Ontology-based Data Integration Research Articles

Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration

• Analyzes the future research trend of GIS and BIM integration through literature review. • GIS and BIM integration has been the basic technology to realize smart cities. • Future research should focus on smart city and urban rail transportation. Geographic information system (GIS) data provide geospatial data on cities and spatial analysis functions that are essential for urban design. Building information modeling (BIM) includes a digital entity of construction, a passive presentation of micro-digital information on real entities, and an active application of models in the entire life cycle realization of the architecture, engineering, and construction industries. A combination of these technologies could provide a core technology for the urban digital twin to support sustainable smart city design. Through an insightful literature review, this paper summarizes the different disciplinary classifications of GIS and BIM functional integration, distills the value of data, and discusses the ontology-based data integration approach that GIS and BIM should take in the future to conduct research on integration applications in smart cities. To verify this view, keyword analysis, co-country analysis, and co-citation and coupling analyses are conducted using CiteSpace. GIS and BIM integration has attracted much attention. However, a professional disconnect and fragmented composition pose challenges in the field of GIS and BIM integration. Future research should focus on smart city planning, updating, management; ontology-based GIS and BIM data integration platform; and operation; and the collaborative management of urban rail transportation engineering.

Reusable Ontology Modelling Patterns for Biodiversity Data with Reasonable Ontology Templates (OTTR)

The Norwegian Biodiversity Information Centre (NBIC) is currently building a traitbank for Norwegian species. The purpose of the NBIC TraitBank is to enhance sharing of traits and other information about species to support conservation actions and ecological research. The traitbank will cover a subset of traits for all multicellular species and taxa that are found in Norway. Observations of traits, collected through citizen science as well as by experts, are connected to the NBIC TraitBank ontology, forming a knowledge base. We have modeled TraitBank’s ontology in accordance with NBIC’s data management requirements, focusing on the domain knowledge necessary for ontology-based data integration of internal databases and queries specified by use cases. Our initial steps in ontology construction included outlining competency questions, which are natural language sentences expressing the questions system users expect an ontology to answer (Bezerra et al. 2013, Ren et al. 2014). The ontology for the TraitBank is populated using expert input (manual entry) and through harvesting of traits from existing internal databases at NBIC. We will present our experiences with implementing Reasonable Ontology Templates (OTTR) (Skjæveland et al. 2021) as the means for modeling the ontology and populating the TraitBank ontology. OTTR is a language for formally representing and instantiating ontology modeling patterns and is designed to support knowledge base construction and interaction at a higher level of abstraction. In the case of the TraitBank, ontology patterns are edited and published using a Semantic MediaWiki (SMW) extension for OTTR (FloSchroeder 2022), thereby providing a tool for the domain expert to work directly with templates. We build the TraitBank ontology by instantiating the templates directly in SMW as wiki pages. We argue that templates are an effective means to support the integration and use of digital biodiversity data in transparent ways, leading to successful collaboration and reuse of data. Following the "Don't repeat yourself" (DRY) principle of software development (Hunt and Thomas 1999), OTTR templates lend themselves well to easier ontology maintenance, allowing updates to occur through changes in individual template definitions rather than to repeated statements spread throughout the ontology. OTTR reshapes how domain experts work with ontologies and the data connected to the knowledge base, lifting the biodiversity expert away from dealing directly with logical axioms and Web Ontology Language (OWL). The template libraries have the power to improve international collaboration, making it easier to exchange and reuse specific templates and suggest improvements. Our templates include mappings to standards developed by Biodiversity Information Standards (TDWG) and biodiversity-related ontologies, linking to the international community. Use of OTTR supports the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data and demonstrates a new technology that can support the creation of an extensive online network of knowledge. Example: Scientific Name The NBIC uses Scientific Name as the main identifier and means to track a species. The OTTR template shown below captures the NBIC’s modelling pattern for Scientific Name. The signature of the template specifies the Internationalized Resource Identifier (IRI) of the template (adb-t:ScientificName), and six parameters (where ?iri is the 1st parameter). The parameters are used in the body of the template and define how instances of the template are expanded to Resource Description Framework (RDF) statements. Template instance expansion is done in a recursive manner, similar to many macro programming languages. With the OTTR template definition given in Fig. 1, a template instance can be expanded, as shown in the example for Metopa glacialis Fig. 2. The benefits of using the OTTR framework is that modeling patterns are explicitly represented as an OTTR template, allowing for instances of patterns to be compactly and consistently captured. The format of template instances lends itself to instantiation from tabular data sources like spreadsheets and databases.

A unified ontology-based data integration approach for the internet of things

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

Ontology-based data integration for the internet of things in a scientific software ecosystem

Ontology-based data integration for the internet of things in a scientific software ecosystem

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19.

The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

TOWARDS LIMITING SEMANTIC DATA LOSS IN 4D URBAN DATA SEMANTIC GRAPH GENERATION

Abstract. To enrich urban digital twins and better understand city evolution, the integration of heterogeneous, spatio-temporal data has become a large area of research in the enrichment of 3D and 4D (3D + Time) semantic city models. These models, which can represent the 3D geospatial data of a city and their evolving semantic relations, may require data-driven integration approaches to provide temporal and concurrent views of the urban landscape. However, data integration often requires the transformation or conversion of data into a single shared data format, which can be prone to semantic data loss. To combat this, this paper proposes a model-centric ontology-based data integration approach towards limiting semantic data loss in 4D semantic urban data transformations to semantic graph formats. By integrating the underlying conceptual models of urban data standards, a unified spatio-temporal data model can be created as a network of ontologies. Transformation tools can use this model to map datasets to interoperable semantic graph formats of 4D city models. This paper will firstly illustrate how this approach facilitates the integration of rich 3D geospatial, spatio-temporal urban data and semantic web standards with a focus on limiting semantic data loss. Secondly, this paper will demonstrate how semantic graphs based on these models can be implemented for spatial and temporal queries toward 4D semantic city model enrichment.

Ontology-based data integration for the Internet of Things in a scientific software ecosystem

The Internet of Things (IoT) enables a smart observation of the environment, producing a large amount of heterogeneous data. On the one hand, it allows the remote collection of data, either providing a ready field data set compilation or serving as a secondary source of information to better analyse the research context. On the other hand, all the raw data generated by disparate sensors need to be integrated to leverage the power of IoT in scientific experiments. This paper proposes an ontology-based data integration architecture that allows data from different sources, formats, and semantics to be integrated and organised by a mediated ontology that provides knowledge inference. The architecture is thus evaluated as a use case testing in a scientific software ecosystem that supports all stages of the experiment life cycle.

GTFS-Madrid-Bench: A benchmark for virtual knowledge graph access in the transport domain

A large number of datasets are being made available on the Web using a variety of formats and according to diverse data models. Ontology Based Data Integration (OBDI) has been traditionally proposed as a mechanism to facilitate access to such heterogeneous datasets, providing a unified view over their data by means of ontologies. Recently, the term “Virtual Knowledge Graph Access” has begun to be used to refer to the mechanisms that provide query-based access to knowledge graphs virtually generated from heterogeneous data sources. Several OBDI engines exist in the state of the art, with overlapping capabilities but also clear differences among them (in terms of the data formats that they can deal with, mapping languages that they support, query expressivity that they allow, etc.). These engines have been evaluated with different testbeds and benchmarks. However, their heterogeneity has made it difficult to come up with a common comprehensive benchmark that allows for comparisons among them to facilitate their selection by practitioners, and more importantly, for their continuous improvement by the teams that maintain them. In this paper we present GTFS-Madrid-Bench, a benchmark to evaluate OBDI engines that can be used for the provision of access mechanisms to virtual knowledge graphs. Our proposal introduces several scenarios that aim at measuring the query capabilities, performance and scalability of all these engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They have been transformed into several formats (CSV, JSON, SQL and XML) and scaled up. The query set aims at addressing a representative number of SPARQL 1.1 features while covering usual queries that data consumers may be interested in.

A Framework Uniting Ontology-Based Geodata Integration and Geovisual Analytics

In a variety of applications relying on geospatial data, getting insights into heterogeneous geodata sources is crucial for decision making, but often challenging. The reason is that it typically requires combining information coming from different sources via data integration techniques, and then making sense out of the combined data via sophisticated analysis methods. To address this challenge we rely on two well-established research areas: data integration and geovisual analytics, and propose to adopt an ontology-based approach to decouple the challenges of data access and analytics. Our framework consists of two modules centered around an ontology: (1) an ontology-based data integration (OBDI) module, in which mappings specify the relationship between the underlying data and a domain ontology; (2) a geovisual analytics (GeoVA) module, designed for the exploration of the integrated data, by explicitly making use of standard ontologies. In this framework, ontologies play a central role by providing a coherent view over the heterogeneous data, and by acting as a mediator for visual analysis tasks. We test our framework in a scenario for the investigation of the spatiotemporal patterns of meteorological and traffic data from several open data sources. Initial studies show that our approach is feasible for the exploration and understanding of heterogeneous geospatial data.

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes.

Summary Objective : To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). Methods : A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Results : Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. Conclusion : In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.

On ontology based data integration: problems and solutions

In the article essential problems of integrating heterogeneous data, arising in development of corporate databases intellectual access systems, are considered. In addition to the common structural problems, caused by variety of data organization, special attention is paid to the less obvious linguistic problems, caused by differences in data notation. A unified approach to overcoming such problems by sequential application of explicit definition of semantics, is described. This approach was tested in development of an intelligent search system for the TATNEFT oil-producing corporation; the system implementation showed high relevance of search results together with an adequate reactivity.

Semantic Integration and Enrichment of Heterogeneous Biological Databases.

Biological databases are growing at an exponential rate, currently being among the major producers of Big Data, almost on par with commercial generators, such as YouTube or Twitter. While traditionally biological databases evolved as independent silos, each purposely built by a different research group in order to answer specific research questions; more recently significant efforts have been made toward integrating these heterogeneous sources into unified data access systems or interoperable systems using the FAIR principles of data sharing. Semantic Web technologies have been key enablers in this process, opening the path for new insights into the unified data, which were not visible at the level of each independent database. In this chapter, we first provide an introduction into two of the most used database models for biological data: relational databases and RDF stores. Next, we discuss ontology-based data integration, which serves to unify and enrich heterogeneous data sources. We present an extensive timeline of milestones in data integration based on Semantic Web technologies in the field of life sciences. Finally, we discuss some of the remaining challenges in making ontology-based data access (OBDA) systems easily accessible to a larger audience. In particular, we introduce natural language search interfaces, which alleviate the need for database users to be familiar with technical query languages. We illustrate the main theoretical concepts of data integration through concrete examples, using two well-known biological databases: a gene expression database, Bgee, and an orthology database, OMA.

Дослідження методів і технологій інтеграції онтологічної моделі з реляційними даними

A study of methods and technological solutions on Data Mapping concerning the integration of an ontological and relational data models has been carried out.The main objective of the investigation is to accelerate and reduce the cost of constructing some ontological models for systems that process distributed data in a heterogeneous environment.At present, the theoretical basis for the integration of various data models (Data Mapping) is a di-rection that has been defined as Ontology-based data integration. The theoretical and practical develop-ment of this area is the Ontology-Based Data Access (OBDA) approach, which integrates ontological models presented in the form of RDF graphs with relational data.The methodology of Data Mapping application for distributed data processing systems has been developed. As an example the process of data models consolidation for Ukrainian State Budget monitor-ing system is given. The database of Ukrainian State Budget monitoring system consists of many rela-tional tables, which contain reports of the State Treasury on the implementation of revenue and expendi-ture parts of both state and municipal budgets, as well as the regional section. In addition, the database contains data on lending and arrears of budget institutions.The ontology model connects to data sources through a declarative specification provided in terms of display, including classes and their properties.The given application converts a SPARQL queries into a SQL query to the relational database. The generated SQL query may be execute by the Oracle 11g database driver, which returns the result as a data snapshot. Then, to improve the performance of SPARQL queries, it should be used the semantic query optimization method.Indicative application of the methodology on the example of the construction of an ontological model of the system of monitoring the state budget of Ukraine in Protege 5. It has been demonstrated the results of the execution of the SPARQL-queries to the relational data of the budget process under the Oracle 11g Database. It has been shown the directions semantic optimization for SPARQL-queries, which allow improving obtained data quality.The proposed methodology allows: to integrate data presented by different models — ontological and relational knowledge acquisition; to overcome constraints when databases based on outdated data models are being merged with modern ontological-oriented systems; to eliminate data redundancy in designing knowledge-based systems.

An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival

BackgroundCancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges.MethodsFollowing best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs.ResultsBased on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies.ConclusionsUsing an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.

Logical foundations of information disclosure in ontology-based data integration

Ontology-based data integration systems allow users to effectively access data sitting in multiple sources by means of queries over a global schema described by an ontology. In practice, data sources often contain sensitive information that the data owners want to keep inaccessible to users. Our aim in this paper is to lay the logical foundations of information disclosure in ontology-based data integration. Our focus is on the semantic requirements that a data integration system should satisfy before it is made available to users for querying, as well as on the computational complexity of checking whether such requirements are fulfilled. In particular, we formalise and study the problem of determining whether a given data integration system discloses a source query to an attacker. We consider disclosure on a particular dataset, and also whether a schema admits a dataset on which disclosure occurs. We provide matching lower and upper complexity bounds on disclosure analysis, in the process introducing a number of techniques for analysing logical privacy issues in ontology-based data integration.

An ontology-based data integration framework for construction information management

Information management during the construction phase of a built asset involves multiple stakeholders using multiple software applications to generate and store data. This is problematic as data come in different forms and are labour intensive to piece together. Existing solutions to this problem are predominantly in proprietary applications, which are sometimes cost prohibitive for small engineering firms, or conceptual studies with use cases that cannot be easily adapted. In view of these limitations, this research presents an ontology-based data integration framework that makes use of open-source tools that support Semantic Web technologies. The proposed framework enables rapid answering of queries over construction data integrated from heterogeneous sources, data quality checks and reuse of project software resources. The attributes and functionalities of the proposed solution align with the requirements common to small firms with limited information technology skill and budget. Consequently, this solution can be of great benefit for their data projects.

SemLinker: automating big data integration for casual users

A data integration approach combines data from different sources and builds a unified view for the users. Big data integration inherently is a complex task, and the existing approaches are either potentially limited or invariably rely on manual inputs and interposition from experts or skilled users. SemLinker, an ontology-based data integration system, is part of a metadata management framework for personal data lake (PDL), a personal store-everything architecture. PDL is for casual and unskilled users, therefore SemLinker adopts an automated data integration workflow to minimize manual input requirements. To support the flat architecture of a lake, SemLinker builds and maintains a schema metadata level without involving any physical transformation of data during integration, preserving the data in their native formats while, at the same time, allowing them to be queried and analyzed. Scalability, heterogeneity, and schema evolution are big data integration challenges that are addressed by SemLinker. Large and real-world datasets of substantial heterogeneities are used in evaluating SemLinker. The results demonstrate and confirm the integration efficiency and robustness of SemLinker, especially regarding its capability in the automatic handling of data heterogeneities and schema evolutions.

RODI: Benchmarking relational-to-ontology mapping generation quality

Accessing and utilizing enterprise or Web data that is scattered across multiple data sources is an important task for both applications and users. Ontology-based data integration, where an ontology mediates between the raw data and its consumers, is a promising approach to facilitate such scenarios. This approach crucially relies on useful mappings to relate the ontology and the data, the latter being typically stored in relational databases. A number of systems to support the construction of such mappings have recently been developed. A generic and effective benchmark for reliable and comparable evaluation of the practical utility of such systems would make an important contribution to the development of ontology-based data integration systems and their application in practice. We have proposed such a benchmark, called RODI. In this paper, we present a new version of RODI, which significantly extends our previous benchmark, and we evaluate various systems with it. RODI includes test scenarios from the domains of scientific conferences, geographical data, and oil and gas exploration. Scenarios are constituted of databases, ontologies, and queries to test expected results. Systems that compute relational-to-ontology mappings can be evaluated using RODI by checking how well they can handle various features of relational schemas and ontologies, and how well the computed mappings work for query answering. Using RODI, we conducted a comprehensive evaluation of seven systems.

Mapping of Description Logic to the Relational Data Model

General issues of the data integration problem are discussed, including ontology-based data integration in the semantic web. The importance of establishing mappings between ontologies and relational databases is shown, and a brief analytical review of studies in this field is given. The binary relational data model is defined and the mapping of the description logic ALC and its extensions to the binary relational data model is described.

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

ABSTRACTIn the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontology-based approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods.