Finding Good URLs: Aligning Entities in Knowledge Bases with Public Web Document Representations

Institute for Web Science & Technologies – WeST

Finding Good URLs: Aligning Entities in Knowledge Bases with Public Web

Document Representations

Christian Hachenberg and Thomas Gottron

Workshop Web of Linked Entities (WoLE 2012) at ISWC 2012

Sunday, 11 November 2012

Thomas Gottron WoLE Workshop 2012 2Finding Good URLs

Mapping Documents to Entities

dbpedia.org:Rob_Roy_(film)


Mapping Entities to Documents

dbpedia.org:Rob_Roy_(film)

Align entities in KB with public documents

• Publish knowledge base• Propagate changes• Human readable

representation

Align entities in KB with public documents

• Publish knowledge base• Propagate changes• Human readable

representation


Task Definition

Harrison Ford

dbpedia:Star_Wars_Episode_IV:_A_New_Hope

dbpedia:George_Lucas

prop

erty

: sta

rrin

g

type: actor

type: movie

type: director

dbpedia:Harrison_Ford

Star Wars IV: A New Hope

George Lucas

prop

erty

: dire

cts

3 types of information:

• Labels• Link structure• Types

???


Label Search (using Web Search Engine)

Harrison Ford



prop

erty

: sta

rrin

g

type: actor

type: movie

type: director



George Lucas

prop

erty

: dire

cts

SW4

SW4

SW4

Implementation:

• Bing


Exploiting Link Structure

Harrison Ford



prop

erty

: sta

rrin

g

type: actor

type: movie

type: director



George Lucas

prop

erty

: dire

cts

GL SW4

SW4

SW4HF

Implementation:

• In-degree• PageRank• HITS

+ Variations:Topic, Focussed


Type Filtering


type: movie


SW4

SW4

RR

SW4

GT

Implementation:

• Borda Count for domain ranking

dbpedia:Gran_Torino_(film)

type: movieGran Torino

dbpedia:Rob_Roy_(film)

type: movieRob Roy


Experimental Setup

100 Entities 4 domains (cities, companies, persons, movies) Stratified by little, medium and large representation on the

web Complete network of linked entities

Application of label search and link structure approaches Type-filtering as post-process

User evaluation (Cranfield setup, pooling) Graded relevance judgements High juror agreement (Krippendorff's Alpha >0.67)


Evaluation Metrics

At which rank can I expect the first relevant result

Average P@1: How often can I expect the first result to be relevant

Precision@1


Evaluation: Results

Statistically significant , p=0.05


Evaluation: Results (Domain, Stratum)


Evaluation: Results (Filtering)


Conclusions and Next Steps

Novel task: Mapping entities to public web URLs

– Evaluated 9 link analysis and web search methods (+1 post-processing using Borda counts)

– Best methods: Label Search and Focussed HITS• Semantic Typing boosts all results

Next steps: Investigate domain-dependent performance of methods


Thank you!

Contact:WeST – Institute for Web Science and Technologies

Universität Koblenz-Landau

[email protected]

Finding Good URLs: Aligning Entities in Knowledge Bases with Public Web Document Representations

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