A Semantic Matching Strategy for Very Large Knowledge Bases Integration

Rinaldi, Antonio M.; Russo, Cristiano; Madani, Kurosh

doi:10.4018/ijitwe.2020040101

Cited by 13 publications

(4 citation statements)

References 24 publications

(1 reference statement)

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“…Future research on the informal district of BaSECo will be focused on the verification on-site of the findings obtained from the configuration analysis. This step will be relevant to develop a more appropriate and specific methods to integrate new knowledge by using ad hoc techniques [100][101][102] based on the formal representation of the urban environment through ontologies [67,103] and the "smart" use of big data [104,105].…”

Section: Discussionmentioning

confidence: 99%

Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the “Smart Perspective”

Pinto

Rinaldi

Rossini

2021

IJGI

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This paper explores the link between the current vision of the “smart city” and the notion of urban autopoiesis understood as self-organized/managed urban systems. It seeks to highlight how the use of GIS analysis, applied to the study of informal settlements, can provide useful information to understand the smart city paradigm. The paper argues the key idea that a smart city should not be seen only as a high-tech urban environment because the transition to smartness will need major changes in its inner structure. Using a combination of quantitative and qualitative GIS analysis methods, this study examines the case of the BaSECo Compound, one of the densest informal settlements in Metro Manila (Philippines), with the aim of both generating a comprehensive morphological analysis of this dynamic urban area as well as contributing to the configurational theory of the smart city. The results suggest that the analysis of autopoietic urban systems could expand our understanding of how the structure of the city could evolve to accommodate the needs of its citizens and creating more resilient and inclusive communities.

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Section: Discussionmentioning

confidence: 99%

Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the “Smart Perspective”

Pinto

Rinaldi

Rossini

2021

IJGI

Self Cite

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“…At the same time, the alignment problem is relevant in comparing large ontologies, which requires the use of automated methods for dividing ontologies into smaller areas of alignment [16] and comparing large knowledge bases [34]. The alignment of large ontologies is carried out by automated clustering methods with subsequent alignment [30].…”

Section: Related Workmentioning

confidence: 99%

Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures

Manziuk

Barmak

Krak

et al. 2022

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The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.

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“…Historic Building Information Modeling (HBIM) has been widely applied for digital documentation of cultural heritage and generally consists of several steps: onsite investigation, modeling, knowledge association, and delivery [1][2][3][4]. With the increasing scale of a project, modeling and knowledge association become time-consuming, which rely on the development of tools or methods to check [5][6][7], exchange or transform [6,[8][9][10][11][12], extract or retrieve [10,[12][13][14][15][16][17], and match or map [10,12,13,15,[18][19][20].…”

Section: Introduction 1review Of Related Workmentioning

confidence: 99%

“…The Neo4j was selected as the platform to build the graph database, for its high performance in processing big data. The query speed will not decrease with the increasing scale of data [12,13,15,[30][31][32].…”

Section: Introduction 1review Of Related Workmentioning

confidence: 99%

The Synergy of Metadata and Metamodel through Algorithm Modeling—Case Study of the Roof Tiles in Yangxindian Palace (Beijing, China)

Wang

et al. 2022

Applied Sciences

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The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying.

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A Semantic Matching Strategy for Very Large Knowledge Bases Integration

Cited by 13 publications

References 24 publications

Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the “Smart Perspective”

Learning from the Informality. Using GIS Tools to Analyze the Structure of Autopoietic Urban Systems in the “Smart Perspective”

Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures

The Synergy of Metadata and Metamodel through Algorithm Modeling—Case Study of the Roof Tiles in Yangxindian Palace (Beijing, China)

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