An Unsupervised Semantic Model for Arabic/French Terminology Extraction

2021 ◽  
pp. 49-59
Author(s):  
Setha Imene ◽  
Aliane Hassina
Keyword(s):  
Semantic Model ◽  
Terminology Extraction

Modelling of Semantic Processes and Evolutional Semantic Model

2019 ◽  
Vol 26 (27) ◽  
pp. 197-210 ◽  
Author(s):  
Anastasia Fedorova
Keyword(s):  
Key Words ◽  
Dynamic Models ◽  
Scientific Literature ◽  
Semantic Model ◽  
Vast Array ◽  
Special Procedure ◽  
Semantic Models ◽  
Semantic Evolution ◽  
Semantic Processes ◽  
The Way

In Linguistics the terms model and modelling have a vast array of meanings, which depends on the purpose and the object, and the type of the scientific research. The article is dedicated to the investigation of a special procedure of semantic processes modelling, deducing and substantiating the notion “evolutional semantic model”, the content and operational opportunities of which differ drastically from the essence and purpose of the known from the scientific literature phenomenon of the same name. In the proposed research this variety of modelling is oriented towards the description of the dynamics of the legal terms content loading, the estimation of possible vectors of the semantic evolution on the way of its terminalization/determinalization. The evolutional model of semantics has here as its basis the succession of sememes or series of sememes, the order of which is determined with accounting of a number of parameters. The typical schemes of the meaning development, illustrated by the succession of sememes, are considered to be the models of semantic laws (evolutional semantic models = EMS). Their function is the explanation of the mechanism and the order of the stages of the semantic evolution of the system of the words which sprung from one root on the way of its legal specialization, and, therefore, the proposed in the paper experience of semantic laws modelling differs from the expertise of the “catalogue of semantic derivations”, proposed by H. A. Zaliznjak, which doesn’t have as its purpose the explanation of meaning displacements, and from the notion of semantic derivation, models of derivation, dynamic models, worked out by O. V. Paducheva, which also only state such a displacement, without proving its reality. Key words: evolutional semantic model (EMS), modelling, semantic law, sememe, pre(law).

The new semantic model in ASIS for Ada 2005

2009 ◽  
Vol 29 (3) ◽  
pp. 127-132
Author(s):  
Jean-Pierre Rosen ◽  
Tucker Taft
Keyword(s):  
Semantic Model

scholarly journals A Semantic Model for Indexing in the Hidden Web

2021 ◽  
Vol 190 ◽  
pp. 324-331
Author(s):  
Larisa Ismailova ◽  
Viacheslav Wolfengagen ◽  
Sergey Kosikov
Keyword(s):  
Semantic Model ◽  
Hidden Web

scholarly journals ORFFM: An Ontology-Based Semantic Model of River Flow and Flood Mitigation

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 44003-44031
Author(s):  
Muhammad Hussain Mughal ◽  
Zubair Ahmed Shaikh ◽  
Asim Imdad Wagan ◽  
Zahid Hussain Khand ◽  
Saif Hassan
Keyword(s):  
River Flow ◽  
Flood Mitigation ◽  
Semantic Model

Ontology-Based Organizational and Technical Components Semantic Model Development

2020 ◽  
Author(s):  
Alexander Afanasyev ◽  
Nikolay Voit ◽  
Maria Ukhanova ◽  
Sergey Kirillov ◽  
Semen Bochkov
Keyword(s):  
Model Development ◽  
Semantic Model

scholarly journals Improvement of Terminology Extraction Method for Specific Patent Search

2014 ◽  
Vol 35 ◽  
pp. 879-885
Author(s):  
Kyoko Yanagihori ◽  
Koji Tanaka ◽  
Kazuhiko Tsuda
Keyword(s):  
Extraction Method ◽  
Terminology Extraction ◽  
Patent Search

scholarly journals MMoOn Core – the Multilingual Morpheme Ontology

Semantic Web ◽  
2020 ◽  
pp. 1-29
Author(s):  
Bettina Klimek ◽  
Markus Ackermann ◽  
Martin Brümmer ◽  
Sebastian Hellmann
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Semantic Model ◽  
Lexical Resources ◽  
Domain Specific ◽  
Word Forms ◽  
Language Data ◽  
Core Ontology ◽  
Machine Readable ◽  
Rapid Emergence

In the last years a rapid emergence of lexical resources has evolved in the Semantic Web. Whereas most of the linguistic information is already machine-readable, we found that morphological information is mostly absent or only contained in semi-structured strings. An integration of morphemic data has not yet been undertaken due to the lack of existing domain-specific ontologies and explicit morphemic data. In this paper, we present the Multilingual Morpheme Ontology called MMoOn Core which can be regarded as the first comprehensive ontology for the linguistic domain of morphological language data. It will be described how crucial concepts like morphs, morphemes, word forms and meanings are represented and interrelated and how language-specific morpheme inventories can be created as a new possibility of morphological datasets. The aim of the MMoOn Core ontology is to serve as a shared semantic model for linguists and NLP researchers alike to enable the creation, conversion, exchange, reuse and enrichment of morphological language data across different data-dependent language sciences. Therefore, various use cases are illustrated to draw attention to the cross-disciplinary potential which can be realized with the MMoOn Core ontology in the context of the existing Linguistic Linked Data research landscape.

scholarly journals A resource to explore the discovery of rare diseases and their causative genes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Friederike Ehrhart ◽  
Egon L. Willighagen ◽  
Martina Kutmon ◽  
Max van Hoften ◽  
Leopold M. G. Curfs ◽  
...  
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Genetic Background ◽  
Gene Discovery ◽  
Google Scholar ◽  
Semantic Model ◽  
Causative Gene ◽  
Scientific Publications ◽  
Monogenic Diseases ◽  
First Time

AbstractHere, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166 rare monogenic diseases and linked them to 3163 causative genes, annotated with OMIM and Ensembl identifiers and HGNC symbols. The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com, and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. This dataset relies on publicly available data and publications with a PubMed identifier, but by our effort to make the data interoperable and linked, we can now analyse this data. Our analysis revealed the timeline of rare disease and causative gene discovery and links them to developments in methods.

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