Feature-based product modelling: an ontological approach

Sanfilippo, Emilio M.

doi:10.1080/0951192x.2018.1497814

Cited by 19 publications

(11 citation statements)

References 39 publications

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“…As for the second part, our study can be compared with an already existing product ontology, such as the product ontologies found in [ 24 , 32 ], the more recent [ 28 ], and the general purpose ontology GoodRelations [ 10 ]. Our difference is that these ontologies offer information about objects, geometrical, physical, and material properties, and create object taxonomies and hierarchical relations.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge

Vassiliades

Bassiliades

Gouidis

et al. 2020

Lecture Notes in Computer Science

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In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval framework for household environments which was constructed by extracting knowledge from the VirtualHome dataset (http://virtual-home.org). The framework provides knowledge about sequences of actions on how to perform human scaled tasks in a household environment, answers queries about household objects, and performs semantic matching between entities from the web knowledge graphs DBpedia, ConceptNet, and WordNet, with the ones existing in our knowledge graph. We offer a set of predefined SPARQL templates that directly address the ontology on which our knowledge retrieval framework is built, and querying capabilities through SPARQL. We evaluated our framework via two different user evaluations.

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Section: Related Workmentioning

confidence: 99%

A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge

Vassiliades

Bassiliades

Gouidis

et al. 2020

Lecture Notes in Computer Science

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“…14 For instance, representing a man-made object's color, one may wish to say that it is red without specifying its exact shade in quantitative terms. Our proposal is to revise CIDOC on the basis of the work done in [7,16], therefore, by allowing for the representation of dimensions' qualitative values, too. This is done by introducing the class Qualitative Quality Space, which provides a way to organize and represent qualities' values in terms of, e.g., mereological or topological structures, among others (see the cidoc:dimension-module described in Sect.…”

Section: Analysis Of Dimensionsmentioning

confidence: 99%

“…At the current development stage, the modular architecture comprises 18 modules including the module called cidoc:whole which is the union of all modules used to build the whole ontology. 16 For data organization in, e.g., RDF triplestores, this module should be always imported for first to guarantee the integration and interoperability of data instantiating the other modules. For the sake of shortness, we provide here only a general overview of the modules; Tables 1 -4 give a schematic view on the entire library, including the structure of imports (owl:imports).…”

Section: Towards the Modularization Of Cidocmentioning

confidence: 99%

“…This approach weakens the expressivity of the ontology in comparison to [7] (e.g., we can not say that 8kg is a value within a space for weights), but it takes the benefits of a Description Logic based formalism to model quantitative dimensions' values. In addition, end-users can introduce data properties like hasWeightInKg to characterize the intended meaning of numerical values attached to dimensions (see [16]). With this approach, differently from the original spirit of CIDOC, dimensions can be now characterized in terms of either quantitative or qualitative values.…”

Section: Towards the Modularization Of Cidocmentioning

confidence: 99%

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Ontological Analysis and Modularization of CIDOC-CRM

Sanfilippo

Markhoff

Pittet

2020

Frontiers in Artificial Intelligence and Applications

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The CIDOC-CRM ontology is a standard for cultural heritage data modeling. Despite its large exploitation, the ontology is primarily maintained in a semi-formal notation, which makes it difficult to homogeneously exploit it in digital environments. In addition, the ontology consists of several classes and relations, whereas one sometimes wishes to reuse it but only partially. The purpose of the paper is to contribute to the use of CIDOC by strengthening its foundations. On the basis of formal ontology theories, we propose a first analysis of the ontology to enhance its conceptual structure. We also present a preliminary modularization of CIDOC aimed at enhancing both its formalization and usage.

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“…The technology of state policy formation based on the ontological approach can be an addition to the expert knowledge about subject area (domain), the features and possible options for the economic development of subject area [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Ontological Modeling of the State Economic Development Policy for Cultural Industries

Tkachenko¹,

Tkachenko²,

Tkachenko

et al. 2021

Bus. Inf. Sys.

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The article discusses an ontological approach to solving the problem of forming state policy of economic development of cultural and creative industries and the corresponding intellectual-information management systems. The purpose of this article is to develop an effective toolkit (based on ontologies) for making optimal decisions in the field of state regulation of the cultural and creative industries, taking into account the dynamic factors of the external environment. The ontological approach considered in the article assumes the presence of three levels of models: meta-ontology, models of subject areas of cultural and creative industries and models of making appropriate management decisions on the formation of economic development policy of cultural and creative industries. The novelty of the proposed approach lies in the purposeful nature of ontological modeling of such complex system as the state policy of economic development of cultural and creative industries. The system under consideration has certain goals, tasks, resources, processes, factors of influence, risks and other characteristics. These characteristics include, in particular, the structure of the model, the ability to highlight the essential objects of real relations of the considered subject areas, the ability to represent knowledge for the joint work of specialists in computer modeling, the processing of expert knowledge and the generation of management decisions within the framework of the corresponding intellectual-information systems.

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Feature-based product modelling: an ontological approach

Cited by 19 publications

References 39 publications

A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge

A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge

Ontological Analysis and Modularization of CIDOC-CRM

Ontological Modeling of the State Economic Development Policy for Cultural Industries

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