DBpedia spotlight: shedding light on the web of documents

By Pablo N. Mendes, Max Jakob, Andres Garcia-Silva, Christian Bizer. International Conference on Semantic Systems, 2011

1,360 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/af297bc9b0d2d36a048350139b203ad3bda6e1ee

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Citations in the corpus

Design Challenges for Entity Linking (2015, methodology): “We are also aware of other systems such as TagMe-2 (Ferragina and Scaiella, 2012), DBpedia Spotlight (Mendes et al., 2011) and WikipediaMiner (Milne and Witten, 2008).”
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases (2016, methodology): “This work relies on an entity linking system [29] to provide disambiguation function, but we do not address their limits here (e.”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, methodology): “For unstructured data, i.e., textual contents, the interlinking is typically done by linking named entities in the text to LOD sources with tools such as DBpedia Spotlight.”
Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “A named entity linking [Mendes et al. 2011] application uses the instances of well-known knowledge graph DBpedia [Auer et al.”
Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation (2019, methodology): “Results We compare our Tree-ACNN ED model with four popular systems: AGDISTIS [23], AIDA [19], Babelfy [24], and DBpedia Spotlight [25].”
Document Retrieval Model Through Semantic Linking (2017, background): “Cornolti et al. [6] provide a benchmarking framework for comparing annotation systems and analyse some of the popular systems such as TagMe [9], Wikipedia-miner [23], and DBpedia Spotlight [20].”
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (2018, methodology): “Entity Extraction: For each document, we extract named entities in the text and link them to their URI with the DBpedia Spotlight [Mendes et al.2011] entity linker.”
Semantic Multimedia Information Retrieval Based on Contextual Descriptions (2013, methodology): “To evaluate the conTagger regarding Hypothesis 1 we have compared the evaluation results to our own simple segment-based NER, NER by DBpedia Spotlight[11] and NER by the Wiki Machine[3].”
ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking (2012, neutral): “Systems performing such a task are available as well (e.g., Open Calais6, DBPedia Spotlight [26]).”
GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, methodology): “DBpedia Spotlight: One of the first semantic approaches [36] was published in 2011, this framework combines NER and NED approach based”
DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia (2015, background): “For example, DBpedia Spotlight [31] is an open source tool23 including a free web service that detects mentions of DBpedia resources in text.”
Improving efficiency and accuracy in multilingual entity extraction (2013, background): “Improving Ef.ciency and Accuracy in Multilingual Entity Extraction Joachim Daiber Computational Linguistics Rijksuniversiteit Groningen Groningen, The Netherlands daiber.joachim@gmail.com Chris Hokamp Lang. and Inf.”
A framework for benchmarking entity-annotation systems (2013, neutral): “DBpedia Spotlight Freie Universitaet Berlin Sa2W [14]”