BOA Bootstrapping the Linked Data Web Daniel Gerber, Axel-Cyrille - - PowerPoint PPT Presentation

BOA

Bootstrapping the Linked Data Web

Daniel Gerber, Axel-Cyrille Ngonga Ngomo

AKSW, Universität Leipzig

http://www.volunteer-conservation-peru.org

http://aksw.org/files/boa.pdf

Motivation

• most knowledge bases extracted from

(semi)-structured data

• Linked Data Cloud grows
• BUT: only 15-20 % of information
• How can we extract data from the

document-oriented web?

Idea

• start with triples from the Data Web
• extract natural language patterns which

express predicates found in triples

• combine patterns & NLP to find labels

which stand in relation with predicate

• generate RDF and feed it into Data Web

Related Work

• NLP & RDF:
• Fox, Extractiv, Alchemy, OpenCalais
• NELL [CAR+10]
• initial ontology: 100+ categories/relations
• PROSPERA [NDA+11]
• harvesting of n-grams-itemset patterns ➤ generalisation

without adding noise

• [JUR+10] M. Mintz, S. Bills, R. Snow, and D. Jurafsky. Distant

supervision for relation extraction without labeled data. ACL, pages 1003–1011, 2009.

The BOA approach

Corpus extraction

• Crawler
• Seed Pages, removes HTML
• Cleaner
• SBD, UTF-8 filters to remove noise
• Indexer
• sentences get index

Knowledge acquisition

• Class C that serves as the rdfs:domain
• r as the rdfs:range of predicate p
• knowledge base for background

knowledge

• extract statements with entities of

rdf:type C as subject or object

• db:Place, db:Person, db:Organisation

Pattern Search

• set of entities s and o connected through p
• find sentences which contain s & o, strip the rest
• replace labels with variables (?D?, ?R?)
• A BOA pattern is a pair P = (μ(p), θ), where μ(p) is p’s

URI and θ is a natural language representation of p.

• A BOA pattern mapping is a function M such that M(p)

= S , where S is the set of natural language representations for p.

• Occurrences, sentences, labels p is learned from,

number of occurrences for each label combination

Pattern Scoring

• Pattern Filtering: Length, Stop Words, Occurrence
• Support: used across several triples in background

knowledge

• Typicity: allows to map ?D?, ?R? to entities with

rdf:type of domain/range of p

• Specificity: used exclusively to express p, IDF

adopted to patterns

• (Similarity: how similar is a pattern to label of

predicate)

• Combine Support, Typicity, Specificity to calculate

local maximum

RDF Generation

• use top-n pattern for each relation
• find sentences which contain pattern
• NER-tag sentence
• look for token’s classes which match

domain/range

• extract labels
• URI retrieval above threshold do not create new

URI

Demo

http://139.18.2.164:8080/boa

Evaluation

• Corpora
• en_wiki (44.7M), en_news (256.1M)
• Background Knowledge
• Organisation, Place and Person (283

relations from 1 to 471920 triples)

• Parameters
• top1,2 pattern, kappa, 500 sentences for

Typicity, 100 example sentences for 12 different KBs

Results

Examples

Relation Top-2 Pattern URI Domain/Range en-wiki en-news foundationPerson Organisation/Person

• 1. R , co-founder of D
• 2. R , founder of D
• 1. R, the co-founder of D
• 2. R, founder of the D

subsidiary Organisation/Organisati

• n
• 1. R , a subsidiary of D
• 2. - (R , a division of D)
• 1. R, a division of D
• 2. D‘s acquisition of R

birthPlace Person/PopulatedPlace 1.D was born in R

• 2. - (D , the mayor of R)
• 1. D has been named in the

R

• 2. D, MP for R

Discussion

• we can use patterns from wiki for every

corpus

• we create many new triples
• we create correct triples
• we need 15 minutes for one iteration
• Q1 & Q2 answered with YES

Future Work

• Train NER on DBpedia classes
• Iteration 1+
• Human feedback
• Pattern generalization
• rdf:type extractor
• Languages/Corpora
• Webservices

Thank you!

Questions?

References

• [NDA+11]
• Ndapandula Nakashole, Martin Theobald, and Gerhard
• Weikum. Scalable knowledge harvesting with high

precision and high recall. In WSDM, pages 227–236, Hong Kong, 2011.

• [CAR+10]
• Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr

Settles, Estevam R. Hruschka Jr., and Tom M. Mitchell. Toward an architecture for never-ending language

• learning. In AAAI, 2010.