TY  - JOUR
AU  - Girju, Roxana
AU  - Badulescu, Adriana
AU  - Moldovan, Dan I.
T1  - Automatic Discovery of Part-Whole Relations.
JO  - Computational Linguistics
PY  - 2006/
VL  - 32
IS  - 1
SP  - 83
EP  - 135
UR  - http://dblp.uni-trier.de/db/journals/coling/coling32.html#GirjuBM06
M3  - 
KW  - discovery
KW  - ol
KW  - ol_web2.0
KW  - ontology_learning
KW  - part-whole
KW  - relation
KW  - toread
KW  - toread_dbe
KW  - methods_relations
L1  - 
SN  - 
N1  - dblp
N1  - 
AB  - An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part–whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part–whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part–whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part–whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.
ER  -

TY  - CONF
AU  - Mori, Junichiro
AU  - Tsujishita, Takumi
AU  - Matsuo, Yutaka
AU  - Ishizuka, Mitsuru
A2  - 
T1  - Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts
T2  - International Semantic Web Conference
PB  - 
CY  - 
PY  - 2006/
M2  - 
VL  - 
IS  - 
SP  - 487
EP  - 500
UR  - 
M3  - 
KW  - extract
KW  - folksonomy
KW  - iin2009
KW  - relations
KW  - resources
KW  - social-networks
KW  - toread
KW  - ol_web2.0
KW  - toread_dbe
KW  - data_socialnetworks
KW  - methods_relations
L1  - 
SN  - 
N1  - 
N1  - 
AB  - Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.
ER  -

TY  - CHAP
AU  - Ruiz-Casado, Maria
AU  - Alfonseca, Enrique
AU  - Castells, Pablo
A2  - Montoyo, Andrés
A2  - Muñoz, Rafael
A2  - Métais, Elisabeth
T1  - Automatic Extraction of Semantic Relationships for WordNet by Means of Pattern Learning from Wikipedia
T2  - Natural Language Processing and Information Systems
PB  - Springer
CY  - Berlin / Heidelberg
PY  - 2005/
VL  - 3513
IS  - 
SP  - 233
EP  - 242
UR  - http://dx.doi.org/10.1007/11428817_7
M3  - 10.1007/11428817_7
KW  - ol_web2.0
KW  - patterns
KW  - wikipedia
KW  - wordnet
KW  - data_wikis
KW  - methods_relations
L1  - 
SN  - 
N1  - SpringerLink - Abstract
N1  - 
AB  - This paper describes an automatic approach to identify lexical patterns which represent semantic relationships between concepts, from an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 1200 new relationships that did not appear in WordNet originally. The precision of these relationships ranges between 0.61 and 0.69, depending on the relation.
ER  -