BipRank: Ranking and Summarizing RDF Vocabulary Descriptions
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ws .nju.ed u.cn BipRank: Ranking and Summarizing RDF Vocabulary Descriptions Gong Cheng 1 , Feng Ji 2 , Shengmei Luo 2 , Weiyi Ge 1 , Yuzhong Qu 1 1 State Key Laboratory for Novel Software Technology, Nanjing University, China 2 Communication Services R&D Institute, ZTE Corporation, China Presented at JIST2011
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Transcript of BipRank: Ranking and Summarizing RDF Vocabulary Descriptions
ws .nju.edu.cn
BipRank: Ranking and SummarizingRDF Vocabulary Descriptions
Gong Cheng1, Feng Ji2, Shengmei Luo2, Weiyi Ge1, Yuzhong Qu1
1State Key Laboratory for Novel Software Technology, Nanjing University, China2Communication Services R&D Institute, ZTE Corporation, China
Presented at JIST2011
Gong Cheng (程龚 ) [email protected] 2 of 25
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Outline
Introduction
Salience measurement
Vocabulary summarization
Conclusions
Gong Cheng (程龚 ) [email protected] 3 of 25
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Vocabularies and Linked Data
Linked Data
Vocabularies Your own vocabulary
Reuse
Gong Cheng (程龚 ) [email protected] 8 of 25
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Vocabulary summarization
Vocabulary summarization = ranking and selecting RDF sentences
Gong Cheng (程龚 ) [email protected] 9 of 25
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Outline
Introduction
Salience measurement
Vocabulary summarization
Conclusions
Gong Cheng (程龚 ) [email protected] 10 of 25
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A bipartite view of vocabulary description
Gong Cheng (程龚 ) [email protected] 13 of 25
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BipRank
type-A behavior
type-B behavior
Next step Current stepUniform?
Gong Cheng (程龚 ) [email protected] 15 of 25
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p(s|u)
Frequency of Pattern(s)#RDF_sentence in the vocabulary that has the same pattern
Popularity of Pattern(s)#Vocabulary in the repository that has the same pattern
Gong Cheng (程龚 ) [email protected] 16 of 25
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Evaluation setting
Test cases9 moderate-sized vocabularies randomly selected from Falcons
Gold standardSalience given by 6 human experts
CompetitorsCp: Zhang et al. (WWW2007)
Our approachBipRank-U: pattern-unaware
BipRank-F: using pattern frequency
BipRank-P: using pattern popularity
MetricPearson product-moment correlation coefficient
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Outline
Introduction
Salience measurement
Vocabulary summarization
Conclusions
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Goodness of a summary
Salience
Query relevanceTextual similarity between query and summary
CohesionTerm overlap between RDF sentences
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Looking for the best summary
Multi-objective optimization
Single aggregate objective function
Solution: a greedy strategy
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Evaluation setting
Judges18 human experts
Test cases190 searches over 2,012 vocabularies crawled by Falcons
CompetitorsGeneric: Zhang et al. (WWW2007)
Our approachQR: query relevance
QR+S: query relevance + salience
QR+C: query relevance + cohesion
MetricRating on a 10-point scale
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Performance testing
Size of vocabulary
Size of summary Runtime
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Outline
Introduction
Salience measurement
Vocabulary summarization
Conclusions
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Conclusions
Salience measurementSentence-term graph
BipRank
Pattern of RDF sentence
Vocabulary summarizationSalience
Query relevance
Cohesion
Implemented in Falcons Ontology Searchhttp://ws.nju.edu.cn/falcons/ontologysearch/
