Danyun Xu, Gong Cheng*, Yuzhong Qu Generating and Characterizing Gold-Standard Entity Summaries: A Study of DBpedia Danyun Xu, Gong Cheng*, Yuzhong Qu

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Introduction Why Entity-centric structured data: Google’s Knowledge Graph Entity summarization Lack gold-standard entity summaries in evaluation What Present and evaluate several algorithms for automatically generating (near-) gold-standard summaries Characterize the generated gold-standard summaries

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Related Work Algorithms Evaluation Rank properties Rank features Intrinsic method Extrinsic method

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Data Set Dbpedia English version of DBpedia 3.7(wiki.dbpedia.org/Downloads37) 42.3 million RDF triples, 3.77 million entities Class 10 classes Almost pairwise disjoint

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Generating Gold-Standard Entity Summaries Basic Idea (Extended Abstracts) Automatically identifies the features of an entity that are mentioned in its textual abstract Algorithms Evaluation

Generating Gold-Standard Entity Summaries Algorithms Preprocess Remove “;” … and “the”… Split phrases: PopulatedPlace Populated Place Lowercase Optional stemming Identify SEQ: a sequence SET_ALL: a set, all the words SET_ANY: a set, any word

Generating Gold-Standard Entity Summaries Evaluation 10 entities from each class, each entity has more than 10 features Manually construct gold-standard entity summaries

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Characterizing Gold-Standard Entity Summaries Lengths Preference for Properties Preference for Diverse Properties Preference for Property Pairs Preference for Property Values

Characterizing Gold-Standard Entity Summaries Length Set maximum length Length varies widely Ratio in a narrower range

Characterizing Gold-Standard Entity Summaries Preference for Properties Name length Popularity Variety

Characterizing Gold-Standard Entity Summaries Preference for Properties Name length Properties with short names are preferable

Characterizing Gold-Standard Entity Summaries Preference for Properties Popularity Web (Bing) Data set Properties frequently seen in the data set are considerably preferable, Web-based popularity of a property seems not a strong indicator of preference Data set Web

Characterizing Gold-Standard Entity Summaries Preference for Properties Variety “familyName” vs “gender” the variety and popularity of a property in the data set are equally effective indicators of preference

Characterizing Gold-Standard Entity Summaries Preference for Diverse Properties diversify a summary the number of distinct properties in the summary/the number of distinct properties in the original description the number of distinct properties/number of features in summary gold-standard entity summaries are highly diversified

Characterizing Gold-Standard Entity Summaries Preference for Property Pairs String Similarity Co-occurrence

Characterizing Gold-Standard Entity Summaries Preference for Property Pairs String Similarity string similarity is not an effective indicator Co-occurrence a pair of properties that frequently co-occur in the data set also tend to be selected, Web-based degree of co-occurrence is a notable indicator of preference

Characterizing Gold-Standard Entity Summaries Preference for Property Values informativeness confirm the effectiveness of selecting rarely seen features into a summary

Introduction Related Work Data Set Generating Gold-Standard Entity Summaries Characterizing Gold-Standard Entity Summaries Conclusion

Conclusion Contribution Shortage Future work can hardly be applied to another data set that provides no textual abstract Future work optimizing the algorithm to generate more natural summaries Explore other factors

Thanks!