A generic metamodel for data extraction and generic ontology population

2021 ◽  
pp. 016555152198964
Author(s):  
Yohann Chasseray ◽  
Anne-Marie Barthe-Delanoë ◽  
Stéphane Négny ◽  
Jean-Marc Le Lann
Keyword(s):  
Data Extraction ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Model Driven Engineering ◽  
Computing Systems ◽  
Intelligent Computing ◽  
Model Driven ◽  
Textual Data ◽  
Ontology Population ◽  
Domain Independent

As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.

Model-driven engineering and run-time model-usage in service robotics

2012 ◽  
Vol 47 (3) ◽  
pp. 73-82 ◽  
Author(s):  
Andreas Steck ◽  
Alex Lotz ◽  
Christian Schlegel
Keyword(s):  
Model Driven Engineering ◽  
Service Robotics ◽  
Time Model ◽  
Model Driven ◽  
Run Time

Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming

2021 ◽  
Author(s):  
Nils Weidmann ◽  
Anthony Anjorin
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Experimental Evaluation ◽  
Graph Grammars ◽  
Model Driven Engineering ◽  
Rule Based ◽  
Consistency Management ◽  
Model Driven ◽  
Triple Graph Grammars ◽  
Domain Constraints

AbstractIn the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge; to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.

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