LinkSUM: Using Link Analysis to Summarize Entity Data

KIT – The Research University in the Helmholtz Association

INSTITUTE OF APPLIED INFORMATICS AND FORMAL DESCRIPTION METHODS (AIFB)

www.kit.edu

LinkSUM: Using Link Analysis to Summarize Entity Data

Andreas Thalhammer, Nelia Lasierra, and Achim Rettinger

16th International Conference on Web Engineering (ICWE 2016) 08.06.2016

Lugano

Institute of Applied Informatics and Formal

Description Methods (AIFB)

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Outline

Introduction

Approach: LinkSUM

Related Resources

Predicate Selection

Configuration

Evaluation

Quantitative

Qualitative

Conclusions

08.06.2016 LinkSUM: Using Link Analysis to Summarize Entity Data



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INTRODUCTION


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Motivation: Entity Summarization (I)

Examples for entities:

Movies:

Pulp Fiction

Kill Bill vol. 1

Books:

1984

A farewell to arms

People:

John Travolta

Arnold Schwarzenegger

etc.


Example for data about an entity:



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Motivation: Entity Summarization (II)


How to decide:

Which facts should we show?

Facts are unranked in the

knowledge base.

Entities have individual

features (even if they are of

the same type).



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Idea

Use link analysis for selecting facts.


Step 1: Select top-k important related resources.

Step 2: Select the most relevant connecting predicate.

Strongly relevance-oriented.

Lightweight.

Avoids redundancy.



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APPROACH: LINKSUM


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Related Resources (I)


Compute PageRank [1] scores (pr) of entities with (untyped) links that

occur in textual descriptions of entities (i.e., Wikipedia).

l(r) – set of incoming links of r.

c(r) – number of outgoing links of r.

d – damping factor (usually 0.85).

Example:

dbpedia:Category:English-language_films 220.961

dbpedia:Quentin_Tarantino 137.403

dbpedia:John_Travolta 105.771

dbpedia:Miramax_Films 993.986

... ...



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Related Resources (II)


Use Backlinks [2] for finding strong connections:

Example:

Pulp FictionQuentin

Tarantino

director

dbpedia:Quentin_Tarantino dbpedia:Roger_Avary

dbpedia:Bruce_Willis dbpedia:Tim_Roth

dbpedia:John_Travolta dbpedia:Ving_Rhames

dbpedia:Samuel_L._Jackson dbpedia:Amanda_Plummer

dbpedia:Harvey_Keitel dbpedia:Lawrence_Bender

dbpedia:Miramax_Films dbpedia:Sally_Menke

dbpedia:Uma_Thurman dbpedia:Maria_de_Medeiros

dbpedia:Andrzej_Sekuła dbpedia:Rosanna_Arquette

dbpedia:Christopher_Walken dbpedia:Eric_Stoltz



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Related Resources (III)


Combined score for related resources:

Linear combination.

Normalized PageRank scores.

Indicator function on the set of Backlinks of e (bl(e)).

Parameter α (alpha) to be estimated.



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Predicate Selection

Problem: multiple predicates connect two resources.

Approaches:

Frequency (FRQ)

#times the predicate is used

Exclusivity (EXC)

1 / (N + M)

Description (DSC):

#domain + #range + #label


and combinations

of those, e.g. (FRQ * EXC)

Pulp

Fiction

Quentin

Tarantino

starring

director



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CONFIGURATOIN


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Dataset and Measure


Introduced in Gunaratna et al. [3].

Contains human-created summaries of 50 entities

(DBpedia 3.9, outgoing relations).

Includes seven top-5 and seven top-10 summaries for each entity.

The dataset was created by 15 experts from the Semantic Web field.

Used measure:



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Configuration


top-5 top-10

Parameters:

α – linear combination of PageRank and Backlinks.

Predicate selection – combinations of FRQ, EXC, and DSC.

Best configuration: α = 0.8 / α = 0.9, FRQ*EXC*DSC



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EVALUATION


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Setup: Quantitative Evaluation

Compare results to the FACES system (introduced in [3]).

FACES:

Semantically diverse predicates via clustering.

Basic ranking heuristic for selecting cluster representatives.

Dataset and quality measure: like in configuration.

Evaluated configurations:

config-1: α = 0.8, FRQ*EXC*DSC

config-2: α = 0.9, FRQ*EXC*DSC

Significance testing:

Wilcoxon Signed-Rank Test with two tails.




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Results: Quantitative Evaluation


SO: Subject-Object pairs (predicates not considered).

SPO: Full triple.

Significance with respect to both LinkSUM configurations (p < 0.05).

Significance with respect to the best LinkSUM configuration (p < 0.05).

Standard deviation.

SD



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Setup: Qualitative Evaluation


Scenario: Search Engine Result Page (SERP).

20 users, 10 entities (from the FACES dataset).



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Results: Qualitative Evaluation


In some cases the task is

subjective.

Reasons for:

Selection

- the presented related

resources are relevant for

the entity.

Rejection

- redundancy.

- related resources do not

characterize the entity.



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CONCLUSIONS


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Conclusions


LinkSUM improves on the state of

the art in entity summarization.

LinkSUM is lightweight and can be

applied in other scenarios, e.g.

Web sites with semantic

annotations.

Semantic MediaWikis.

Entity summarization in SERP

scenarios:

Focus should be on selecting

relevant resources.

Redundancies at the object level

should be avoided.



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Lessons learned and future directions

Selected facts should provide information about the entity (main

difference to recommender systems).

Summaries and rankings are often subjective (but general tendencies

are noticeable).

Established quantitative evaluation datasets are still missing (although

different research efforts already targeted that problem).

Presentation aspects are very important (these should be neutralized in

qualitative evaluation [4]).

Personalization and contextualization of entity summaries is becoming

an important field (LinkSUM can serve as a basis).




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Questions?

[email protected]

@thalhamm

mailto:[email protected]



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Resources

DBpedia PageRank dataset:

http://people.aifb.kit.edu/ath/#DBpedia_PageRank

LinkSUM:

http://km.aifb.kit.edu/services/link/

International Workshop on Summarizing and Presenting Entities and

Ontologies:

http://km.aifb.kit.edu/ws/sumpre2015


FACES:

http://wiki.knoesis.org/index.php/FACES


http://people.aifb.kit.edu/ath/

http://km.aifb.kit.edu/services/link/



http://wiki.knoesis.org/index.php/FACES



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References

1. S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search

engine. In Proceedings of the 7th International Conference on World Wide

Web 7. Elsevier, 1998.

2. J. Waitelonis and H. Sack. Towards exploratory video search using linked

data. Multimedia Tools and Applications, 59:645–672, 2012. 10.1007/s11042-

011-0733-1.

3. K. Gunaratna, K. Thirunarayan, and A. P. Sheth. FACES: Diversity-Aware

Entity Summarization Using Incremental Hierarchical Conceptual Clustering.

In Proceedings of the 29th AAAI Conf. Artificial Intelligence, 2015, Austin,

Texas, USA., 2015.

4. A. Thalhammer and S. Stadtmüller. SUMMA: A Common API for Linked Data

Entity Summaries. In Engineering the Web in the Big Data Era. Springer,

2015.


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