Ontology-based Data 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.

Case representation and retrieval for complex product design based on case-based reasoning

Reusing design knowledge of products is a useful way to solve the efficiency issue of complex product design. The design knowledge is tacit, empirical, and unstructured and there exists insufficient case matching and inefficient design reuse in complex products design process. Aiming at these problems, this paper presents an improved case-based reasoning methodology combining ontology with two-stage retrieval. Firstly, a knowledge domain ontology model of complex product design is constructed, and the technology of ontology-based data access is introduced to automatically generate a case knowledge base with semantic information. Then, a new two-stage case retrieval method integrated semantic query with similarity calculation is proposed. The case subset is selected by query statements. It has the characteristic of isomorphism with design problem. The retrieval mechanism is applied to compress the traversal space, reduce the redundancy of semantic similarity calculation, improve the retrieval efficiency, and fulfill the target of case reuse. Finally, a variant design of the chiller unit as an example is executed to illustrate the use of the proposed method, and experiments are organized to evaluate its performance. The result shows that the proposed approach has an average precision of 92% and high stability, outperforming existing methods.

Monotone Abstractions in Ontology-Based Data Management

In Ontology-Based Data Management (OBDM), an abstraction of a source query q is a query over the ontology capturing the semantics of q in terms of the concepts and the relations available in the ontology. Since a perfect characterization of a source query may not exist, the notions of best sound and complete approximations of an abstraction have been introduced and studied in the typical OBDM context, i.e., in the case where the ontology is expressed in DL-Lite, and source queries are expressed as unions of conjunctive queries (UCQs). Interestingly, if we restrict our attention to abstractions expressed as UCQs, even best approximations of abstractions are not guaranteed to exist. Thus, a natural question to ask is whether such limitations affect even larger classes of queries. In this paper, we answer this fundamental question for an essential class of queries, namely the class of monotone queries. We define a monotone query language based on disjunctive Datalog enriched with an epistemic operator, and show that its expressive power suffices for expressing the best approximations of monotone abstractions of UCQs.

Handling qualitative preferences in SPARQL over virtual ontology-based data access

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

A tetrachotomy of ontology-mediated queries with a covering axiom

Our concern is the problem of efficiently determining the data complexity of answering queries mediated by description logic ontologies and constructing their optimal rewritings to standard database queries. Originated in ontology-based data access and datalog optimisation, this problem is known to be computationally very complex in general, with no explicit syntactic characterisations available. In this article, aiming to understand the fundamental roots of this difficulty, we strip the problem to the bare bones and focus on Boolean conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes. We show that, on the one hand, these rudimentary ontology-mediated queries, called disjunctive sirups (or d-sirups), capture many features and difficulties of the general case. For example, answering d-sirups is Π2p-complete for combined complexity and can be in ▪ or L-, NL-, P-, or coNP-complete for data complexity (with the problem of recognising FO-rewritability of d-sirups being 2ExpTime-hard); some d-sirups only have exponential-size resolution proofs, some only double-exponential-size positive existential FO-rewritings and single-exponential-size nonrecursive datalog rewritings. On the other hand, we prove a few partial sufficient and necessary conditions of FO- and (symmetric/linear-) datalog rewritability of d-sirups. Our main technical result is a complete and transparent syntactic ▪/NL/P/coNP tetrachotomy of d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive query. To obtain this tetrachotomy, we develop new techniques for establishing P- and coNP-hardness of answering non-Horn ontology-mediated queries as well as showing that they can be answered in NL.

Finnish Parliament on the Semantic Web

This paper introduces the system ParliamentSampo – Parliament of Finland on the Semantic Web, a Linked Open Data (LOD) service, data infrastructure, and semantic portal for studying Finnish political culture, language, and networks of the Members of Parliament (MP). The article presents the vision behind the system, the LOD service, and explores the possibilities to utilize it in research and application development. A knowledge graph of linked data has been created based on ca. 962 000 speeches in all plenary sessions of the Parliament of Finland in 1907—2021; the data is also available in XML format, utilizing the new international Parla-CLARIN format. For the first time, the entire time series of the Finnish parliamentary speeches has been converted into data and a data service in a unified format. In addition, the speeches have been interlinked with another knowledge graph created from the database of the MPs and enriched from other data sources into a broader ontology-based data service. The paper shows how the LOD service SPARQL endpoint can be used to research parliamentary culture, the use of political language, and networks of politicians through data analysis. The service endpoint can also be used to develop applications for different user groups without programming skills, such as the ParliamentSampo semantic portal introduced in the paper, too. This application aims to make political decision making more transparent to the general public, media, politicians, and other end users.

ONTOLOGY-BASED DATA ACCESS TO HETEROGENEOUS DATA SOURCES: STATE OF THE ART APPROACHES AND APPLICATIONS

ONTOLOGY-BASED DATA ACCESS TO HETEROGENEOUS DATA SOURCES: STATE OF THE ART APPROACHES AND APPLICATIONS

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>

A learning workflow based on an integrated digital toolkit to support education in manufacturing system engineering

Digital modelling of manufacturing systems is experiencing a fast development, but it still shows significant limitations when considering integration and interoperability of enabling technologies. Indeed, there is still a lack of reference integrated workflows to perform the wide span of tasks, ranging from layout configuration and performance evaluations to 3D representations. Commercial software tools are either too complex or expensive to be approached by non-specialists, therefore it is hard to design effective learning activities in manufacturing system engineering. This paper proposes a structured learning workflow based on an open toolkit that takes advantage of a common ontology-based data model to smoothly integrate digital tools for manufacturing system modelling, performance evaluation, and virtual reality representation. After detailing methodologies and digital tools, the proposed workflow is applied to a pilot case in higher education.

VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a prerequisite for both has made steady progress in the form of new approaches. The range of algorithms varies in the type of information used (data schema, values, or metadata), as well as in the underlying methodology (e.g., use of different machine learning methods or external knowledge bases). Approaches that have been established over the years, however, still come with various weaknesses. Most approaches are evaluated on few small data corpora specific to the approach. This reduces comparability and also limits statements for the general applicability and performance of those approaches. Other research areas, such as computer vision or natural language processing solve this problem by providing unified data corpora for the evaluation of specific algorithms and tasks. In this paper, we present and publish VC-SLAM to lay the necessary foundation for future research. This corpus allows the evaluation and comparison of semantic labeling and modeling approaches across different methodologies, and it is the first corpus that additionally allows to leverage textual data documentations for semantic labeling and modeling. Each of the contained 101 data sets consists of labels, data and metadata, as well as corresponding semantic labels and a semantic model that were manually created by human experts using an ontology that was explicitly built for the corpus. We provide statistical information about the corpus as well as a critical discussion of its strengths and shortcomings, and test the corpus with existing methods for labeling and modeling.

A forensic-driven data model for automatic vehicles events analysis

Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records’ admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.

Semantically Query All (Squerall): A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

In terms of data formats, codecs, and storage capacity, the previous two decades have seen a phenomenal progress. Rather than needing to adjust to one's application demands to the restricted storage solutions available in the past, there is now a wide range of options to pick from in order to best satisfy an application's needs. As a result, there is massive volume of data available in many forms and formats that, when linked and searched together, may yield significant knowledge and insights. We offer data warehouse as a solution and Big data as a solution in this study in order to handle heterogeneous data. However, both of these techniques have drawbacks when it comes to handling diverse data. Afterwards we propose another framework (Squerall), which relies on the ideas of Ontology-Based Data Access (OBDA) to allow querying of diverse heterogeneous sources using a single query language, SPARQL. In Squerall original data is queried on the fly, with no prior data materialization or modification. Squerall, in particular, enables the distributed aggregate and combining of massive data sets. It comes with five data sources out of the box, and it may be programmatically expanded to include other sources and query engines. The framework includes user interfaces for creating required inputs and for assisting non-SPARQL specialists through the process of writing SPARQL queries. It declares data mappings and transformations using RML and FnO, and employs Spark and Presto as query engines. The initiative underscored the importance of developing in this framework, technologies, and processes that enable for decentralized Big Data administration. Furthermore, demonstrating the feasibility and usefulness of OBDA on top of the growing NoSQL movement has a beneficial impact on Semantic Web principles acceptance. This demonstrates Squerall's importance and contribution to the organization.

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.

Loop restricted existential rules and first-order rewritability for query answering

Abstract In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper, we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decidable. In particular, we prove that this language satisfies the so-called bounded derivation-depth property (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in Ac$^{0}$. We also prove that the combined complexity of the CQA is 2-ExpTime complete, while the language membership is Pspace complete. Then we extend the LR TGDs language to the generalized loop restricted (GLR) TGDs language and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.

Big knowledge-based semantic correlation for detecting slow and low-level advanced persistent threats

Targeted cyber attacks, which today are known as Advanced Persistent Threats (APTs), use low and slow patterns to bypass intrusion detection and alert correlation systems. Since most of the attack detection approaches use a short time-window, the slow APTs abuse this weakness to escape from the detection systems. In these situations, the intruders increase the time of attacks and move as slowly as possible by some tricks such as using sleeper and wake up functions and make detection difficult for such detection systems. In addition, low APTs use trusted subjects or agents to conceal any footprint and abnormalities in the victim system by some tricks such as code injection and stealing digital certificates. In this paper, a new solution is proposed for detecting both low and slow APTs. The proposed approach uses low-level interception, knowledge-based system, system ontology, and semantic correlation to detect low-level attacks. Since using semantic-based correlation is not applicable for detecting slow attacks due to its significant processing overhead, we propose a scalable knowledge-based system that uses three different concepts and approaches to reduce the time complexity including (1) flexible sliding window called Vermiform window to analyze and correlate system events instead of using fixed-size time-window, (2) effective inference using a scalable inference engine called SANSA, and (3) data reduction by ontology-based data abstraction. We can detect the slow APTs whose attack duration is about several months. Evaluation of the proposed approach on a dataset containing many APT scenarios shows 84.21% of sensitivity and 82.16% of specificity.

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.

Enhancing virtual ontology based access over tabular data with Morph-CSV

Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-the-fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with the GTFS-Madrid benchmark; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of Morph-CSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.

Benchmarking Ontology-Based Query Rewriting Systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates ``relevant'' test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of ``inferences'', each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system.