What is the difference between ontology and database

This steady performance was present in the benchmarking measurements as well. The dramatic difference between the performance results of ontologies compared with DBs is due to several technological and tool-based factors.

The main reason for such a difference could be the buffering of the queries in the Jena ARQ, Footnote 17 since KB do not repeat the existing instance. This allows for caching of the paths between nodes. Unlike the query tests, the population was similar to the DB population benchmarking results. After finishing all the benchmarking tests, the JMH framework presented the overall results, which are presented in Fig. As shown, the results show a very distinct performance difference between updating and querying the models.

This difference could be generated by the nature of the technology, since the DB inserts data directly without extra mapping operations as the DB table is constructed. In contrast, the ontology update operation requires a mapping process in order to not duplicate the instances of the classes.

This difference could be caused by the caching feature, provided by the JENA ARQ, which caches the path between nodes for a querying operation—providing better performance. In this research, these parameters were kept at the default configuration settings to represent a common client trying to use the tool directly from the box. It is important to highlight that these tests depend on the search engine for each technology; therefore, the specific indexing algorithm should be further analyzed.

These results can be compared with other research that addressed the same problem; however, the comparison can be unfair for some technologies since the tests and experimental techniques might vary, with different configurations and parameters. This could affect the test results, where HTTP services can add latency to the system. This section has presented the experimental results that have enabled a performance comparison of DB and ontology technologies.

On the performance level, the tests intend to eliminate the technology effects, as close as possible, by following the same test conditions and using the same OS. However, as the vision was to try to deploy these tests with the most commonly used tools available in order to reflect the real-world scenario, this has been a challenging experiment. Instead of comparing both technologies, it is important to highlight that both technologies may work side by side—each providing unique features for the user.

As an example, from the tests, ontologies should perform better than DBs at querying processes, which makes it a more reasonable choice to be used as a knowledge provider. On the other hand, DBs show more consistent performance for both querying and updating, which makes them suited for use as a data store. In addition, an ontology-based model permits more rich representation of the data. This fact suggests that ontologies would be a better choice for applications that require reasoning and for inferencing implicit knowledge of the data model.

With regard to RQ2, the boundaries between databases and knowledge-base technologies must be investigated. Due to the involvement of the authors of this research in several EU projects, they have experience of the evolution of both data modeling technologies DBs and ontologies with new concepts.

As an example, the Cloud Collaborative Manufacturing Networks C2NET Footnote 18 project includes both technologies within the same solution, in order to exploit different features of each one. In this context, the C2NET project provides key functionalities for the smart and medium-sized enterprises SMEs on the enterprise resource planning ERP level in the well-known automation pyramid described in the ISA standard [ 73 ].

These features, which are provided as web services in the C2NET project, include i optimization, ii monitoring and iii assessment of production, delivery and logistics plans.

Besides this, the C2NET platform also allows the companies to interact with other companies in the same supply chain, acting as a network for the exchange of information and facilitating communicating through the web. In regard to its architecture, and related to the synergy of databases and ontologies, the C2NET platform employs both technologies.

The transformation is applied within the ontology technology since each company can provide the data in a different schema or format. The C2NET platform then uses the database technology to manage the transformed data before it is utilized by the aforementioned features. Although it is discussed in the following section, at first glance it can be argued that the knowledge base provides more flexibility and adjustability for the data format, whereas the database provides better performance and robustness to the system.

The core objective of this research was to compare two data modeling approaches that are used in the context of PLM and PPR: ontologies and databases. In order to achieve this, the authors explored the literature to identify what work existed in this area. The knowledge gap that was identified gives rise to a limited comparison of the two technologies for common applications with limited quantitative and qualitative analyses. To address this gap, three RQs were synthesized:. RQ1 focused on understanding how the data modeling approaches performed as data volume increased.

This is important because databases are currently used extensively in industrial environments, handling large volumes of data, and it is necessary to understand how the ontological approach compares. The authors worked to create data models in a way which enabled a fair comparison with benchmarks that presented data on the following: event counting, product counting, event-in-time counting, and data model population. The results found that the ontology performed more than three times better in the event counting benchmarks, and orders of magnitude better than databases in all other test—apart from the population test.

It is proposed that this is due to the fact that when an instance is created in an ontology, the respective mappings must also be created based on rules, which is not necessarily the case for databases accounting for the poor performance of ontologies in executing population tasks.

By comparison, once the instance exists, it is much easier to access and manipulate it in an ontological model than in a database due to the benefits that these mappings provide. In addition, it is important to keep in mind the effect of the tools used for such a study. The tool itself might play an important role since each tool is supported by optimization algorithms to enhance the performance. This could be the subject of further study to gain insight into the potential optimization process.

RQ2 considered the idea that, ultimately, databases and ontologies have been developed for two quite distinct purposes and it is therefore necessary to understand how these respective technologies may complement each other. Drawing on experience from previous projects e. This type of complementary working addresses an environment where there may be a need to realize interoperability between heterogeneous software.

Such an environment is typical of a manufacturing system. The results do demonstrate, however, that the population rate of an ontological model gives cause for concern in a high-data-volume environment. Given the significant differences in performance between ontologies and databases, it would be of value to investigate if the high-speed data instantiation of databases could be brought into a system where the high-speed querying of ontologies could be exploited.

RQ3 aimed to examine the maintainability of databases in comparison with ontologies, but was not directly addressed in this work. This question was included as the authors appreciate the need to examine the respective technologies holistically and therefore require a lifecycle assessment from system design, through to implementation and then reconfiguration—this is true for both physical and digital systems.

This article lacks a study that evaluates the maintenance efforts, although the authors do shed some light on the creation of the respective models.

To address this, the authors are working on a further piece of research to introduce a change to system requirements and to assess the efforts required to realize them. This research concludes that ontologies and databases should not replace, but rather complement each other.

The experiments show that both technologies present differences in their performance and that the decision for using one instead of the other will depend on the implementation and application. Nevertheless, it may be seen that the experiments presented do not allow the full exploitation of ontologies, due to the low expressivity of event information. To address this, the authors will further increase the complexity of event content—enabling the demonstration of other features such as implicit knowledge inference.

In summary, the work presented in this research contributes significantly to the body of knowledge by:. Quantitatively comparing ontological models and databases with a view to understanding how data volume affects performance.

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Knowl Inf Syst 63, — Download citation. Received : 20 December Revised : 05 March Accepted : 10 March Published : 03 April Issue Date : June Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management PLM and product, process, resource PPR modeling.

Introduction The advent of computer science and information communication technologies ICT in diverse fields such as manufacturing, healthcare and smart cities has improved the manner in which information is created and exchanged between multiple stakeholders [ 1 ]. Literature and industrial practices review Data modeling and management A vast amount of data are currently generated throughout the product realization process—from design, through to process planning, and then on to manufacturing system design and engineering.

Ontologies What is an ontology? Types of ontologies There are different types of ontologies, as reported in [ 30 ], with two main criteria that are used to categorize them: the level of formalization, and the level of specificity.

Ontology development methodologies As a result of over two decades of development and learning, a number of methodologies have evolved to support the development of ontological models from the modeling process through to implementation and use.

Databases What is a database? The authors suggest the following non-exhaustive expectations for a database in the modern production environment: 1. The authors of this research work claim that, in the manufacturing domain, databases should, at least: 1. Types of databases There are many ways to classify different kinds of databases as they can be differentiated according to their structure, contents or application area. Database development methodologies There are many methodologies that database designers and developers may follow in order to create coherent and consistent data models [ 51 , 52 , 53 ].

Previous work on comparison of different approaches for data modeling A number of works have been published that present some level of comparison between ontologies and databases. A summary of the conclusions made concerning the differences are as follows: Design approach: Databases are created from scratch for a specific purpose, whereas ontologies may be created by reusing existing ontologies.

Some tuning may be required. A black art. Optimization: Fundamental step. Ontology Toss expensive constraints. Manual, geared to independent; Lost meaning. Page 11 How is it Implemented and Used? Agility, Flexibility set of queries per Queries usable on DB. Semantics hardwired in Tight coupling. Looser coupling. Lost meaning. Semantics explicit. Hard to evolve and Potentially easier to maintain. ETL tools to help. Few tools. Still no picnic!

Page 12 Page 13 Not work well with Reduced inferencing: too many joins. Tradeoff: Performance vs. Page 14 Page 15 Page 16 Page 17 Where are the Semantics for Database Schema? You cannot maintain what you do not understand!

Page 18 Page 19 Represented using a formal language. Shared meaning of some Expressivity: types, properties, constraints. Constraints for consistency checking. Reused to build new ones.

Reused in unexpected ways. Embedded natural Formal model-theoretic language definitions. Constraints for meaning. Constraints for ensuring self-consistency not data. Secondary Efficient querying and storage for data. Standardized diagram notation. More Alike? Constraints for data integrity. Industry-wide construction guidelines important: what is expressed and how.

Scale to huge sizes. Unimportant Cardinality constraints for getting foreign for Ontology keys right and ensuring tables created for More Different? Toss semantics after conceptual modeling. Optimization for specific set of queries. Sophisticated tool support for migrating data when schema evolve ETL. Can you convert one into the other? Improve this answer. Artemis Artemis 3, 2 2 gold badges 17 17 silver badges 32 32 bronze badges.

Thanks, answer are quite clear. And the phenomenon about answer 4 confused me a lot. As my understanding. Answer 4 means that once we need lots schema, we can not use DB to manage data in good performance. Am I right? Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

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