Proceedings of the 11th international conference on World Wide Web
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Recognizing named entities in tweets
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Collective entity linking in web text: a graph-based method
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Discovering User Interest on Twitter with a Modified Author-Topic Model
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Object matching in tweets with spatial models
Proceedings of the fifth ACM international conference on Web search and data mining
Adding semantics to microblog posts
Proceedings of the fifth ACM international conference on Web search and data mining
Robust disambiguation of named entities in text
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
LINDEN: linking named entities with knowledge base via semantic knowledge
Proceedings of the 21st international conference on World Wide Web
Probase: a probabilistic taxonomy for text understanding
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
LIEGE:: link entities in web lists with knowledge base
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
TwiNER: named entity recognition in targeted twitter stream
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A graph-based approach for ontology population with named entities
Proceedings of the 21st ACM international conference on Information and knowledge management
Entity linking at the tail: sparse signals, unknown entities, and phrase models
Proceedings of the 7th ACM international conference on Web search and data mining
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Twitter has become an increasingly important source of information, with more than 400 million tweets posted per day. The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base is called tweet entity linking. This task is of practical importance and can facilitate many different tasks, such as personalized recommendation and user interest discovery. The tweet entity linking task is challenging due to the noisy, short, and informal nature of tweets. Previous methods focus on linking entities in Web documents, and largely rely on the context around the entity mention and the topical coherence between entities in the document. However, these methods cannot be effectively applied to the tweet entity linking task due to the insufficient context information contained in a tweet. In this paper, we propose KAURI, a graph-based framework to collectively link all the named entity mentions in all tweets posted by a user via modeling the user's topics of interest. Our assumption is that each user has an underlying topic interest distribution over various named entities. KAURI integrates the intra-tweet local information with the inter-tweet user interest information into a unified graph-based framework. We extensively evaluated the performance of KAURI over manually annotated tweet corpus, and the experimental results show that KAURI significantly outperforms the baseline methods in terms of accuracy, and KAURI is efficient and scales well to tweet stream.