Knowledge Base

―Semantic Web and Ontology (4)―

Masaharu Yoshioka

Answer of the last lecture question

◼ Following are list of concepts and roles defined

for the question

– Concepts

• Female, Male, Human ≡ Male ⊔ Female, Animal

– Roles

• has-student, bred-by, eaten-by

◼ Define following concepts using given concepts

and roles.

Answer of the last lecture question (cont)

◼ Domestic animal（Animal who is bred by human

for eating by human）Animal ⊓ eaten-by.Human ⊓ bred-by.Human

Animal ⊓ eaten-by.Human ⊓ bred-by.Human

◼ Teacher（Man who has human student）Human ⊓ has-student.Human

◼ Female teacher

Female ⊓ has-student.Human

◼ Teacher who have only male students

– Human ⊓ has-student. Male ⊓ has-student. Male

Comments from the last report

◼ 男の子だけを持つ父親はどう書きますか？

◼ Can I interpret that epistemology is an antonym of

ontology?

◼ DL言語などがどのようにコンピュータデー応用されているのでしょうか？

◼ 私は普通に英語でもOKです。

◼ 日本語で話す割合を増やしてほしい。日本語メインで英語をサブにしてほしい。

◼ 予習したいので、スライドを早くほしい。

Web Ontology: Background

◼ Web2.0

– Large volume of user generated contents

• Wikipedia：Encyclopedia edited by volunteer editors

• GeoNames：Geographical database that can edit by

volunteer editors

– Varieties of knowledge resources are available through

the Web

◼ Basic information about concept hierarchy

– WordNet：Dictionary for representing sense for the

strings (words)

• Define concept hierarchy, word sense relationships,

hypernym, hyponym, …

Wikipedia

◼ Free Internet based encyclopedia

– Metadata related to an article

is organized in an Infobox

– Articles are categorized into

multiple categories

DBpedia

◼ Metadata database based on Wikipedia articles

– Core part of Linked Open Data

◼ Quality of DBpedia relies on quality of Wikipedia

DBpedia Facts & Figures

https://wiki.dbpedia.org/about/facts-figures

◼ Large information resource of things

– 4.58 million things

– 4.22 million are classified in a consistent ontology (DBpedia ontology)

• 1,445,000 persons

• 735,000 places

– 478,000 populated places

• 411,000 creative works

– 123,000 music albums, 87,000 films and 19,000 video games

• 241,000 organizations

– 58,000 companies and 49,000 educational institutions

• 251,000 species

• 6,000 diseases.

DBPedia Ontologyhttp://mappings.dbpedia.org/server/ontology/classes/

◼ Ontology for classify the things defined in

DBpedia

Data Example of DBpedia

◼ Add metadata to the correcsponding Wikipedia

article in RDF format

<http://dbpedia.org/resource/Aristotle>

<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>

<http://www.w3.org/2002/07/owl#Thing> .

<http://dbpedia.org/resource/Aristotle>

<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>

<http://dbpedia.org/ontology/Person> .

<http://dbpedia.org/resource/Aristotle>

<http://dbpedia.org/property/wikiPageUsesTemplate>

<http://dbpedia.org/resource/Template:Persondata> .

<http://dbpedia.org/resource/Aristotle>

<http://dbpedia.org/property/placeOfDeath> "Chalcis"@en .

<http://dbpedia.org/resource/Aristotle>

<http://dbpedia.org/property/dateOfDeath> "322 BC"@en .

<http://dbpedia.org/resource/Aristotle>

<http://dbpedia.org/property/placeOfBirth> "Stageira"@en .

Wikidata

◼ Wikidata stores structured

data that are not precise

enough using Infobox

https://www.wikidata.org/

wiki/Wikidata:Main_Page

WordNet

https://wordnet.princeton.edu/

◼ WordNet is a large lexical database of English.

Nouns, verbs, adjectives and adverbs are grouped

into sets of cognitive synonyms (synsets), each

expressing a distinct concept.

– Synsets represent word sense.

◼ Synsets are interlinked by means of conceptual-

semantic and lexical relations.

– Words (strings) with multiple sense (e.g., bridge) have

multiple links with synsets.

– Synsets with synonyms (multiple words for one sense)

have multiple links with words(strings).

Concept Definition of WordNet

http://wordnetweb.princeton.edu/perl/webwn

◼ Browsing WordNet using WordNet Search

– Search Results for “philosopher”

WordNet Statisticshttps://wordnet.princeton.edu/documentation/wnstats7wn

◼ Version 3.0 (Version 3.1)

◼ There are several other language version including

Japanese version(Wn-Ja 1.1)

http://compling.hss.ntu.edu.sg/wnja/

– 57,238 synsets; 93,834 words; 158,058 word-sense

pairs

– 135,692 Definitions; 48,276 example sentences

POS Unique Synsets Total Strings Word-Sense Pairs

Noun 117798 82115 146312

Verb 11529 13767 25047

Adjective 21479 18156 30002

Adverb 4481 3621 5580

Totals 155287(175979) 117659 (155327) 206941 (207016)

Linked Open Data

◼ Link open data provided by various knowledge

and information resources for using them as a

large database, knowledge base.

◼ One of the largest ontology based knowledge base

that covers concept definition and knowledge

about instances.

Linking Open Data Cloud

“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”

YAGO：Yet Another Great Ontology

◼ Web-based ontology constructed by using

Wikipedia、WordNet、GeoNames

◼ Class definition based on the Wikipedia categories

and WordNet for handling class hierarchy

– Select appropriate class for the article using category

list of the article with the reference to the category

Class：PhilosopherCategory of Aristotle

Integration of Geographical Information to

YAGO

◼ GeoNames: Linked Open Data for Geographical

Data

more than 10,000,000

geographical entries

19.8

million

articles

large geographical database

YAGO2

(Yet Another Great Ontology2)

Integration method by YAGO2

[Hoffart et.al., 2012]

◼ Integration by using name and coordinate'Burgos' Province in Spainhttp://en.wikipedia.org/wiki/Burgos

'Min River' River in Chinahttp://en.wikipedia.org/wiki/Min_River_(Fujian)

Name matching

Coordinates matching

84,349 corresponding pairs have been found.

SPARQL Endpoint

◼ Most of the Linked Open Data sites provide

SPARQL Endpoint.

– DBpedia

http://dbpedia.org/sparql

– DBpedia(Japanese)

http://ja.dbpedia.org/sparql

– Wikidata

https://query.wikidata.org/

– YAGO2

https://gate.d5.mpi-

inf.mpg.de/webyagospotlx/WebInterface

Example of Using SPARQL Endpoint

◼ DBpedia(Japanese)

http://ja.dbpedia.org/sparql

– Example

http://ja.dbpedia.org/

◼ YAGO2

https://www.mpi-

inf.mpg.de/departments/databases-and-

information-systems/research/yago-

naga/yago/demo/

Supplemental material

◼ Another approach to make a mapping between

GeoNames and Wikipedia

Integration Method based on Wikipedia

Category [Yoshioka et al, 2012]

◼ Wikipedia category for geographical entity

– <class information> (in|of) <location information>

e.g., Populated Place In Spain

◼ GeoNames

– Country and administrative code: location information

– Feature code: class information

'Burgos' Province in Spainhttp://en.wikipedia.org/wiki/Burgos

Populated Place In Spain -> Populated Place In Burgos

Integration Method based on Wikipedia Category

◼ Use matching tables between feature code and

Wikipedia category class information

– Distance is not first priority information to select an

appropriate corresponding entity.

'Narosura' populated place in Kenya

http://en.wikipedia.org/wiki/Narosura

Distance ○

△

Algorithms for link discovery

◼ Comparison of names for candidate pairs

Wikipedia: Rome, Iowa

Category: Cities in Iowa

Populated places in Henry County, Iowa

GeoNames(id, name (alter name), feature

class, country and administrative code)

6459720, Rome, PPL, US:IA:087

3169070, Roma (Rome), PPLC, IT:07:RM,

….

Algorithms for link discovery 2

◼ Extraction of information from category

Wikipedia: Rome, IowaCategory: Cities in Iowa Populated places in Henry County, Iowa

Class candidates:

City, Populated places

→

PPL, PPLC, ADM1, …

Location candidates:

Iowa → US:IA

Henry County, Iowa

→ US:IA

Algorithms for link discovery 3

◼ Selection of candidate pair

Wikipedia: Rome, IowaCategory: Cities in Iowa Populated places in Henry County, Iowa

GeoNames(id, name (alter name), feature

class, country and administrative code)

○6459720, Rome, PPL, US:IA:087

×3169070, Roma (Rome), PPLC, IT:07:RM, ….

Algorithms for link discovery 4

◼ Elimination of low precision data

– 1 to N mapping (It may includes errors)

• Multiple Wikipedia pages for a single GeoNames

entry

• Multiple GeoNames entries for a single Wikipedia

page

Results of Automatic Integration

◼ Classify integration results by using distance

information

– Wikipedia coordinate information is extracted by using

DBpedia and GeoHack

Types of pairs Pages Manual

evaluation

Nearby pairs (<= 5km) 26,047 200/200

Distant pairs (>5km) 4,333 180/200

Pairs with no distance

information

14,200 190/200

Inconsistent Geographical Information

◼ There are several appropriate pairs with long

distance.

Type of Inconsistency Cases

Inconsistent geographic information for

appropriate pairs (e.g., large area such as lake,

stream,…)

150/200

Errors in Wikipedia and/or GeoNames 30/200

Errors due to our link detection method 20/200

Errors in Automatic Integration

◼ Variations in names

– The names of entities might not be represented in

English in GeoNames.

◼ Failure to estimate the appropriate administrative

code

– Wikipedia category has administrative information, but

name of the administrative code is different from

GeoNames ones.

Errors in Original Data (Wikipedia and

DBpedia)

◼ Wikipedia infobox may include errors

– There are several errors for coordinate in Wikipedia

• Copy and paste

• Difficulties to use template (hidden parameters for

type of longitude (E or W))

– DBPedia also contains many errors for coordinate

information

• DBPedia assumes coordinates are represented by 3

integers (degrees, minutes and seconds) but there are

several coordinate information by using float values.

Errors in Original Data (GeoNames)

◼ Inappropriate pairs between GeoNames and

Wikipedia in original GeoNames database

– Failure about disambiguation of entries for different

feature code

e.g., Populated place is matched with train station of the

city.

Another Issues for Linking Wikipedia and

GeoNames

◼ Different granularity level of the geographical

entity

– It is problematic for using owl:SameAs link.

◼ Wikipedia issues

– Geographical entities with multiple points

• Geographical entity about large area may contains

multiple points.

• Example: river (source, mouth, …)

– Wikipedia pages with multiple geographical entities

• Geographical entity about large area may contains

multiple points.

• Example: mountain range pages contained

information about several mountains in the range

Another Issues for Linking Wikipedia and

GeoNames (cont.)

◼ GeoNames issues

– Geographical entities with multiple feature classes

• A single GeoNames entry corresponds to one

feature class.

• Example: “Milolii, Hawaii” has two corresponding

GeoNames entities (5851041: administrative

division and 5851402: populated place).

Summary

◼ Semantic Web

– For handling the semantic information of the web page,

it is necessary to have

• Ontology: Concept definition for understanding the

difference of the meaning in different website.

• Metadata annotation: It is necessary to have

metadata annotation as structured data with schema

definition.