Enhancing Relevance with Global Scores for DBpedia Facts

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Assigning Global Relevance Scores
to DBpedia Facts
Philipp Langer, Patrick Schulze, Stefan George,
Tobias Metzke, 
Ziawasch Abedjan
, Gjergji
Kasneci
DESWeb 03/31/2014
Structured Data
 
■
Advantages of structured data over
unstructured data:
□
Search for explicit facts
□
Summarization of possibly
interesting information
□
Automated knowledge discovery
■
Google Knowledge Graph
 
 
 
■
RDF Knowledge bases
□
DBpedia, YAGO/NAGA
Assigning Global Relevance Scores to DBpedia Facts
1
A handful of salient facts
about the query entity.
■
Asking for classes to which Albert Einstein belongs
Assigning Global Relevance Scores to DBpedia Facts
2
Querying YAGO
■
Asking for classes to which Albert Einstein belongs
Assigning Global Relevance Scores to DBpedia Facts
3
Querying DBpedia
Challenge
 
Assigning Global Relevance Scores to DBpedia Facts
4
select distinct ?p, ?o where
{ dbpedia:Barack_Obama ?p ?o}
Web Documents
Challenges
 
Assigning Global Relevance Scores to DBpedia Facts
5
B
i
g
 
D
a
t
a
DBpedia 3.8,
ClueWeb corpus
A
r
c
h
i
t
e
c
t
u
r
e
Text extraction, score
computation/ranking, query
processing
E
v
a
l
u
a
t
i
o
n
Conduction of user studies
R
a
n
k
i
n
g
 
S
t
r
a
t
e
g
i
e
s
Imrove the ranking results
Overview
Assigning Global Relevance Scores to DBpedia Facts
6
Languages
•
Python
•
Java
•
SPARQL
•
JavaScript
Frameworks:
•
Django
•
Lucene
Web application (Django)
DBpedia
Endpoint
(Apache Jena)
Application Data
(Postgres)
Web corpus
(Lucene Index)
User
Studies
Querying
Ranking strategies
Intra
DBpedia
strategies
Web
Corpus
strategies
6
Ranking Facts
 
■
Query types:
□
Subject queries - return all physicists
 
□
Property queries - return all facts related to Einstein
 
 
■
Ranking strategies
□
Ranking by frequency and document frequency
□
Ranking by information diversity
□
Random walk
□
Web-based co-occurrence statistics
Assigning Global Relevance Scores to DBpedia Facts
7
SELECT 
?p 
?o
 
{ 
Albert_Einstein
 
?p 
?o
 }
SELECT 
?s 
{ 
?s
 
type 
Physicist 
}
Ranking by frequency and document
frequency
Assigning Global Relevance Scores to DBpedia Facts
8
<Albert_Einstein>
<
topic
>
 
<Nobel_laureates>;
<
topic
>
 
<Theoretical_physicists>;
<
topic
>
 
<German_physicists>;
<
topic
>
 
<American_inventors>;
<type>
 
<Scientist>;
<type>
 
<Person>;
<type>
 
<Thing>;
<residence>
 
"Switzerland";
<residence>
 
"Austria-Hungary";
<residence>
 
"German Empire";
<spouse>
 
"Mileva Maric";
...
 
subject document of 
„Albert Einstein“
<Newton> <
topic
> <Theoretical_physicists>.
<Newton> <
topic
> <Nobel_laureates>.
<Newton> <
topic
> <Mathematicians>.
<Newton> <
topic
> <Optical_physicists>.
<Newton> <
topic
> <History_of_calculus>.
<Newton> <
topic
> <English_alchemists>.
<Einstein>
 
<
topic
> <Theoretical_physicists>.
<Einstein>
 
<
topic
> <Nobel_laureates>.
<Einstein>
 
<
topic
> <German_physicists>.
<Einstein>
 
<
topic
> <American_inventors>.
 
predicate document of
„topic“
<Isaac_Newton>   <
topic
> <Theoretical_physicists>.
<Albert_Einstein>
 
<
topic
> <Theoretical_physicists>.
<Bruno_Coppi>     <
topic
> <Theoretical_physicists>.
<Ravi_Gomatam> <
topic
> <Theoretical_physicists>.
 ...
 
object document of
„Theoretical physicists“
[Shady et al ESWC’11]
Ranking by frequency and document
frequency
■
Subject queries:
□
Global relevance
Assigning Global Relevance Scores to DBpedia Facts
9
Isaac Newton
academicAdvisor ...;
birthDate ...;
birthPlace ...;
comment ...;
ethnicity ...;
field ...;
influenced ...;
influencedBy ...;
knownFor ...;
label ...;
notableStudent ...;
subject ...;
subject ...;
type ...;
Ravi Gomatam
subject  ...;
subject  ...;
subject  ...;
subject  ...;
subject  ...;
Limitations for Property Queries
■
Property queries:
□
Global relevant but distinctive to the given subject
–
type
 
Person
 vs. 
type
 
Scientist
Assigning Global Relevance Scores to DBpedia Facts
10
Ranking by diversity
 
■
Following a probabilistic model
□
 Property queries:
–
Properties and objects that are as discriminative as
possible
 
 
□
Subject queries:
Assigning Global Relevance Scores to DBpedia Facts
11
Random Walk Model
 
 
■
Consider the knowledge base as a directed graph
□
Already applied in [Kasneci CIKM’09]
□
Problem: literals have no outgoing link
■
Use Wiki Pagelinks and Infobox Property Mappings
□
Entities with high indegree, such as countries, are favored
–
Good for subject queries
–
Bad for property queries
Assigning Global Relevance Scores to DBpedia Facts
12
Web Documents
Co-occurrence statistics
 
■
Lemur Project Clueweb09 Category-B web corpus
□
50 million web documents (1.5 TB)
□
Only English-language documents
□
Includes approx. 2.7 million Wikipedia articles
■
Create an inverted index
■
Consider different word distance limits as documents
■
Rank subject-object pairs
□
„Albert Einstein“ and „Physicist“
□
Store only pairwise co-occurrence:
□
Compute frequency of s:
Assigning Global Relevance Scores to DBpedia Facts
13
Evaluation
 
■
User study 1
□
8 queries
□
all results
□
12 users
□
19 approaches/
configurations
 
■
1-4: irrelevant- highly relevant
 
 
 
■
User study 2
□
8+20 queries
□
top-10 results of best 4
approaches side-by-side 10
users
□
Best 3 approaches from
user study 1
 
Assigning Global Relevance Scores to DBpedia Facts
14
Top 4 Approaches in User study 1
Assigning Global Relevance Scores to DBpedia Facts
15
User study 2
Assigning Global Relevance Scores to DBpedia Facts
16
Results Example:Theoretical
Physicists
Assigning Global Relevance Scores to DBpedia Facts
17
DBpedia
Random Walk Model
Results Example: Albert Einstein
DBpedia                                         Co-occurrence statistics
Assigning Global Relevance Scores to DBpedia Facts
18
Conclusions
■
Investigated multiple approaches to rank DBpedia facts
□
Information theory, statistical reasoning, random walk, and
co-occurrence statistics in web documents
■
DBpedia Knowledge base already provides enough information to
improve the ranking of results
■
Improvement of property queries through web-based co-
occurrence statistics
■
We provide the annotated datasets at
□
https://www.hpi.uni-potsdam.de/naumann/sites/dbpedia/ 
Assigning Global Relevance Scores to DBpedia Facts
19

								

								

								

										

												
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Explore the advantages of structured data over unstructured data, learn about querying YAGO and DBpedia for specific classes, and discover challenges and strategies in text extraction and ranking within the DBpedia framework. The overview delves into web applications, ranking strategies, and user studies using diverse technologies.


								
    
																		
  •   DBpedia
    • 
																	
  •    Structured Data
    • 
																	
  •    Relevance Scores
    • 
																	
  •    YAGO
    • 
																	
  •    Querying
    • 
																

								

								

									

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Presentation Transcript

							

							
  1. Assigning Global Relevance Scores  to DBpedia Facts Philipp Langer, Patrick Schulze, Stefan George,  Tobias Metzke, Ziawasch Abedjan, Gjergji  Kasneci DESWeb 03/31/2014
    2. 
            
            
            
  2. Structured Data 1     Advantages of structured data over  unstructured data:     Search for explicit facts     Summarization of possibly  interesting information     Automated knowledge discovery     Google Knowledge Graph A handful of salient facts  about the query entity.     RDF Knowledge bases     DBpedia, YAGO/NAGA Assigning Global Relevance Scores to DBpedia Facts
    3. 
            
            
            
  3. Querying YAGO 2     Asking for classes to which Albert Einstein belongs Assigning Global Relevance Scores to DBpedia Facts
    4. 
            
            
            
  4. Querying DBpedia 3     Asking for classes to which Albert Einstein belongs predicate object rdf:type owl:Thing rdf:type dbpedia:Agent rdf:type dbpedia:Person rdf:type dbpedia:Scientist rdf:type umbel:Scientist rdf:type schema:Person rdf:type yago:Astronomer109818343 rdf:type foaf:Person rdf:type 19th-centuryAmericanPeople rdf:type 19th-centuryGermanPeople Assigning Global Relevance Scores to DBpedia Facts
    5. 
            
            
            
  5. Challenge 4 select distinct ?p, ?o where  { dbpedia:Barack_Obama ?p ?o} Web Documents p c p c owl:orderInOffice President of the United  States rdf:type owl:Thing rdf:type dbpedia:Person dbpedia:type dbpedia:Politician rdf:type yago:Person100007846 dbpedia:spouse dbpedia:Michelle_Obama ... ... owl:birthPlace dbpedia:Honolulu rdf:type dbpedia:Politician dbpprop:residence dbpedia:White_House ... ... .... ..... dbpedia:spouse dbpedia:Michelle_Obama rdf:type owl:Thing Assigning Global Relevance Scores to DBpedia Facts
    6. 
            
            
            
  6. Challenges 5 Architecture Text extraction, score  computation/ranking, query  processing Big Data DBpedia 3.8,  ClueWeb corpus Ranking Strategies Imrove the ranking results Evaluation Conduction of user studies Assigning Global Relevance Scores to DBpedia Facts
    7. 
            
            
            
  7. Overview 6 Web application (Django) Languages Python     Ranking strategies Ranking strategies Java     Intra  DBpedia strategies Web  Corpus  strategies SPARQL     JavaScript     Frameworks: User  Studies Querying Django     Lucene     DBpedia  Endpoint (Apache Jena) Web corpus (Lucene Index) Application Data (Postgres) 6 Assigning Global Relevance Scores to DBpedia Facts
    8. 
            
            
            
  8. Ranking Facts 7     Query types:     Subject queries - return all physicists SELECT ?s { ?s type Physicist }     Property queries - return all facts related to Einstein SELECT ?p ?o { Albert_Einstein ?p ?o }     Ranking strategies     Ranking by frequency and document frequency     Ranking by information diversity     Random walk     Web-based co-occurrence statistics Assigning Global Relevance Scores to DBpedia Facts
    9. 
            
            
            
  9. Ranking by frequency and document  frequency 8 ds={(s,x,y)|(s,x,y)  GKB} [Shady et al ESWC   11] dp={(x,p,y)|(x,p,y)  GKB} subject document of     Albert Einstein    predicate document of     topic    <Albert_Einstein> do={(x,y,o)|(x,y,o)  GKB} freq(s;p)=|{(s, p,x)|(s, p,x)  GKB}| <topic> <topic> <topic> <topic> <type> <type> <type> <residence> "Switzerland"; <residence> "Austria-Hungary"; <residence> "German Empire"; <spouse> "Mileva Maric"; ... <Nobel_laureates>; <Theoretical_physicists>; <German_physicists>; <American_inventors>; <Scientist>; <Person>; <Thing>; <Newton> <topic> <Theoretical_physicists>. <Newton> <topic> <Nobel_laureates>. <Newton> <topic> <Mathematicians>. <Newton> <topic> <Optical_physicists>. <Newton> <topic> <History_of_calculus>. <Newton> <topic> <English_alchemists>. |dp| object document of     Theoretical physicists    dfreq(s)=|{p|(s, p,x)   ds}|+|{o|(s,x,o)   ds}| |D| <Isaac_Newton>   <topic> <Theoretical_physicists>. <Albert_Einstein> <topic> <Theoretical_physicists>. <Bruno_Coppi>     <topic> <Theoretical_physicists>. <Ravi_Gomatam> <topic> <Theoretical_physicists>. ... <Einstein> <topic> <Theoretical_physicists>. <Einstein> <topic> <Nobel_laureates>. <Einstein> <topic> <German_physicists>. <Einstein> <topic> <American_inventors>. Assigning Global Relevance Scores to DBpedia Facts
    10. 
            
            
            
  10. Ranking by frequency and document  frequency Isaac Newton academicAdvisor ...; birthDate ...; birthPlace ...; comment ...; ethnicity ...; field ...; influenced ...; influencedBy ...; knownFor ...; label ...; notableStudent ...; subject ...; subject ...; type ...; 9 Ravi Gomatam     Subject queries: subject  ...; subject  ...; subject  ...; subject  ...; subject  ...;     Global relevance Score(s;p,o)= freq(s;p)  freq(s;o)  log(1+dfreq(s)) Assigning Global Relevance Scores to DBpedia Facts
    11. 
            
            
            
  11. Limitations for Property Queries 10     Property queries:     Global relevant but distinctive to the given subject     type Person vs. type Scientist freq(p;s)  freq(o;s) log(1+dfreq(p))  log(1+dfreq(o)) Score(p,o;s)= Assigning Global Relevance Scores to DBpedia Facts
    12. 
            
            
            
  12. Ranking by diversity 11     Following a probabilistic model     Property queries:     Properties and objects that are as discriminative as  possible P(p,o|s)  aP(o| p)  P(p|s)+(1-a)P(p|o)  P(o|s)     Subject queries: P(s| p,o)   P(s)  P(p,o|s) Assigning Global Relevance Scores to DBpedia Facts
    13. 
            
            
            
  13. Random Walk Model 12     Consider the knowledge base as a directed graph     Already applied in [Kasneci CIKM   09]     Problem: literals have no outgoing link     Use Wiki Pagelinks and Infobox Property Mappings     Entities with high indegree, such as countries, are favored     Good for subject queries     Bad for property queries Assigning Global Relevance Scores to DBpedia Facts
    14. 
            
            
            
  14. Co-occurrence statistics 13 Web Documents     Lemur Project Clueweb09 Category-B web corpus     50 million web documents (1.5 TB)     Only English-language documents     Includes approx. 2.7 million Wikipedia articles     Create an inverted index     Consider different word distance limits as documents     Rank subject-object pairs        Albert Einstein    and    Physicist    # par(s,o)        Store only pairwise co-occurrence:     Compute frequency of s:  # par(s)= # par(s,o) (s,x,o)  G Assigning Global Relevance Scores to DBpedia Facts
    15. 
            
            
            
  15. Evaluation 14     User study 1     User study 2     8 queries     8+20 queries     all results     top-10 results of best 4  approaches side-by-side 10  users     12 users     19 approaches/  configurations     Best 3 approaches from  user study 1     1-4: irrelevant- highly relevant Assigning Global Relevance Scores to DBpedia Facts
    16. 
            
            
            
  16. Top 4 Approaches in User study 1 15 Assigning Global Relevance Scores to DBpedia Facts
    17. 
            
            
            
  17. User study 2 16 Assigning Global Relevance Scores to DBpedia Facts
    18. 
            
            
            
  18. Results Example:Theoretical  Physicists 17 DBpedia Random Walk Model Subject Albert Einstein Isaac Newton Galileo Galilei James Clerk Maxwell Richard Feynman Stephen Hawking Max Planck Enrico Fermi Werner Heisenberg Pierre-Simon Laplace Assigning Global Relevance Scores to DBpedia Facts
    19. 
            
            
            
  19. Results Example: Albert Einstein 18 DBpedia  Co-occurrence statistics predicate object predicate object rdf:type owl:Thing fields Physics rdf:type dbpedia:Agent field Physics rdf:type dbpedia:Person deathPlace United States rdf:type dbpedia:Scientist placeOfDeath United States rdf:type umbel:Scientist shortDescription Physicists rdf:type schema:Person description Physicist rdf:type yago:Astronomer109818343 type Scientist ethnicity Jewish rdf:type foaf:Person subject Einstein family rdf:type 19th-centuryAmericanPeople residence Switzerland rdf:type 19th-centuryGermanPeople Assigning Global Relevance Scores to DBpedia Facts
    20. 
            
            
            
  20. Conclusions 19     Investigated multiple approaches to rank DBpedia facts     Information theory, statistical reasoning, random walk, and  co-occurrence statistics in web documents     DBpedia Knowledge base already provides enough information to  improve the ranking of results     Improvement of property queries through web-based co- occurrence statistics     We provide the annotated datasets at     https://www.hpi.uni-potsdam.de/naumann/sites/dbpedia/  Assigning Global Relevance Scores to DBpedia Facts
    21. 
            
            
            
						

						

					

					
					
					

						
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