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Disambiguating named entities in natural-language text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective disambiguation, by harnessing context from knowledge bases and using a new form of coherence graph. It unifies prior(More)
We present YAGO2, an extension of the YAGO knowledge base, in which entities , facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 447 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95% of the facts in YAGO2. In this paper, we present the(More)
We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as(More)
Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic(More)
This paper describes an advanced search engine that supports users in querying documents by means of keywords, entities, and categories. Users simply type words, which are automatically mapped onto appropriate suggestions for entities and categories. Based on named-entity disambiguation, the search engine returns documents containing the query's entities(More)
We present AIDA, a framework and online tool for entity detection and disambiguation. Given a natural-language text or a Web table, we map mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base like DBpedia, Freebase, or YAGO. AIDA is a robust framework centred around collective disambiguation exploiting(More)
Knowledge bases (KB's) contain data about a large number of people, organizations, and other entities. However, this knowledge can never be complete due to the dynamics of the ever-changing world: new companies are formed every day, new songs are composed every minute and become of interest for addition to a KB. To keep up with the real world's entities,(More)
Inferring lexical type labels for entity mentions in texts is an important asset for NLP tasks like semantic role labeling and named entity disambiguation (NED). Prior work has focused on flat and relatively small type systems where most entities belong to exactly one type. This paper addresses very fine-grained types organized in a hierarchical taxonomy,(More)
Recent research has shown progress in achieving high-quality, very fine-grained type classification in hierarchical tax-onomies. Within such a multi-level type hierarchy with several hundreds of types at different levels, many entities naturally belong to multiple types. In order to achieve high-precision in type classification, current approaches are(More)
To advance the Web of Linked Data, mapping ambiguous names in structured and unstructured contents onto knowledge bases would be a vital asset. State-of-the-art methods for Named Entity Disambiguation (NED) face major trade-offs regarding efficiency/scalability vs. accuracy. Fast methods use relatively simple context features and avoid compu-tationally(More)