Linked Lexical Knowledge Bases: Foundations and Applications

Gurevych, Iryna; Eckle‐Kohler, Judith; Matuschek, Michael

doi:10.2200/s00717ed1v01y201605hlt034

Cited by 8 publications

(10 citation statements)

References 136 publications

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“…An implementation of our method along with induced lexical resources is available online. 6 2 Related Work Methods based on resource linking surveyed by Gurevych et al (2016) gather various existing lexical resources and perform their linking to obtain a machine-readable repository of lexical semantic knowledge. For instance, BabelNet (Navigli and Ponzetto, 2012) relies in its core on a linking of WordNet and Wikipedia.…”

Section: Introductionmentioning

confidence: 99%

Automatic Induction of Synsets from a Graph of Synonyms

Ustalov

Panchenko

Biemann

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.

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Section: Introductionmentioning

confidence: 99%

Automatic Induction of Synsets from a Graph of Synonyms

Ustalov

Panchenko

Biemann

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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“…In linguistic terminology, this lexical relation is called hypernymy: an edge connects a more special class, called hyponym, with a generalized class, called hypernym. [203] gives an overview of this kind of lexical resources. Further examples of (potentially unclean) taxonomies include the Wikipedia category system or product catalogs.…”

Section: Taxonomies (Aka Class Hierarchies)mentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

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Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

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“…Word Sense Alignment. Gurevych et al (2016) define Word Sense Alignment as linking senses or concepts that has an identical meaning from multiple Lexical Knowledge Bases (LKB). There has been a lot of work with various goals such as aligning WordNet, Cyc, and VerbNet for building knowledge representation (Crouch and King, 2005), aligning FrameNet, VerbNet, and WordNet for semantic parsing (Shi and Mihalcea, 2005), and building large-scale LKB alignments (Matuschek, 2015;Gurevych et al, 2012;Navigli and Ponzetto, 2012).…”

Section: Related Workmentioning

confidence: 99%

Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding

Putri¹,

Hong²,

Myaeng³

2019

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

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Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.

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Linked Lexical Knowledge Bases: Foundations and Applications

Cited by 8 publications

References 136 publications

Automatic Induction of Synsets from a Graph of Synonyms

Automatic Induction of Synsets from a Graph of Synonyms

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding

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