Assigning Global Relevance Scores to DBpedia Facts Philipp Langer, Patrick Schulze, Stefan George,...

Assigning Global Relevance Scores to DBpedia Facts

Philipp Langer, Patrick Schulze, Stefan George, Tobias Metzke, Ziawasch Abedjan, Gjergji Kasneci

DESWeb 03/31/2014


2

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

A handful of salient facts about the query entity.


3■ Asking for classes to which Albert Einstein belongs

Querying YAGO


4■ Asking for classes to which Albert Einstein belongs

Querying DBpedia

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


5

Challenge

select distinct ?p, ?o where

{ dbpedia:Barack_Obama ?p ?o}

p c

rdf:type owl:Thing


rdf:type yago:Person100007846

... ...

rdf:type dbpedia:Politician

... ...

dbpedia:spouse dbpedia:Michelle_Obama

Web Documents

p c

owl:orderInOffice President of the United States

dbpedia:type dbpedia:Politician

dbpedia:spouse dbpedia:Michelle_Obama

owl:birthPlace dbpedia:Honolulu

dbpprop:residence dbpedia:White_House

.... .....

rdf:type owl:Thing


6

Challenges

Big DataDBpedia 3.8,

ClueWeb corpus

ArchitectureText extraction, score

computation/ranking, query processing

EvaluationConduction of user studies

Ranking StrategiesImrove the ranking results


Overview

7Languages

• Python

• Java

• SPARQL

• JavaScript

Frameworks:

• Django

• Lucene

Web application (Django)

DBpedia Endpoint

(Apache Jena)

Application Data(Postgres)

Web corpus(Lucene Index)

User StudiesQuerying

Ranking strategiesRanking strategies

Intra DBpedia

strategies

Web Corpus

strategies

7


8

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

SELECT ?p ?o { Albert_Einstein ?p ?o }

SELECT ?s { ?s type Physicist }


9

Ranking by frequency and document frequency

<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]


10

Ranking by frequency and document frequency

■ Subject queries:

□ Global relevance

Isaac NewtonacademicAdvisor ...;birthDate ...;birthPlace ...;comment ...;ethnicity ...;field ...;influenced ...;influencedBy ...;knownFor ...;label ...;notableStudent ...;subject ...;subject ...;type ...;

Ravi Gomatam

subject ...;subject ...;subject ...;subject ...;subject ...;


11

Limitations for Property Queries

■ Property queries:

□ Global relevant but distinctive to the given subject– type Person vs. type Scientist


12

Ranking by diversity

■ Following a probabilistic model

□ Property queries:– Properties and objects that are as discriminative as

possible

□ Subject queries:


13

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


14

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:


15

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


16

Top 4 Approaches in User study 1


17

User study 2


18

Results Example:Theoretical Physicists

Subject

Albert Einstein

Isaac Newton

Galileo Galilei

James Clerk Maxwell

Richard Feynman

Stephen Hawking

Max Planck

Enrico Fermi

Werner Heisenberg

Pierre-Simon Laplace

DBpedia Random Walk Model


19

Results Example: Albert Einstein

DBpedia Co-occurrence statistics

predicate object

rdf:type owl:Thing

rdf:type dbpedia:Agent


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

predicate object

fields Physics

field Physics

deathPlace United States

placeOfDeath United States

shortDescription Physicists

description Physicist

type Scientist

ethnicity Jewish

subject Einstein family

residence Switzerland


20

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 Philipp Langer, Patrick Schulze, Stefan George,...

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