THE SUBSYSTEM GROUPING SCHEME USING USE CASE DEPENDENCY GRAPH AND DOMAIN-SPECIFIC SEMANTIC MODEL FOR LARGE COMPLEX SYSTEMS

Author(s):  
NANCHAYA KHRUEAHONG ◽  
WIWAT VATANAWOOD
Semantic Web ◽  
2020 ◽  
pp. 1-29
Author(s):  
Bettina Klimek ◽  
Markus Ackermann ◽  
Martin Brümmer ◽  
Sebastian Hellmann

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.


2021 ◽  
Vol 103 (2) ◽  
Author(s):  
S. Belga Fedeli ◽  
Y. V. Fyodorov ◽  
J. R. Ipsen

1960 ◽  
Vol 269 (4) ◽  
pp. 274-298
Author(s):  
Mihajlo D. Mesarović

Author(s):  
Srdjan Zivkovic ◽  
Krzystof Miksa ◽  
Harald Kühn

It has been acknowledged that model-based approaches and domain-specific modeling (DSM) languages, methods and tools are beneficial for the engineering of increasingly complex systems and software. Instead of general-purpose one-size-fits-all modeling languages, DSM methods facilitate model-based analysis and design of complex systems by providing modeling concepts tailored to the specific problem domain. Furthermore, hybrid DSM methods combine single DSM methods into integrated modeling methods, to allow for multi-perspective modeling. Metamodeling platforms provide flexible means for design and implementation of such hybrid modeling methods and appropriate domain-specific modeling tools. In this paper, we report on the conceptualization of a hybrid DSM method in the domain of network physical devices management, and its implementation based on the ADOxx metamodeling platform. The method introduces a hybrid modeling approach. A dedicated DSM language (DSML) is used to model the structure of physical devices and their configurations, whereas the formal language for knowledge representation OWL2 is used to specify configuration-related constraints. The outcome of the work is a hybrid, semantic technology-enabled DSM tool that allows for efficient and consistency-preserving model-based configuration of network equipment.


2015 ◽  
Vol 48 (1) ◽  
pp. 301-305 ◽  
Author(s):  
Mark Könnecke ◽  
Frederick A. Akeroyd ◽  
Herbert J. Bernstein ◽  
Aaron S. Brewster ◽  
Stuart I. Campbell ◽  
...  

NeXus is an effort by an international group of scientists to define a common data exchange and archival format for neutron, X-ray and muon experiments. NeXus is built on top of the scientific data format HDF5 and adds domain-specific rules for organizing data within HDF5 files, in addition to a dictionary of well defined domain-specific field names. The NeXus data format has two purposes. First, it defines a format that can serve as a container for all relevant data associated with a beamline. This is a very important use case. Second, it defines standards in the form of application definitions for the exchange of data between applications. NeXus provides structures for raw experimental data as well as for processed data.


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