Towards Open Data for Linguistics: Linguistic Linked Data

Chiarcos, Christian; McCrae, John P.; Cimiano, Philipp; Fellbaum, Christiane

doi:10.1007/978-3-642-31782-8_2

Cited by 44 publications

(41 citation statements)

References 21 publications

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“…Similar encoding strategies are found in other domains, such as linguistics [7] and in multimedia [2], and also with other forms of data such as temporal [28] and streaming data [31].…”

Section: The Default Data Model?mentioning

confidence: 69%

“…Successful end-to-end models need to take this topological variance into account so they can recognize that similar information is expressed in different ways. 7 Even then, there may be cases where the respective topologies are simply too different, and no learning algorithm could learn the required mapping without supervision. In this case, however, we can still use active learning: letting a user provide minimal feedback to the learning process, without hand-designing a complete mapping between different data-sources.…”

Section: Differently-modelled Knowledgementioning

confidence: 99%

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The knowledge graph as the default data model for learning on heterogeneous knowledge

Wilcke

Bloem

Boer

2017

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Abstract. In modern machine learning, raw data is the preferred input for our models. Where a decade ago data scientists were still engineering features, manually picking out the details we thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal more ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of expressing heterogeneous knowledge naturally in various domains, in as usable a form as possible, and satisfying as many use cases as possible. In this position paper, we argue that the knowledge graph is a suitable candidate for this data model. We further describe current research and discuss some of the promises and challenges of this approach.

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“…Similar encoding strategies are found in other domains, such as linguistics [7] and in multimedia [2], and also with other forms of data such as temporal [28] and streaming data [31].…”

Section: The Default Data Model?mentioning

confidence: 69%

Section: Differently-modelled Knowledgementioning

confidence: 99%

The knowledge graph as the default data model for learning on heterogeneous knowledge

Wilcke

Bloem

Boer

2017

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“…The JRC-Names linked dataset is served on the web via the EU Open Data Portal with: an RDF dump file 14 , a public SPARQL endpoint 15 and dereferenceable URIs 16 . Additionally, the resource is registered on the datahub plateform 17 .…”

Section: Dataset Features and Web Accessmentioning

confidence: 99%

“…With respect to Natural Language Processing, adopting linked data principles for the distribution of linguistic resources can bring many benefits, including: resource interoperability, both at a structural and conceptual level; resource integration (via interlinking); and resource maintenance (via a rich ecosystem of technologies allowing, among other things, continuous updating) [14]. Based on such insights, members of the NLP and SW communities -in particular the Open Linguistics Working Group and the W3C OntologyLexica Community Group 2 -joined efforts for the definition of best practices [27] and the design of principled models for the representation of linguistic information [46,42].…”

Section: Introductionmentioning

confidence: 99%

JRC-Names: Multilingual entity name variants and titles as Linked Data

Ehrmann

Jacquet

Steinberger

2016

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Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/‫ﺒﻨﻴﺎﻤﻴﻥ‬ Netanyahu/Netanjahu/Nétanyahou/Netahny/Нетаньяху/‫.)ﻨﺘﻨﻴﺎﻫﻭ‬ This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.

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“…Benefits of LLOD are also identified in linking through URIs, federation, dynamic linking between resources (Chiarcos et al, 2013b).…”

Section: Linguistic Linked Open Data (Llod) Integrationmentioning

confidence: 99%

Terminology and Knowledge Representation. Italian Linguistic Resources for the Archaeological Domain

Buono¹,

Monteleone²,

Elia³

2014

Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

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Knowledge representation is heavily based on using terminology, due to the fact that many terms have precise meanings in a specific domain but not in others. As a consequence, terms becomes unambiguous and clear, and at last, being useful for conceptualizations, are used as a starting point for formalizations. Starting from an analysis of problems in existing dictionaries, in this paper we present formalized Italian Linguistic Resources (LRs) for the Archaeological domain, in which we integrate/couple formal ontology classes and properties into/to electronic dictionary entries, using a standardized conceptual reference model. We also add Linguistic Linked Open Data (LLOD) references in order to guarantee the interoperability between linguistic and language resources, and therefore to represent knowledge.

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Towards Open Data for Linguistics: Linguistic Linked Data

Cited by 44 publications

References 21 publications

The knowledge graph as the default data model for learning on heterogeneous knowledge

The knowledge graph as the default data model for learning on heterogeneous knowledge

JRC-Names: Multilingual entity name variants and titles as Linked Data

Terminology and Knowledge Representation. Italian Linguistic Resources for the Archaeological Domain

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