY NON-‐SEMANTIC
CITY

IT Biz
Total
net

sales
of

all
Orders

today
Reports
Data
Integration
and
Business
Intelligence
17

Business
Question
How
many
orders
were
placed
in
November
2017?
317,595
317,124
316,899
Billing
Shipping
E-‐Commerce
18

What
do
you
mean
by
…
What
is
an
Order?
When
a
user

clicks
“Order”
on

the
website
When
the

customer
has

received
the

product
When
it
comes

out
of
the
billing

system
and
the
CC

has
been
charged
Billing
Shipping
E-‐Commerce
19

IT
Biz
Total
net

sales
of

all
Orders

today
Data
Architect
SELECT

..

FROM
…
csv csv
csv
MS
Access
T=1
T=2T=3
XLS
• Did
the
Biz
User

communicate
the
correct

message
to
IT?

• Did
IT
understand
correctly

what
the
Biz
User
wanted?

• Did
IT
deliver
the

correct/precise
results?

Reports
XLS
XLS
Status
Quo
1
20
https://www.wsj.com/articles/finance-‐pros-‐say-‐youll-‐have-‐to-‐pry-‐excel-‐out-‐of-‐their-‐cold-‐dead-‐hands-‐1512060948

Enterprise
Data
Warehouse
IT Biz
Reports
Time
and
$
Total
net

sales
of

all
Orders

today
ETL
ETL
ETL
Total
net

sales
of
all

Orders

today
with

FX
Status
Quo
2
Data
Architect
21

What
is
actually
going
on
here
• Subject
Matter
Expert

knows
the
business

domain
• Dialog
between
users

• Understand
the

Domainà ontology
• Find
where
it
is
in
the

data
à mappings
• Sound
familiar?

22
Giarratano&
Riley.
Expert
Systems:

Principles
and
Programming.
1989
H

Semantic
Web
can
help...right?
Who
creates
this?
Using
what
tools?
IT
IS
NOT
EASY!
HOWEVER

Chasm
between
the
two
Cities
24G.
Moore.
Crossing
the
Chasm.

SEMANTIC
CITY

Observation
1:
Boiling
the
Ocean
• Ontology
Engineering
– Traditional
ontology

engineering

methodologies

– Using
competency

questions

– Test
driven
development

– Ontology
design
patterns

– ...
• Mapping
Engineering
– Ontology

Matching/Alignment
– Schema

Matching/Alignment
25
“There
is
not
a
right
ontology.
But
a
useful”
-‐ F.
van
Harmelen
https://www.flickr.com/photos/eclogite/4950276577/

Observation
2:
Real
World
Schemas
are
Hard
26

Observation
3:
Real
World
Mappings
are
Hard
27
How
to
deal
with
NULLs
and
Duplicates
in
a
mapping?

Observation
4:
Tools
are
made
for
Semantic
City
28

So
what
is
the
solution?
• Create
tools
for
citizens
of
the
Non-‐Semantic
City

(?)

• Knowledge
Engineering
as
a
“transfer
process”
of

human
knowledge
to
a
KB
during
the
80s
did
not

succeed

• Assumption:
knowledge
exists,
just
has
to
be
collected
and

implemented
• Knowledge
was
obtained
by
interviewing
experts
on
how
they

solve
specific
tasks

• Feasible
for
small
prototypical
systems
• Failed
to
produce
large,
reliable
and
maintainable
knowledge

bases
29
Studer et
al.
Knowledge
Engineering:
Principles
and
methods.
Data
&
Know.
Engineering
1998
H

IT Biz
The
Resurrection
of
the
Knowledge
Engineer!
30
KE
Knowledge
Engineer
Data
Engineers
Domain
(Biz)
Experts
Business
&

Data

Modeling
Data
Access
“People
Person”“Geeky
Person”
D.
Michie.
Knowledge
Engineering.
Kybernetes 1973
H

The
Knowledge
Engineer
• Analyze
graph
structures
and
content
and

develop
new
semantic
representations.
• Make
decisions
and
provide
guidance
about

ontologies
and
semantic
representations.
• Write
code
to
gather,
process,
and
analyze

data
of
various
kinds.
• Work
with
researchers,
engineers,
and

linguists
to
develop
new
techniques
for

expansion,
improvement,
and
analysis
of
the

Knowledge
Graph.
https://careers.google.com/jobs#!t=jo&jid=/google/linguist-‐ontologist-‐google-‐knowledge-‐firebase-‐345-‐spear-‐st-‐san-‐francisco-‐ca-‐3182490028

Knowledge
Engineer
vs
Data
Scientist
32
IT BizKE
Knowledge
Engineer
Data
Engineers
Domain
(Biz)
Experts
DS
Data

Scientist
“Most
data
scientists
spend
only
20
percent
of
their
time

on
actual
data
analysis
and
80
percent
of
their
time
finding,

cleaning,
and
reorganizing
huge
amounts
of
data,
which
is

an
inefficient
data
strategy”
https://www.infoworld.com/article/3228245/data-‐science/the-‐80-‐20-‐data-‐science-‐dilemma.html

How
is
the
Knowledge
Engineer

empowered
in
order
to
be

successful?

Idea1:
Pay
as
you
go
Methodology
34
A
Pay-‐As-‐You-‐Go
Methodology
for
Ontology-‐Based
Data
Access.
Sequeda
&
Miranker.
IEEE
Internet
Computing
2017
-‐ Studer et
al.
Knowledge
Engineering:
Principles

and
methods.
Data
&
Know.
Engineering
1998
-‐ CommonKADS,
MIKE,
PROTÉGÉ,
VITAL,
EXPECT
Knowledge
Engineering
as
a
modeling
process
H

Idea
2:
Extract
mappings
from
Source
Queries
SELECT
o.orderid, o.orderdate,
o.ordertotal
- ot.finaltax
- CASE
WHEN o.currencyid in (‘USD’, ‘CAD’) THEN
o.shippingcost
ELSE o.shippingcost - ot.shippingtax
END AS netsales,
o.currencyid
FROM order o, ordertax ot
WHERE o.orderid = ordertax.orderid
AND o.statusid NOT IN (4, 5)

Idea
3:
Tools
for
the
Knowledge
Engineer
36

Bridging
the
Chasm
37
SEMANTIC
CITY
Knowledge
Engineer

Our
Vision
38

Thanks
• Daniel
Miranker
• Marcelo
Arenas
• Oscar
Corcho
• ..
And
many
more
• Daniel
Miranker
• Wayne
Heideman
• Will
Briggs
• Rick
Liao
• Bill
Rogers
• ...
And
many
more
39

Takeaway
Message
40
Juan
Sequeda,
Ph.D
Co-‐Founder
– Capsenta
juan@capsenta.com
@juansequeda
Sequeda
J.
Integrating
Relational
Databases
with
the
Semantic
Web.
IOS
Press.
2016
http://www.iospress.nl/book/integrating-‐relational-‐databases-‐with-‐the-‐semantic-‐web/
We
are
always
looking
for

smart
people
(and

Knowledge
Engineers!)
THANK
YOU!
Don’t
reinvent
the
wheel

Know
the
History
Read
pre-‐pdf
paper
Knowledge
Engineer
It’s
back

And
sexy
Ontology
and
Mapping

Engineering
challenges
New
Problems
Because
we
need
to
bridge
the
chasm
between
the
Semantic
and
Non-‐Semantic
Cities.

We
need
Knowledge
Engineers,
who
need
to
be
empowered
with
methodologies
and
tools.
Why
is
it
so
hard
to
deploy
Semantic
Web
technologies
in
the
real
world?

Integrating Semantic Web in the Real World: A Journey between Two Cities

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