Regular path queries with constraints
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Autonomously semantifying wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Amplifying community content creation with mixed initiative information extraction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Building Community Wikipedias: A Machine-Human Partnership Approach
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
WiGipedia: A Tool for Improving Structured Data in Wikipedia
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
From DBpedia to Wikipedia: Filling the Gap by Discovering Wikipedia Conventions
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
BlueFinder: recommending wikipedia links using DBpedia properties
Proceedings of the 5th Annual ACM Web Science Conference
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DBpedia is the semantic mirror of Wikipedia. DBpedia extracts information from Wikipedia and stores it in a semantic knowledge base. This semantic feature allows complex semantic queries, which could infer new relations that are missing in Wikipedia. This is an interesting source of knowledge to increase Wikipedia content. But, what is the best way to add these new relations following the Wikipedia conventions? In this paper, we propose a path indexing algorithm (PIA) which takes the resulting set of a DBPedia query and discovers the best representative path in Wikipedia. We evaluate the algorithm with real data sets from DBpedia.