SPARQL Query Verbalization for Explaining Semantic Search Engine Queries

KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association
1 Institute of Applied Informatics and Formal Description Metthods (AIFB), Karlsruhe, Germany
2 University of Southampton, United Kingdom
www.kit.edu
SPARQL Query Verbalization for Explaining Semantic
Search Engine Queries
Basil Ell,1 Andreas Harth,1 Elena Simperl2
11th Extended Semantic Web Conference 2014
28 May 2014, Anissaras/Heronissou, Crete, Greece

Institute of Applied Informatics and Formal Description
Metthods (AIFB)
2
Overview
Motivation
Anatomy of a query verbalization
Main ideas
Example
Evaluation
Conclusions
Basil Ell - SPARQL Query Verbalization for Explaining Semantic Search Engine
Queries

Metthods (AIFB)
3
Motivation (1/2)
Expert user
Triple store
SPARQL
Input interpretation
SPARQL query
generation
Casual user
Keywords
Question (NL/CNL)
Query results
Query results
Queries

Metthods (AIFB)
4
Motivation (2/2)
Casual user
What is the second highest
mountain?
Triple store
SELECT ?s WHERE {
?s rdf:type dbo:Mountain .
?s dbo:elevation ?elev .
} ORDER BY DESC(?elev) LIMIT 1
:Mount_Everest
Unless the user does not understand the generated query,
the user cannot trust the results.
SELECT ?s WHERE {
?s rdf:type dbo:Mountain .
?s dbo:elevation ?elev .
} ORDER BY DESC(?elev) LIMIT 1
The mountain that has the highest
elevation value:
What is the second
highest mountain?
Queries
Mount Everest
> Anatomy

Metthods (AIFB)
5
Anatomy of a query verbalization (1/4)
subject constraints requests
The country that has the
highest number of
languages
Show also, if available, its
English labels.
.
SELECT ?uri ?string WHERE {
?uri rdf:type onto:Country .
?uri dbo:language ?language .
OPTIONAL {
?uri rdfs:label ?string .
FILTER(lang(?string) = 'en')
}
} ORDER BY DESC (COUNT(?language)) LIMIT 1
Queries

Metthods (AIFB)
6
Caves that have numbers
of entrances > 3
Show also, if available, these
caves‘ English labels.
.
SELECT ?uri ?string WHERE {
?uri rdf:type dbo:Cave .
?uri dbo:numberOfEntrances ?entrance .
FILTER (?entrance > 3) .
OPTIONAL {
FILTER (lang(?string) = 'en')
}
}
Queries

Metthods (AIFB)
7
Distinct cities that have population urbans
> 2000000 or that have a
population > 2000000
Show also, if available, these
cities‘ English labels.
.
SELECT DISTINCT ?uri ?string WHERE {
?uri rdf:type onto:City.
{ ?uri prop:population ?population. }
UNION
{ ?uri prop:populationUrban ?population. }
FILTER (xsd:integer(?population) > 2000000) .
OPTIONAL {
FILTER(lang(?string)='en')
}
}
Queries

Metthods (AIFB)
8
subject constraints
a film that has the English name
"Batman Begins" and that is
starring a thing that has the
English label "Christian Bale"
?
ASK WHERE {
?film rdf:type onto:Film .
?film onto:starring ?actors .
?actors rdfs:label 'Christian Bale'@en .
?film foaf:name 'Batman Begins'@en
}
Queries
Is it true that
there is
> Main Idea

Metthods (AIFB)
9
Main idea
Decompose a query into independently verbalizable
messages (top-down approach)
Retrieve labels via URI look-up
Verbalize messages using templates
Assemble verbalized messages
Templates are mostly schema-agnostic
Templates depend on linguistic features of properties
Verbalizer is domain-independent
Schema-specific templates can be added
Lemon dictionaries could be used
Queries
> Example

Metthods (AIFB)
10
Example – SPARQL query
01 SELECT ?uri ?string ?p WHERE {
02 ?uri rdf:type :Person .
03 ?uri :birthPlace ?p .
04 ?uri :surname 'Elcar' .
05 { ?uri :givenName 'Dana'@en . } UNION {
06 ?uri :alias ?alias .
07 FILTER(regex(?alias, 'Dana')) .
08 }
09 OPTIONAL {
10 ?uri rdfs:label ?string .
11 FILTER(lang(?string)='en')
12 }
13 }
Queries
> Graph rep.

Metthods (AIFB)
11
Example query – graph representation
?uri
/Dana/
LANG=en
?var
?var
resource
filter
projection var
variable
Queries
UNION
rdf:type
:Person
?p
‘Elcar‘
rdfs:label
OPT
?string
‘Dana‘@en
?alias
M3
M6
> Message rep.

Metthods (AIFB)
12
Example query – message rep.
Queries
> Example verb.

Metthods (AIFB)
13
Example – Subject verbalization
Control variables
Subject template (excerpt) → Case: abcdef
→ Result: “People“
Due to depedencies:
36 cases instead of
2^6=64
( )
Queries
?uri
rdf:type
:Person
M3

Metthods (AIFB)
14
Example – Constraint verbalization (1/2)
Control variables
Classes of properties
Queries
schema-
independence,
based on POS

Metthods (AIFB)
15
Example – Constraint verbalization (2/2)
RV-Constraint template (class 1) (excerpt)
Property class: C1
→ Case: abcdeFghij
→ Result: „ that have a surname “Elcar““
Queries
?uri
‘Elcar‘

Metthods (AIFB)
16
Example – verbalization result
Queries
?uri
/Dana/
LANG=en
UNION
rdf:type
:Person
?p
‘Elcar‘
rdfs:label
OPT
?string
‘Dana‘@en
?alias
M3
M6

Metthods (AIFB)
17
Example – verbalization result
Queries
People
that have a surname "Elcar"
and
that have birth places
and
that have the English given name "Dana"
or
that have aliases that match the expression /Dana/
.
Show also, if available,
these people's English labels
and
these people's birth places
.
M3
M2
M1
M4
M5
M6
?p
> Evaluation

Metthods (AIFB)
18
Evaluation (1/2)
Comparative evaluation: Spartiqulation vs.
SPARQL2NL [Ngonga Ngomo et al., 2013]
(bottom-up approach)
6 evaluators, 38 verbalizations
Queries

Metthods (AIFB)
19
Evaluation (2/2)
Non-comparative evaluation
6 evaluators, 40 verbalizations
Queries
> Conclusions

Metthods (AIFB)
20
Conclusions
Verbalization of SPARQL queries allow users to
observe discrepancies between intended
questions and generated queries
Domain-independent approach:
Templates are based on linguistic properties of
properties
Evaluation shows
high accuracy,
acceptable syntactical correctness
outperforms SPARQL2NL i.t.o. understandability
Queries

Metthods (AIFB)
21
?question
Queries
The authors acknowledge the support of the European Commission's Seventh Framework Programme
FP7/2007-2013 (PlanetData, Grant 257641) and
FP7-ICT-2011-7 (XLike, Grant 288342).
#eswc2014Ell

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