Extracting Keyphrases to Represent Relations in Social

Networks from Web

Junichiro Mori and Mitsuru IshizukaUniversiry of Tokyo

Yutaka MatsuoNational Institute of Advanced

Industrial Science and Technology

IJCAI-07

Abstract

• The goal is extracting the underlying relations between entities that are embedded in social networks.

• The algorithm automatically extracts labels that describe relations among entities.

• The algorithm– clusters similar entity pairs– underlying relations between entities are obtained

from results of clustering.

Introduction

• Social networks for AI and the Semantic Web – trust estimation– ontology construction– end-user ontology

• Building social networks– extraction of social networks automatically from vari

ous sources of information.• Flink : Web pages, e-mail messages, and publications

• Polyphonet [www06]

Introduction

• Explore underlying relations• Most automatic extraction methods are superficial approac

h

• Co-occurrence analysis

• Non-profound assessment

– Flink : provide a clue to the strength of relations– Polyphonet : defines four kinds of relations

• C5

• Co-Author, Co-Lab, Co-Proj, Co-Conf

Related Work

• A supervised method– Need large annotated corpora– to gather the domain specific knowledge– a priori to define extracted relations

• Ontology population (Semantic annotation)– Pattern-based approaches– context-based approaches

• Web is highly heterogeneous and unstructured– In this paper

• context-based• a bag-of-words of context [Turney, 2005]

Method - Concept (1/4)

• The social network was extracted according to co-occurrence of entities on the Web.

Method - Concept (2/4)

• Given entity pairs in the social network– discover relevant keyphrases

• to analyze the surrounding local context (Co-occur on the Web )

• keyword extraction

Method - Concept (3/4)

• The keywords are ordered according to TF-IDF-based scoring

Method - Concept (4/4)

• Hypothesize:– the local contexts of entity pairs in the Web are similar,

the entity pairs share a similar relation.– [Harris, 1968; Schutze, 1998]: words are similar to the

extent that their contextual representations are similar.

• According to that hypothesis– the method clusters entity pairs according to the simila

rity of their collective contexts.– each cluster represents a different relation and each en

tity pair in a cluster is an instance of similar relation.

Method - Procedure

Method - Context Model and Similarity Calculation

• Ci,j (n,m) = t1, ..., tN

– A context model Ci,j of an entity pair (ei, ej)

– N terms t1, ..., tN that are extracted from the context of an entity pair

– m is the number of intervening terms between ei and ej

– n is the number of words to the left and right of either entity.

– a feature weight of ti : TF-IDF

• TF : term frequency of term ti in the contexts

• IDF : log(|C|/df(ti))+1

Method - Clustering and Label Selection

• TFIDF-based cosine similarity • Hierarchical agglomerative clustering

– complete linkage– The similarity between the clusters CL1 , CL2 is evalu

ated by considering the two most dissimilar elements

• With a cluster CL’s labels l1, ..., ln scored according to the term relevancy, an entity pair, ei and ej , that belongs to the CL can be regarded as holding the relations described by l1, ..., ln.

Experiment – 1/3

• Test Data– 143 distinct entity pairs from a political social netwo

rk• pair of a politician and a geo-political entity

– 421 entity pairs from a researcher network• pair of Japanese AI researchers

• Context model of each entity pair– 100 Web pages– NP and Noun by part-of-speeches (POS) – exclude stop words

Experiment – 2/3

• Clustering– complete-linkage agglomerative

• five distinct clusters for the political social network

• twelve distinct clusters for the researcher network

• two human subjects– three or fewer possible labels for each pairs– a cluster label

• the most frequent term among the manually assigned relation labels of entity pairs in the cluster.

Experiment – 3/3

Evaluation

• For each cluster cl– EPcl,correct : manually assigned relation labels include t

he label of cluster cl– EPcl,total : the number of entity pairs in the cluster cl

• For each relation l– EPl,correct : the relation label l whose cluster label is l– EPl,total : the number of entity pairs have the relation l

abel l

Evaluation

Conclusions

• Automatically extracting labels– relations between entities in social networks– Unsupervised and domain independent

• Utilizing the Web to obtain the collective contexts– Semantic Web– Web mining

• Future– other types of social networks– enriching social networks