Thursday, 15 October 2015

Example of using SWRL built-ins with Solar System ontology.

Introduction to SWRL


Semantic Web Rule Language (SWRL for short) is a combination of OWL DL and OWL Lite sub-languages of OWL Web Ontology. It is possible to write ontology with SWRL built-ins in Ontorion Fluent Editor. One of such example of ontology written by using Semantic Web Rule Language is Cognitum's Solar System Ontology.

To follow along open Fluent Editor, go to File -> New and type Solar System. Double click on the template to open.







Introduction to Solar System ontology



Here is the core of our solar system ontology.
In the picture above it is seen that every instance in our ontology that orbits something is either a planet or a moon. Planets orbit some stars and moons orbit some planets. Later it can be possible to ask very complex queries with Fluent Editor with these two simple rules. Then later in the ontology data modelling is utilized. 

We define relations such as:
  • has-index
  • orbits
  • has-semimajor-axis-1000-km
  • has-period
  • was-discovered-by
  • was-discovered-in-year
And with these relations assigned to each instance we can later ask very complex queries.





How does rule written in SWRL look like?



You describe concepts/instances as a thing and attributes as a value. If you want to reference previous stated instance/concept use the thing and while referencing previous stated attribute use the value
Fluent Editor supports core SWRL built-ins such as: comparisons, maths, strings, date, time and duration. In the example above it can be seen that comparison operator is used.
Rule above states which radius of orbiting of a star is higher. For instance let's ask the materialized graph (reasoner HermiT does not support SWRL yet) following query.

Who-or-What is covered by Earth?




It got us this response and indeed, it is true. Venus and Mercury are closer to the Sun than the Earth.
Now let's ask about orbiting radius of Mars.

Who-or-What is covered by Mars?




And once again it can be seen that our ontology, thus SWRL is working correctly. Not only the Venus and Mercury is included but also the Earth.

Other types of query

It is possible to directly make SPARQL queries.

SELECT ?y WHERE {:Mars :covers ?y}



Or use SWRL Debugger in Tools tab.





Complex queries

Try following query:

Who-or-What was-discovered-in-year greater-than 1900 and is a planet and covers Neptune?



While debate about Pluto being a planet is still up to date Fluent Editor, will provide an answer regardless of others people opinion. 

Not only we had built SWRL feature into Fluent Editor but also we expanded it. We implemented SOUNDEX algorithm into semantics of Fluent Editor. Check example below.



SOUNDEX algorithm allows us to approximate and compare words which have similar pronunciation. For instance, ask following query:


It responded with all moons which were discovered by a person with approximately the same name.
In this case - identical.

Summary


Fluent Editor provides necessary tools to write simple and quite complex rules using SWRL built-ins. You can easily query the materialized graph, or use SPARQL queries directly to achieve response you expect.

More Resources:

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  97. Wow, this is incredibly fascinating! I never thought about combining SWRL built-ins with a Solar System ontology before. It's amazing to see the potential of integrating these two concepts.
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  103. That's a fascinating example of leveraging SWRL built-ins with the Solar System ontology! The Solar System ontology provides a great platform to demonstrate the power and flexibility of SWRL rules. By utilizing SWRL built-ins, we can enhance the ontology's reasoning capabilities and enable more complex inferencing.
    For instance, with SWRL built-ins, we can easily define rules to calculate planetary distances, such as determining the distance between Earth and Mars. We can also incorporate logical operators to perform comparisons, like finding the closest planet to the Sun or identifying planets with similar characteristics.
    Moreover, SWRL built-ins enable us to create dynamic inferences based on real-time data. We can develop rules that update planetary positions, velocities, or other relevant properties, providing accurate and up-to-date information to users.
    Overall, this example showcases the practical applications of SWRL built-ins in the context of the Solar System ontology. It's a remarkable demonstration of how integrating these technologies can significantly enhance reasoning and knowledge representation in ontologies.

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  107. Wow, this is such an interesting example of how SWRL built-ins can be applied to the Solar System ontology! I love how this demonstration showcases the practical use of Semantic Web Rule Language in the context of celestial bodies.
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    Kudos to the team behind this example for highlighting the potential of SWRL built-ins in the context of the Solar System ontology. It's truly inspiring and paves the way for future developments in this field. Keep up the great work!

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  109. SWRL integration with Solar System ontology is fascinating! Utilizing Fluent Editor, the ontology design seems seamless. Excited to explore complex queries and delve deeper into celestial relationships. Thanks for the insight
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  117. This blog post provides a comprehensive example of utilizing SWRL built-ins, offering valuable insights for those navigating Semantic Web Rule Language. With clear explanations and practical demonstrations, it simplifies complex concepts for readers. Whether you're a novice or seasoned professional in the realm of semantic technologies, this resource equips you with the knowledge to leverage SWRL built-ins effectively. Dive into the world of semantic reasoning with confidence, guided by this informative post. Unlock the potential of SWRL and enhance your understanding of rule-based systems today!
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