SWRL2SPIN: Converting SWRL to SPIN

Bassiliades, Nick

doi:10.29007/p6vz

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…SWRL is a semantic web rule language that combines OWL ontologies with Horn Logic rules of the RuleML family of rule languages. However, key inference problems for SWRL are undecidable [ 38 ]. SPIN can represent SPARQL rules and constraints on Semantic Web models [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

Knowledge graph of alpine skiing events: A focus on meteorological conditions

et al. 2022

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Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are the most advanced organization form of knowledge base, which can make explicit the complex relationships among different objects. Thus, introducing knowledge graph to the event management of alpine skiing is significant to improve the ability of risk prediction and decision-making. In this research, we analyze the components and dynamic characteristics of alpine skiing, and construct an “Object-Characteristic-Relation” representation model to express multi-level knowledge. Moreover, we propose a “Characteristic-value- Relationship” representation method based on the multi-source data, to construct the knowledge graph of alpine skiing. With the proposed method, comprehensive relationships between meteorological conditions and alpine skiing can be represented clearly, and support further knowledge reasoning for the event management under meteorological conditions. We have tested the utility of the proposed method in a case study of 2018 Winter Olympics in PyeongChang. The case study realizes an semi-automatic construction of knowledge graph for alpine skiing, provides decision supports for event risk managements, according to different meteorological conditions, and grounds a foundation for future knowledge graph construction of other large-scale sport events.

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Section: Related Workmentioning

confidence: 99%

Knowledge graph of alpine skiing events: A focus on meteorological conditions

et al. 2022

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“…Being supported by the Protégé ontology editor 26 as well as by popular rule engines and ontology reasoners, such as Jess, 25 Pellet, 30 Hermit, 31 FaCT++, 32 and Drools, 33–35 SWRL has become a very popular rule description language for emerging rule-based systems on top of ontologies. 41 During the inferencing process via any reasoner, the inferencing process is executed only by running OWL individuals allocated to ontology on SWRL rules. For example, a SWRL rule is as follows: “hasParent (?x,?y), hasBrother (?y,?z) → hasUncle (?x,?z).” It means if x has parent y and y has brother z, then x has uncle z.…”

Section: Reasoning Of the Recommendations By Iementioning

confidence: 99%

A rule-based decision support system for aiding iron deficiency management

Ertuğrul

Toygar

Foroutan

2021

Health Informatics J

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Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health problems including “anemia.” In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency‐ANEmia‐ Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients’ demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.

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“…Semantic rules are also grounded in formal logic and rich semantics; they can deduce further statements with explanations. Semantic rules are more manageable and understandable than procedural codes to lessen the semantic gap between different parties (Bassiliades 2018). In short, ontologies and rules can provide semantics to disambiguate the meaning of the information concerning how the geospatial data are visualised, and thus foster better transfer, interpretation, and reuse of such knowledge.…”

Section: Knowledge Representation Using Semantic Web Technologiesmentioning

confidence: 99%

“…This rule modelling transition is also being advocated by some Semantic Web researchers; see e.g. Bassiliades (2018). In this paper, we use SPIN rules (with the namespaces spin and sp; for details, see Knublauch ( 2011)) to model the portrayal rules.…”

Section: Formalisation Of Data Portrayalmentioning

confidence: 99%

Towards knowledge-based geovisualisation using Semantic Web technologies: a knowledge representation approach coupling ontologies and rules

Huang

Harrie

2019

International Journal of Digital Earth

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Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge. Current syntactic approaches to presenting visualisation information lack semantics on the one hand, and on the other hand are too bespoke. Such limitations impede the transfer, interpretation, and reuse of the geovisualisation knowledge. In this paper, we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies. Specifically, we represent knowledge regarding cartographic scale, data portrayal and geometry source, which are three key aspects of geovisualisation in the contemporary web mapping era, coupling ontologies and semantic rules. The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions. A prototype system is developed in which geospatial linked data are used as underlying data, and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users. The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.

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SWRL2SPIN: Converting SWRL to SPIN

Cited by 7 publications

References 17 publications

Knowledge graph of alpine skiing events: A focus on meteorological conditions

Knowledge graph of alpine skiing events: A focus on meteorological conditions

A rule-based decision support system for aiding iron deficiency management

Towards knowledge-based geovisualisation using Semantic Web technologies: a knowledge representation approach coupling ontologies and rules

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