Ontologies are crucial for managing and integrating diverse datasets in digital libraries, where data heterogeneity poses ongoing challenges. This paper presents a novel framework specifically designed to address the unique needs of digital libraries using Semantic Label Property Graphs. Our methodology aligns with semantic web standards, ofering a sophisticated approach to data management that enhances integration, querying, and visualization of complex datasets. The proposed framework supports automated ontology generation, advanced semantic integration, and seamless visualization, leveraging the structural eficiency of Property Graphs with semantic annotations to optimize resource discovery, management, and retrieval. We detail the architecture and core functionalities of the framework, demonstrating its adaptability in managing complex ontologies and improving workflows for researchers and practitioners. Empirical evaluations reveal significant performance improvements in data management and linked data integration, underscoring the framework's potential to streamline workflows and enhance semantic interoperability. This innovative approach addresses the evolving challenges of large-scale data management, positioning the framework as a valuable tool for the future of digital libraries.
Digital libraries are essential platforms for storing, managing, and providing access to vast collections
of cultural, historical, and academic resources. These collections are diverse, encompassing textual
documents, multimedia, complex metadata, and intricate relationships between various entities such as
authors, works, genres, and historical events. As these libraries continue to grow in size and complexity,
traditional data management systems increasingly struggle to handle the heterogeneity, scale, and
interconnected nature of the data, leading to significant challenges in data integration, retrieval, and
usability [
Digital libraries face several critical challenges that impede their ability to efectively manage,
integrate, and provide access to their vast collections:
1. Data heterogeneity and integration: Digital libraries typically aggregate data from a multitude
of sources, each employing distinct metadata standards and formats, such as Dublin Core and
MARC [
To address these challenges, the integration of Semantic Web technologies and graph-based data models
has emerged as a critical area of research [
Graph databases, particularly those based on the Label Property Graph (LPG) model, ofer a
complementary approach by eficiently managing the interconnected nature of digital library data. LPGs
provide a flexible structure for modeling entities and their relationships, supporting advanced queries
and visualizations that are crucial for exploring complex datasets. The hybrid approach of combining
Semantic Web technologies with graph databases promises to overcome current limitations, ofering
a powerful solution for managing, integrating, and retrieving semantically enriched data in digital
libraries [
Hybrid approaches that integrate Semantic Web technologies with graph-based data models have
become increasingly relevant in the digital library domain, enhancing data management, integration,
and retrieval capabilities. Previous research has demonstrated the advantages of combining Semantic
Web standards, such as RDF (Resource Description Framework), with graph-based models like Label
Property Graphs (LPGs) [
Nguyen et al. [
Graph databases such as Neo4j1, ArangoDB2, and Amazon Neptune3 have incorporated RDF and LPG
capabilities, supporting the semantic integration of diverse datasets [
Despite the progress made in integrating Semantic Web technologies and graph databases in digital
libraries, several critical gaps remain unaddressed. One of the key challenges is the limited adoption of
hybrid models that combine RDF (Resource Description Framework) and Label Property Graphs (LPGs).
Although these hybrid models ofer significant advantages in terms of data integration and semantic
enrichment, their implementation is still relatively rare in digital libraries. This limited adoption is
largely due to technical challenges, such as the complexity of integrating these technologies [
Scalability also remains a significant issue in the current landscape [
Moreover, there is a pressing need for more robust semantic enrichment tools that can automatically generate and manage semantic annotations within graph-based models. The current lack of sophisticated tools in this area hampers the ability to create richer data connections and enhance user interactions with library content. Such tools are essential for facilitating deeper exploration of digital library collections, allowing users to navigate complex relationships and discover new connections within the data.
This study is motivated by the need to develop and refine methodologies that leverage the strengths of Semantic Web technologies and graph databases to address the ongoing challenges faced by digital libraries. By creating a framework that integrates SLPGs, this research aims to favour the management of complex datasets, improve semantic interoperability, and optimize the user experience for researchers, students, and practitioners in the field of cultural heritage and beyond.
This research utilizes the OntoBuilder tool [23] to construct and automatically populate an ontology tailored for the digital library domain. The methodology consists of several key phases:
The process begins with constructing the SLPG ontology schema, where entities (e.g., books, authors, genres) and their relationships (e.g., authoring, publication, thematic links) are defined. Each node represents an entity, while edges define relationships, and both can have associated properties. This structure is flexible, enabling the modeling of complex and heterogeneous data in digital libraries.
Semantic web standards such as RDF, RDF Schema (RDFS), and OWL (Web Ontology Language) are integrated within the graph-based framework. This allows the representation of rich metadata, enhancing the system’s ability to understand and manage relationships between entities. RDF triples are mapped into the LPG structure, enriching nodes and edges with semantic meaning that aligns with global standards for data interchange and reuse.
Concepts and relationships are extracted from structured and unstructured data sources within the digital library. Using predefined rules and algorithms, entities like book titles, author names, and 1https://neo4j.com 2https://arangodb.com 3https://aws.amazon.com/it/neptune publication dates are automatically identified and incorporated into the SLPG. This automation reduces the manual efort required to build ontologies while ensuring consistency and accuracy in the ontology creation process.
Once the SLPG is constructed, it supports advanced querying capabilities through graph traversal techniques combined with semantic reasoning. Users can query the ontology to retrieve complex information, such as identifying all works by a particular author within a specified timeframe or discovering thematic connections across diferent collections. The LPG’s inherent flexibility in managing relationships allows for eficient querying, while semantic annotations ensure the relevance and precision of search results.
The methodology supports continuous evolution and scaling of the ontology. As new data is ingested into the digital library, the ontology can be updated dynamically, allowing for the seamless integration of additional metadata and relationships. This adaptability ensures that the ontology remains relevant as the digital library grows and evolves over time.
By adhering to RDF and other semantic web standards, the SLPG-based ontologies ensure that data can be shared and integrated across diferent systems. This interoperability is crucial for digital libraries that rely on external data sources, such as linked open data initiatives, to enrich their collections. The ability to interlink resources across various libraries and cultural heritage institutions enhances the discoverability and usability of digital assets.
The final SLPG ontology can be exported into RDF format for integration with other semantic web technologies or maintained in the LPG format (e.g., Neo4j) to optimize graph-specific features and performance. This flexibility allows digital libraries to choose the most appropriate format based on their specific needs, balancing semantic richness with operational eficiency.
This methodology provides a robust and flexible approach for enhancing data management within digital libraries, ofering improvements in metadata organization, search functionality, and interoperability. By integrating SLPGs with semantic web standards, it enables more eficient handling of complex datasets, addressing the challenges of scalability, data integration, and advanced querying in modern digital libraries.
In the evolving landscape of digital libraries, efective organization and retrieval of information hinge on the development of robust ontologies. In this section, we outline a systematic approach to constructing an ontology that captures essential entities within a digital library framework, such as books, authors, topics, publishers, locations, and contributors. Using existing semantic resources, this process not only enhances the richness of the data, but also facilitates better user interactions and knowledge discovery. A key feature of our approach is the automatic population of the ontology from textual documents. This allows for the seamless extraction and categorization of information, ensuring that the ontology remains up-to-date and reflective of the latest publications and research. The following steps detail the approach, beginning with the integration of DBpedia as a foundational reference for creating our ontology.
The first step is to use guided creation using DBpedia as a reference. For instance, we select the class Book as the base entity. From DBpedia, we incorporate several properties that describe books, including: • comment: A brief description or annotation of the book. • author: The name of the person who wrote the book. • subject: The main topics or themes addressed in the book. • publisher: The company or organization responsible for publishing the book. • publicationDate: The date when the book was first published.
• isbn: The International Standard Book Number, a unique identifier for books.
Additionally, we define custom properties specific to the digital library, such as: • bookStatus: Indicates the current availability status of the book (e.g., available, checked out). • editingContributors: Names of the individuals involved in the editing process of the book.
Once the class Book is created with these properties, it is named and linked to both the DBpedia resources and the internal digital library resources. DBpedia properties maintain links to external URIs, while custom properties are linked to specific URIs representing the internal reference environment of the digital library.
To describe the manual creation process, consider the book “The Name of the Rose” by Umberto Eco. The following details how an instance of the class Book would be created and populated: Creation of the class Book • Name: Book • DBpedia Properties: – comment: A description of the book. – author: Umberto Eco. – subject: Historical novel, Mystery. – publisher: Bompiani. – publicationDate: 1980.
– isbn: 978-88-452-1523-5. • Custom Properties: – bookStatus: Available.
– editingContributors: Maria Bonfantini.
Manual insertion of properties • comment: “The Name of the Rose is a historical novel and mystery written by Umberto Eco, set in a Benedictine monastery in the 14th century.” • author: Umberto Eco. • subject: Historical novel, Mystery. • publisher: Bompiani. • publicationDate: 1980. • isbn: 978-88-452-1523-5. • bookStatus: Available.
• editingContributors: Maria Bonfantini.
During the ontology population phase, instances of classes can be added either manually or automatically. In the manual mode, users input values for class properties, as demonstrated above. In the automatic mode, an advanced language model is employed to process text, identifying and categorizing entities and properties. For example, given the text: “The Name of the Rose, written by Umberto Eco, is a historical novel published by Bompiani in 1980. The book deals with themes such as faith, truth, and heresy and is available in our library.”
In automatic insertion mode, the language model parses the text, identifying “The Name of the Rose” as a book, extracting the author “Umberto Eco,” the publisher “Bompiani,” the publication date “1980,” and other relevant information. This information is then used to automatically create class instances in the ontology.
Once properties are associated with DBpedia and custom resources, the ontology is enriched with semantic data. This enriched data can be visualized using graph-based tools [24]. For example, the SKATEBOARD interface [25] can be used to visualise the connections between books, authors, and other entities, allowing dynamic exploration and further semantic augmentation of the ontology (see Figure 1).
This study highlights the importance of integrating Semantic Web technologies and graph databases, specifically through Semantic Label Property Graphs, to enhance data management in digital libraries. By addressing challenges such as data heterogeneity, complex relationships, scalability, and interoperability, our methodology demonstrates improved organization and retrieval of diverse datasets.
An empirical evaluation of the methodology applied, as detailed in [23], reveals that the proposed methodology significantly enhances ontology management and linked data integration. The results indicate strong user satisfaction, particularly in areas like integration compatibility and ontology representation accuracy, underscoring the framework’s efectiveness in real-world applications.
The case study illustrates how our framework enables eficient ontology creation and automated metadata integration, fostering richer user interactions. The ability to dynamically update ontologies ensures ongoing relevance and usability.
Future research can focus on enhancing semantic enrichment tools and addressing scalability challenges to further improve user experience and collaboration among institutions. Overall, our work contributes both theoretical insights and practical methodologies for optimizing digital library management, positioning libraries to better serve cultural and academic resources.
This research was partially supported by projects CHANGES “Cultural Heritage Active Innovation for Sustainable Society” (PE00000020), Spoke 3 “Digital Libraries, Archives and Philology” and FAIR “Future AI Research” (PE00000013), spoke 6 “Symbiotic AI”, funded by the Italian Ministry of University and Research NRRP initiatives under the NextGenerationEU program. up to complex knowledge graph creation pipelines 1, Semantic Web (2024) 1–28. [23] E. Bernasconi, M. Ceriani, S. Ferilli, Lpg semantic ontologies: A tool for interoperable schema creation and management, Information 15 (2024). URL: https://www.mdpi.com/2078-2489/15/9/565. doi:10.3390/info15090565. [24] E. Bernasconi, M. Ceriani, D. Di Pierro, S. Ferilli, D. Redavid, Linked data interfaces: A survey, Information 14 (2023). URL: https://www.mdpi.com/2078-2489/14/9/483. doi:10.3390/info14090483. [25] E. Bernasconi, D. Di Pierro, D. Redavid, S. Ferilli, Skateboard: Semantic knowledge advanced tool for extraction, browsing, organisation, annotation, retrieval, and discovery, Applied Sciences 13 (2023). URL: https://www.mdpi.com/2076-3417/13/21/11782. doi:10.3390/app132111782.