Integration Strategy and Tool between Formal Ontology and Graph Database Technology

Ferilli, Stefano

doi:10.3390/electronics10212616

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…A description of the current version of the GraphBRAIN knowledge representation structure and formalism, which extends and refines that proposed in [3]; 2.…”

mentioning

confidence: 86%

“…GraphBRAIN [3,51] is a framework for the management of KGs that has the vision of joining the efficiency in data handling provided by LPG-based graph DBs (specifically, Neo4j) with the expressive power of ontologies. One further objective of GraphBRAIN is to provide more handling possibilities than those provided by standard SW reasoners.…”

Section: Graphbrain Frameworkmentioning

confidence: 99%

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A Graph DB-Based Solution for Semantic Technologies in the Future Internet

Ferilli,

Bernasconi,

Di Pierro

et al. 2023

Future Internet

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With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the data they process and exchange. This paper describes a framework and online platform for the Internet-based knowledge graph definition, population, and exploitation based on the LPG graph model. Its main advantages are its efficiency and representational power and the wide range of functions that it provides to its users beyond traditional Semantic Web reasoning: network analysis, data mining, multistrategy reasoning, and knowledge browsing. Still, it can also be mapped onto the SW.

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“…A description of the current version of the GraphBRAIN knowledge representation structure and formalism, which extends and refines that proposed in [3]; 2.…”

mentioning

confidence: 86%

Section: Graphbrain Frameworkmentioning

confidence: 99%

A Graph DB-Based Solution for Semantic Technologies in the Future Internet

Ferilli,

Bernasconi,

Di Pierro

et al. 2023

Future Internet

Self Cite

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show abstract

“…To perform this conversion, mapping rules were established to determine how RDF classes, individuals, object properties, data properties, and annotations would correspond to LPG nodes, properties, and edges. Specifically, RDF classes and individuals were represented as LPG nodes, RDF object properties were represented as LPG edges, RDF data properties were represented as LPG node properties, and RDF annotations were represented as LPG node or edge properties, depending on the context [32].…”

Section: Ontologymentioning

confidence: 99%

SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications

Lymperis,

Goumopoulos

2023

Future Internet

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The development of smart city applications often encounters a variety of challenges. These include the need to address complex requirements such as integrating diverse data sources and incorporating geographical data that reflect the physical urban environment. Platforms designed for smart cities hold a pivotal position in materializing these applications, given that they offer a suite of high-level services, which can be repurposed by developers. Although a variety of platforms are available to aid the creation of smart city applications, most fail to couple their services with geographical data, do not offer the ability to execute semantic queries on the available data, and possess restrictions that could impede the development process. This paper introduces SEDIA, a platform for developing smart applications based on diverse data sources, including geographical information, to support a semantically enriched data model for effective data analysis and integration. It also discusses the efficacy of SEDIA in a proof-of-concept smart city application related to air quality monitoring. The platform utilizes ontology classes and properties to semantically annotate collected data, and the Neo4j graph database facilitates the recognition of patterns and relationships within the data. This research also offers empirical data demonstrating the performance evaluation of SEDIA. These contributions collectively advance our understanding of semantically enriched data integration within the realm of smart city applications.

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“…This endows SKATEBOARD with the versatility to not only visualise Linked Data in existing endpoints, but also contribute to the creation of Linked Data, addressing the challenges associated with extracting semantic knowledge from unstructured texts and semantic annotation. Furthermore, with its integration with the GraphBRAIN system [3,4] for ontology creation and management, SKATEBOARD provides a comprehensive solution for the lifecycle of Linked Data. The GraphBRAIN system provides a dedicated API that enforces ontology compliance for all interactions with the Knowledge Graph (KG).…”

Section: Related Workmentioning

confidence: 99%

SKATEBOARD: Semantic Knowledge Advanced Tool for Extraction, Browsing, Organisation, Annotation, Retrieval, and Discovery

Bernasconi,

Di Pierro,

Redavid

et al. 2023

Applied Sciences

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This paper introduces Semantic Knowledge Advanced Tool for Extraction Browsing Organisation Annotation Retrieval and Discovery (SKATEBOARD), a tool designed to facilitate knowledge exploration through the application of semantic technologies. The demand for advanced solutions that streamline Knowledge Extraction, management, and visualisation, characterised by abundant information, has grown substantially in the current era. Graph-based representations have emerged as a robust approach for uncovering intricate data relationships, complementing the capabilities offered by AI models. Acknowledging the transparency and user control challenges faced by AI-driven solutions, SKATEBOARD offers a comprehensive framework encompassing Knowledge Extraction, ontology development, management, and interactive exploration. By adhering to Linked Data principles and adopting graph-based exploration, SKATEBOARD provides users with a clear view of data relationships and dependencies. Furthermore, it integrates recommendation systems and reasoning capabilities to augment the knowledge discovery process, thus introducing a serendipity effect generated by the SKATEBOARD interface exploration. This paper elucidates SKATEBOARD’s functionalities while emphasising its user-centric design. After reviewing related research, we provide an overview of the SKATEBOARD pipeline, demonstrating its capacity to bridge RDF and LPG representations. Subsequent sections delve into Knowledge Extraction and exploration, culminating in the evaluation of the tool. SKATEBOARD empowers users to make informed decisions and uncover valuable insights within their data domains, with the added dimension of serendipitous discoveries facilitated by its interface exploration capabilities.

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Integration Strategy and Tool between Formal Ontology and Graph Database Technology

Cited by 12 publications

References 15 publications

A Graph DB-Based Solution for Semantic Technologies in the Future Internet

A Graph DB-Based Solution for Semantic Technologies in the Future Internet

SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications

SKATEBOARD: Semantic Knowledge Advanced Tool for Extraction, Browsing, Organisation, Annotation, Retrieval, and Discovery

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