Zhishi.lemon: On Publishing Zhishi.me as Linguistic Linked Open Data

Fang, Zhijia; Wang, Haofen; Gracia, Jorge; Bosque-Gil, Julia; Ruan, Tong

doi:10.1007/978-3-319-46547-0_6

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Gracia et al [116] propose an extension of lemon that builds on early work from Montiel-Ponsoda et al [180] and introduces relations specific to modeling translations as linked data, such as TranslationSource and Trans-lationTarget as well as a set of categories to specify the type of translation, i.e., literal, cultural, lexical. This translation module is reused in other approaches, such as Zhishi.lemon [88] to represent links of translations from Chinese to other languages and resources. Such a translation module was the seed of the later variation and translation (vartrans) module of OntoLex-Lemon, 17 which in addition to represent translations is able to represent any other type of lexico-semantic relation, including terminological variants.…”

Section: Translation and Terminologymentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

Self Cite

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Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

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Section: Translation and Terminologymentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

Self Cite

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“…Modelling multilinguality in RDF has been a challenging aspect. According to Fang et al [38] (p. 51):…”

Section: Future Work and Conclusionmentioning

confidence: 99%

“…As shown in Table 1, Ontolex-Lemon was the only model which fulfilled all the modelling requirements. PWN and BabelNet have published their datasets in RDF format using the lemon model, as has Zhishi.lemon, the lexical realisation of Zhishi.me, a Chinese dataset in the Linked Open Data cloud, and the ABD, a machine translation platform with up to 40 language pairs [18] (p. 2), [38] (p. 47). Exemplars of other language resources converted to Linked Data using Ontolex-Lemon (or its predecessor, lemon) include "'Al-Qāmūs Al-Muhit", a Classical Arabic dictionary, the Pattern Dictionary of English Verbs, K Dictionary Series' German monolingual dictionary, and an exemplar dictionary article from "Dictionnaire étymologique de l'ancien francais" [39] (p. 325), [40] (pp.…”

mentioning

confidence: 99%

Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

Gillis-Webber

2018

Information

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The English-Xhosa Dictionary for Nurses (EXDN) is a bilingual, unidirectional printed dictionary in the public domain, with English and isiXhosa as the language pair. By extending the digitisation efforts of EXDN from a human-readable digital object to a machine-readable state, using Resource Description Framework (RDF) as the data model, semantically interoperable structured data can be created, thus enabling EXDN's data to be reused, aggregated and integrated with other language resources, where it can serve as a potential aid in the development of future language resources for isiXhosa, an under-resourced language in South Africa. The methodological guidelines for the construction of a Linguistic Linked Data framework (LLDF) for a lexicographic resource, as applied to EXDN, are described, where an LLDF can be defined as a framework: (1) which describes data in RDF, (2) using a model designed for the representation of linguistic information, (3) which adheres to Linked Data principles, and (4) which supports versioning, allowing for change. The result is a bidirectional lexicographic resource, previously bounded and static, now unbounded and evolving, with the ability to extend to multilingualism.Information 2018, 9, 274 2 of 30 into a simple digital resource, typically as images in JPEG format. However, by retrodigitising the artefact, converting it from a simple digital resource to a complex digital resource, it has the potential to become machine-interoperable. The form of complex digital objects can vary, with examples including: a collection of XML files; the same content but as HTML files with semantic markup to describe the content therein; as a dataset stored in a relational database or as Resource Description Framework (RDF). For the latter example, RDF "is a framework for representing information in the Web", published by the World Wide Web Consortium (W3C), where data is described in short statements, called triples, which are of the form subject-predicate-object [9]. A subject can be an internationalised resource identifier (IRI) or a blank node; an object can be an IRI, blank node, or a literal; and a predicate can be an IRI only [9].The lexical entry Abdomen can be expressed in the following short statements:Abdomen is a lexical entry.Abdomen is a word.Abdomen is a noun.Abdomen is an English term.Using Turtle syntax, a human-readable serialisation of RDF, the same short statements can be described in RDF: SubjectPredicate Object :abdomen :isA :lexicalEntry ; :isA :word ; :isA :noun ; :isLanguage :English .These short statements are visualised in Figure 1, using node-edge-node structure. Information 2018, 9, x FOR PEER REVIEW 2 of 29Despite EXDN being published more than seventy-five years ago, as a language resource for an under-resourced indigenous African language, its content is still of value [1] (p. 1). EXDN is in print form, bounded and static, and by digitising the dictionary, it is converted from an analogue resource into a simple digital resource, typically as images in JPEG format. Howev...

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“…This means although the Chinese knowledge base is relatively large in size, it is far from complete. Furthermore, most relations in Chinese knowledge bases are extracted from infoboxes, in the form of attribute-value pairs (Fang et al, 2016;Niu et al, 2011;Wang et al, 2013). Thus, the knowledge harvested from UGCs can be an important supplementary for these systems.…”

Section: Non-taxonomic Relation Extractionmentioning

confidence: 99%

Learning Fine-grained Relations from Chinese User Generated Categories

Wang

Fan

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.

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Zhishi.lemon: On Publishing Zhishi.me as Linguistic Linked Open Data

Cited by 6 publications

References 5 publications

Multilinguality and LLOD: A survey across linguistic description levels

Multilinguality and LLOD: A survey across linguistic description levels

Conversion of the English-Xhosa Dictionary for Nurses to a Linguistic Linked Data Framework

Learning Fine-grained Relations from Chinese User Generated Categories

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