Autonomously semantifying wikipedia

By Fei Wu, Daniel S. Weld. 2007

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication

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Citations in the corpus

Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction (2013, methodology): “It was also used to train open extractors based on Wikipedia infoboxes and corresponding sentences (Wu and Weld, 2007; Wu and Weld, 2010).”
Open Information Extraction Using Wikipedia (2010, methodology): “Specifically, WOE generates relation-specific training examples by matching Infobox1 attribute values to corresponding sentences (as done in Kylin (Wu and Weld, 2007) and Luchs (Hoffmann et al., 2010)), but WOE abstracts these examples to relationindependent training data to learn an unlexicalized e”
Effective Crowd Annotation for Relation Extraction (2016, methodology): “In response, researchers developed methods for distant supervision (DS) in which a knowledge base such as Wikipedia or Freebase is used to automatically tag training examples from a text corpus (Wu and Weld, 2007; Mintz et al., 2009).”
DELIS Dynamically Evolving , Large-scale Information Systems Integrated Project Member of the FET Proactive Initiative Complex Systems DELIS-TR-594 Yago : A Large Ontology from Wikipedia and WordNet (2007, background): “[148] aim at filling in missing values in Wikipedia infoboxes and achieve a remarkable precision of 73% to 97%.”
Types of Common-Sense Knowledge Needed for Recognizing Textual Entailment (2011, background): “Likewise, most knowledge extraction systems focus on extracting one speciﬁc kind of knowledge from text, often factual relationships (Banko et al., 2007; Suchanek et al., 2007; Wu and Weld, 2007), although other specialized extraction techniques exist as well.”
Automatically Generating Wikipedia Articles: A Structure-Aware Approach (2009, background): “Instances of this work include information extraction, ontology induction and resource acquisition (Wu and Weld, 2007; Biadsy et al., 2008; Nastase, 2008; Nastase and Strube, 2008).”
Relation Extraction Using Distant Supervision (2018, background): “The authors of [68] developed a self-supervised classifier that uses information from Wikipedia pages and their infoboxes to predict article classes and to extract attribute values for infobox refinement.”
Learning classifiers from only positive and unlabeled data (2008, methodology): “Papers using this general approach include [24, 20, 23], and the idea has been rediscovered independently a few times, most recently in [ 22, Section 2.4 ].”
Decoding Wikipedia Categories for Knowledge Acquisition (2008, background): “Kylin ( Wu & Weld, 2007 ) and the system presented by Nguyen et al. (2007) use Wikipedia infoboxes n snippets of structured information about a company, person, region, and so on n as training data, and learn how to ll in similar templates for pages that do not have such information.”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, background): “[43], KYLIN [55], and also our own YAGO project (with first results given in [51] and new techniques presented in this article).”
Learning multilingual named entity recognition from Wikipedia (2013, background): “Wu and Weld [88] learn the relationship between information in Wikipedia’s infoboxes and the associated article text, and use it to extract similar types of information from the web.”
DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia (2015, methodology): “KYLIN [44] is a system which autonomously extracts structured data from Wikipedia and uses self-”
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations (2011, methodology): “The KYLIN system aplied weak supervision to learn relations from Wikipedia, treating infoboxes as the associated database (Wu and Weld, 2007); Wu et al. (2008) extended the system to use smoothing over an automatically generated infobox taxon- omy.”
Automatically refining the wikipedia infobox ontology (2008, methodology): “Furthermore, machine-learning systems, such as Kylin [ 35 ], can use these infoboxes as training data, and then accurately extra ct even more triples from Wikipedia’s natural-language text.”
Leveraging Linguistic Structure For Open Domain Information Extraction (2015, background): “The most common of these are distantly supervised relation extractors (Craven and Kumlien, 1999; Wu and Weld, 2007; Mintz et al., 2009; Sun et al., 2011), and rule based systems (Soderland, 1997; Grishman and Min, 2010; Chen et al.”
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia (2016, methodology): “The goal of automatically extracting structured information from unstructured Wikipedia text was ﬁrst advanced by Wu and Weld (2007).”
Methods for exploring and mining tables on Wikipedia (2013, background): “Examples include systems that extract relational tuples from Web text [3–7] or from semi-structured Web sources like Wikipedia infoboxes [12, 13] or lists [14, 15].”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, background): “Furthermore, others (Wu and Weld, 2007) have shown how to associate language models with such relations.”