summarization and relevance of linked data facts

Dr. Harald SackHasso-Plattner-Institut for IT-Systems Engineering

University of Potsdam

Summarization and Relevance of

Linked Data Facts

4. Leipziger Semantic Web Tag 2012Leipzig, 25.09.2012

Dienstag, 25. September 12



Summarization and Relevance of Linked Data Facts

•Linked Open Data and Semantics•The Importance of Being Relevant•The Most Important Facts•Heuristics for Fact Relevance•Relevance Evaluation


Dr. Harald Sack, Hasso-Plattner-Institut Potsdam, Leipziger Semantic Web Tage, 24./25.09.2012, Leipzig

There are more than 30 Billion facts in the Linked Data Universe

http://www4.wiwiss.fu-berlin.de/lodcloud/state/










• LOD facts are encoded in RDF• related ontologies only provide ,shallow‘ semantics• poor data quality

• inconsistencies • ambiguities• redundancies

• mapping and interlinking are still a problem

State of LOD - as a knowledge base



Consider a single LOD Dataset

Aldous Huxley

•e.g.,..Albert Einstein•> 600 facts•> 70 properties•no given order of facts•no given relevance of facts



How to find outwhat facts are

important?



• dbpedia:Albert_Einstein rdf:type yago:AmericanVegetarians .

vs.

• dbpedia:Albert_Einstein rdf:type yago:TheoreticalPhysicist .

Which fact is more important?




vs.


Importance depends on the Context

Context: ,Nutrition‘




vs.


Context: ,Science‘

Importance depends on the Context



What can it be used for?



• Making Decisions• which fact(s) should be considered to make a decision?• how should the (relevant) facts be weighted to make the

right decision? • Applications: Recommendation, Exploratory Search,...

• Creating Summarizations• “entity summarization ... produce a version of the original

[entity] description that is more concise, yet containing sufficient information for users to quickly identify the underlying entity.“ [1]

[1] Gong Cheng, Thanh Tran, and Yuzhong Qu. \RE-LIN: relatedness and informativeness-based centralityfor entity summarization". In: Proc. of the 10th intl.conf. on The semantic web - Volume Part I. ISWC'11.Bonn, Germany: Springer-Verlag, 2011, pp. 114{129.

What can it be used for?



Google Knowledge Graph

• Summarizations of factsrelated to a given entity

• on average 192 facts per entity [2]

[2] Gong Cheng, Thanh Tran, and Yuzhong Qu. RELIN: relatedness and informativeness-based centrality for entity summarization. In Proc. of the 10th int. conf. on The Semantic Web - Vol. Part I, ISWC’11, pages 114–129, Berlin, Heidelberg, 2011. Springer-Verlag.




• the Google users decidewith their querieswhat is important

• Queries:• subject + object

„Einstein ETH Zürich“

• subject + property„Einstein birthplace“

• Collaborative Filtering



Entity Summarization

dbpedia:Albert_Einstein

yago:AmericanVegetarian

yago:TheoreticalPhysicist

rdt:type

rdt:type

Albert

foaf:givenName

Einstein

foaf:familyName

RDF GraphDienstag, 25. September 12





yago:TheoreticalPhysicist

rdt:type

rdt:type

Albert

foaf:givenName

Einstein

foaf:familyName

RDF Graph

FS(dbpedia:Albert_Einstein)FS(dbpedia:Albert_Einstein)

f1 <rdf:type, yago:AmericanVegetarian>

f2 <rdf:type, yago:TheoreticalPhysicist>

f3 <foaf:givenName, „Albert“>

f4 <foaf:familyName, „Einstein“>




Given a feature set FS(e) of an entity e and a positive integer k < |FS(e)|, the problem of entity summarization is to select Summ(e) ⊂ FS(e) such that |Summ(e)| = k. Summ(e) is called a summary of e. [3]

[3] Gong Cheng, Thanh Tran, and Yuzhong Qu. \RE-LIN: relatedness and informativeness-based centralityfor entity summarization". In: Proc. of the 10th intl.conf. on The semantic web - Volume Part I. ISWC'11.Bonn, Germany: Springer-Verlag, 2011, pp. 114{129.



How to determine relevant LOD facts?

graph analysis

(usage) statistics

semantic analysis

linguistic analysis



Heuristics for Feature Relevance

(1) (Manual) Whitelisting(2) RDF-properties connecting objects of same rdf:type(3) Inverse and symmetric RDF-properties(4) Disambiguations(5) Bidirectional (wiki)Links(6) Linguistic Co-occurrences(7) Frequency of features shared with entities of same type (8) Frequency of features shared with ,neighborhood‘

entities [4,5]

[4] Jörg Waitelonis and Harald Sack. Towards exploratory video search using linked data. Multimedia Tools and Applications, 59:645–672, 2012[5] Andreas Thalhammer, Ioan Toma, Antonio J. Roa-Valverde, and Dieter Fensel. Leveraging usage data for linked data movie entity summarization. In Proc. of the 2nd Int. Ws. on Usage Analysis and the Web of Data (USEWOD2012)



Heuristics for Feature Relevance(1) (Manual) Whitelisting

dbpedia:Albert_Einstein dbpedia:Switzerland

dbpedia-owl:residence

dbpedia-owl:Place

rdf:type

dbpedia-owl:Person

rdf:type

Locations are (manually) considered to be important for persons...



Heuristics for Feature Relevance(2) RDF-properties connecting objects of same rdf:type



dbpedia-owl:Scientist

rdt:type

rdt:type

dbpedia:AlfredKleiner

dbpedia-owl:doctoralAdvisor

rdt:type

dbpedia:BillCosby

rdt:type



Heuristics for Feature Relevance(3) Inverse and Symmetric RDF properties

dbpedia:Albert_Einstein dbpedia-owl:Scientist

rdt:type


dbpedia-owl:doctoralAdvisor

rdt:type

dbpedia-owl:doctoralStudent

dbpedia-owl:doctoralAdvisor owl:inverseOf dbpedia:doctoralStudent .



Heuristics for Feature Relevance(4) Disambiguations

dbpedia:Sumerian dbpedia:Cuneiform_script

dbpedia-owl:wikiPageDisambiguates

dbpedia:Sumer

dbpedia:Sumerian_religion

dbpedia:Sumerian_language

dbpedia:Sumerian_art



Heuristics for Feature Relevance(5) Bidirektional (Wiki)Links



dbprop:wikilink

dbprop:wikilink



Heuristics for Feature Relevance(6) Linguistic Co-occurrences

dbpedia:Albert_Einstein dbpedia:Physics

dbpprop:fields

Albert

foaf:givenNameEinstein

foaf:familyName

dbpedia:Violinists

rdf:type



Heuristics for Feature Relevance(7) Frequency of features shared with entities of same type



dbpedia-owl:Scientist

rdt:type

rdt:type

dbpedia:Archimedes

dbpedia:Niels_Bohr

rdt:type



Heuristics for Feature Relevance(8) Frequency of features shared with ,neighborhood‘ entities

(1) Determine neighborhood entitiesNe,k ⊂ E of an entity e ∈ E

(2) Frequency of features shared withneighbors determines featureimportance:

For all features FS(e) of entity e:Ae,f and Be,f are sets of items sharing the same features,where Ae,f ⊂ Ne,k and Be,f ⊂ E

(3) The weight we(f) of a feature ffor an entity e is determined as



A Ground Truth is Hard to Find

• A ground truth for relevance evaluation of facts is hard to find. We need:

• a sufficient number of arbitrary and independent facts• a sufficient number of people to ask with different

opinions

• Idea: Crowd Sourcing

• Game based approach for evaluation



Game Based Evaluation Approach

• Idea: a Quiz GameCreate Questions from LOD facts

• Hypotheses:• If the user is able to answer the question correctly,

it is rather likely that the fact behind the answer iswell known and of some importance for the related entity.

• If the user is not able to answer the question or gives the wrong answer, it is rather likely that the fact behind the answer is not well known and maybe not of high importance for the related entity.




Jörg Waitelonis, Nadine Ludwig, Magnus Knuth, Harald Sack: Whoknows? - Evaluating Linked Data Heuristics with a Quiz that cleans up DBpedia. International Journal of Interactive Technology and Smart Education (ITSE), Emerald Group, Vol. 8, 2011 (3).

http://tinyurl.com/whoknowsgame


http://www.hpi.uni-potsdam.de/fileadmin/hpi/FG_ITS/Semantic-Technologies/paper/Waitelonis2011b.pdf








Lina Wolf, Magnus Knuth, Johannes P. Osterhoff, Harald Sack: RISQ! Renowned Individuals Semantic Quiz – A Jeopardy like Quiz Game for Ranking Facts. In Proc. of 7th Int. Conf. on Semantic Systems I-SEMANTICS, 07.-09. Sept., 2011, Graz, Austria, ACM, 2011, pp. 71-78.

http://apps.facebook.com/hpi-risq


http://www.hpi.uni-potsdam.de/fileadmin/hpi/FG_ITS/Semantic-Technologies/paper/Wolf2011.pdf








• WhoKnows? Movies! game adapted for entity summarization in the movie domain

• restricted set of entities (movies) and related properties

• Questions are generated out of RDF triples, e.g.

fb:en.pulp_fiction :hasActor fb:en.john_travolta .

Question: John Travolta is the actor of ...?Correct Answer: Pulp Fiction

http://bit.ly/WhoKnowsMovies





Game Based Ground Truth• WhoKnows? Movies has been played 690 times by 217

players• All 2,829 triples have been played at least once, 2,314

triples at least three times. • In total 8,308 questions have been played of which 4,716

have been answered correctly.• Overall result of relevance ranking:

http://yovisto.com/labs/iswc2012/



Evaluation of Fact Ranking Heuristics• Heuristics based on User-Based Entity Summarization (UBES),

i.e. heuristics (8) Frequency of features shared with ,neighborhood‘ entities with usage-based neighborhood determinationwith data from Freebase and HetRec2011 MovieLens2k dataset

• Comparison with results derived from Google Knowledge Graph (GKG) and random results

• Kendall τ rank correlation used to evaluate the orderings of GKG and UBES with our ground truth



Evaluation of Fact Ranking Heuristics

• Feature Ranking for the cast of a movie• Average difference of both, UBES and GKG to random is significant




Summary of Results

•LOD fact relevance can be determined via heuristics based on statistical, linguistic, and semantic features

•Ground Truth for fact relevance can be created with the help of a game based approach

•Ground truth dataset and evaluation dataset are publicly available

(Thalhammer, Knuth, Sack: Evaluating Entity Summarizations Using a Game-Based Ground Truth, ISWC 2012)




Contact:Harald SackHasso-Plattner-Institut für SoftwaresystemtechnikUniversität PotsdamProf.-Dr.-Helmert-Str. 2-3D-14482 Potsdam

Homepage: http://bit.ly/HaraldSack-HPI http://www.yovisto.com/Blog: http://moresemantic.blogspot.com/E-Mail: [email protected] Twitter: lysander07 / biblionomicon / yovisto

Summarization and Relevance of Linked Data Facts


http://www.yovisto.com

http://www.yovisto.com

http://moresemantic.blogspot.com

http://moresemantic.blogspot.com

mailto:[email protected]

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summarization and relevance of linked data facts

Education

leipziger semantic web

harald sack hassoplattner

institut potsdam

semantic web volume

harald sackhassoplattner

einstein rdf

type yago

einstein rdt