In: Proceedings of the 16th Pacific Rim international conference on artificial intelligence (PRICAI) In: International conference on language resources and evaluation-LREC 2006įumagalli M, Bella G, Giunchiglia F (2019) Towards understanding classification and identification. J Biomed Semant 9(1):4įrancopoulo G, George M, Calzolari N, Monachini M, Bel N, Pet M, Soria C (2006) Lexical markup framework (LMF). In: Proceedings of the ninth international conference on language resources and evaluation (LREC-2014), Reykjavik, Icelandįaria D, Pesquita C, Mott I, Martins C, Couto FM, Cruz IF (2018) Tackling the challenges of matching biomedical ontologies. Semant Web 6(4):371–378Įhrmann M et al (2014) Representing multilingual data as linked data: the case of BabelNet 2.0. Stud Health Technol Inf 121:279Įckle-Kohler J, McCrae JP, Chiarcos C (2015) LemonUby-a large, interlinked, syntactically-rich lexical resource for ontologies. Nucleic Acids Res 32(Suppl 1):D267–D270ĭonnelly K (2006) SNOMED-CT: the advanced terminology and coding system for eHealth. In: The diversity workshop at the European conference on artificial intelligenceīodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Web Semant Sci Serv Agents World Wide Web 43(1):1–17īella G, Zamboni A, Giunchiglia F (2016) Domain-based sense disambiguation in multilingual structured data. Support for Diversicon is already integrated into two of the most popular ontology matcher applications, a fact that we exploit to validate the framework and demonstrate its use on a example study that evaluates the effect of several common-sense and domain knowledge resources on a medical ontology matching task.īella G, Giunchiglia F, McNeill F (2017) Language and domain aware lightweight ontology matching. The major components of the framework are: (1) an API and domain knowledge model that allow applications to retrieve domain knowledge through a common interface from a diversity of resource types, (2) implementations of the API for some of the most commonly used symbolic and statistical knowledge sources, (3) a domain-aware knowledge base that helps integrate static lexico-semantic resources, and (4) an online catalogue that either hosts or links to the existing resources from multiple domains. This paper presents the open-source Diversicon Framework that helps application developers in finding, integrating, and accessing lexical domain knowledge, both symbolic and statistical, in a unified manner. The heterogeneity of such resources is, however, a major obstacle to their efficient use, especially in combination. For the analysis of domain-specific data, specialised knowledge resources (terminologies, grammars, word vector models, lexical databases) are necessary. taxonomical information: generalizations (i.e., more general concepts) and specializations (i.e.Natural language understanding is a key task in a wide range of applications targeting data interoperability or analytics.definitions (often multiple) in each language.What makes WordAtlas special is its linkage between concepts and words in hundreds of languages: WordAtlas provides millions of lexicalizations for each language, from common nouns, adjectives, verbs and adverbs, to hundreds of thousands of technical terms and millions of named entities, such as people, locations, organizations and products.Įach meaning comes with a wealth of information, including: Roberto Navigli’s lab at the Sapienza University of Rome. It greatly enhances BabelNet®, the award-winning multilingual semantic network, thanks to the know-how of years of research in computational linguistics in Prof. WordAtlas is the next-generation multilingual knowledge graph. They organize knowledge into a coherent network of meanings and they enable Artificial Intelligence applications which exploit this knowledge to perform text understanding. Knowledge bases and knowledge graphs are the 21st century counterpart of dictionaries in previous centuries. WordAtlas: a huge multilingual knowledge graph
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