Knowledge extract and ontology construction method of assembly process text

The actual product assembly process mainly relies on manual assembly by workers, and the personal experience of workers is difficult to effectively reuse. Ontology as a knowledge management and expression tool is gradually applied in the field of assembly. However, the manual construction of the ontology is time-consuming and labor-intensive, and the automatic construction of the ontology requires a large number of corpora for training, both of which are difficult to obtain a good assembly case ontology. This paper proposes a method in which automatically extracts relevant knowledge from case assembly process files to generates case database and integrates ontology framework of assembly domain to construct ontology. It shows that the accuracy can be guaranteed on the basis of the rapid construction of case ontology. The feasibility of this method is proved by a practical case.

Ontology Versioning Driven by Instance Evolution in the τOWL Framework

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.

Visualisation for ontology sense-making: A tree-map based algorithmic approach

Ontology sense-making or visual comprehension of the ontological schemata and structure are vital for cross-validation purposes of the ontology increment during the process of applied ontology construction. Also, it is important to query the ontology in order to verify the accuracy of the stored knowledge embeddings. This will boost the interactions between domain specialists and ontologists in applied ontology construction processes. Hence existing mechanisms have numerous of deficiencies (discussed in the paper), a new algorithm is proposed in this research to boost the efficiency of usage of tree-maps for effective ontology sense making. Proposed algorithm and prototype are quantitatively and qualitatively assessed for their accuracy and efficacy.

An approach to the automatic linguistic ontology construction to determine the interests of social networks users

Social networks provide researchers with the opportunity to obtain an array of text data for further analysis within a certain subject area. Each subject area has its own specific professional vocabulary and writing style. When defining the subject area of text material there is a big problem with building dictionaries, thesauri, and ontologies. In this article a linguistic ontology is considered under ontology and which is aimed to determine the subject area of text material. An algorithm for the automatic construction of an ontology based on the Wikidata knowledge graph is presented. The task is to map a set of objects of the Wikidata knowledge graph to a set of entities of a linguistic ontology. The article pro-poses an algorithm for determining the degree of belonging of the text material to the subject area. Experiments on assessing the time of building an ontology and the applicability of the obtained linguistic ontologies to the problem of determining the degree of belonging of text materials in the subject area have shown: the running time of the algorithm and the number of terms in the formed ontology are directly proportional to the number of analyzed properties and Wikidata objects; the formed linguistic ontology is applicable to the problem of determining the degree of belonging of a text to a subject area