Abstract
Work towards creation of a knowledge sharing system for sustainability science through the application of semantic data modeling is described. An ontology grounded in description logics was developed based on the ISO 15926 data model to describe three types of sustainability science conceptualizations: situational knowledge, analytic methods, and scenario frameworks. Semantic statements were then created using this ontology to describe expert knowledge expressed in research proposals and papers related to sustainability science and in scenarios for achieving sustainable societies. Semantic matching based on logic and rule-based inference was used to quantify the conceptual overlap of semantic statements, which shows the semantic similarity of topics studied by different researchers in sustainability science, similarities that might be unknown to the researchers themselves.
Highlights
We have developed a system based on principles of semantic data modeling for realizing a knowledge sharing platform mediated by a computer searching and matching engine that uses logical and rule-based inference to calculate the semantic similarity between computer-understandable descriptions of the knowledge resources, such as academic papers and databases, which are shared on the platform
Towards the construction of a knowledge sharing platform for sustainability science, we have developed an extension of the ontology for the domain of engineering failures that encompasses concepts related to sustainability science, and we have created a set of semantic statements in EKOSS using that ontology to describe research related to sustainability science
At the core of the EKOSS-based knowledge sharing system for sustainability science is a formalized ontology based on a description logic that provides a knowledge model for describing concepts from the domain of sustainability science
Summary
We have developed a system based on principles of semantic data modeling for realizing a knowledge sharing platform mediated by a computer searching and matching engine that uses logical and rule-based inference to calculate the semantic similarity between computer-understandable descriptions of the knowledge resources, such as academic papers and databases, which are shared on the platform. The system, called EKOSS for Expert Knowledge Ontology-based Semantic Search, provides authoring tools that empower the authors of the resources to create descriptions using formalized languages that can be interpreted semantically, and thereby “understood”, by computers. Using those computer-understandable descriptions, which we call semantic statements, EKOSS provides computer-enabled knowledge sharing services, such as semantic searching and matching of specific relationships between target entities that are not explicitly stated but that can be inferred from the formal structure of the language used to author the semantic statements. Many forms of expert knowledge in addition to the technological knowledge of the fuel cell system itself may be necessary to evaluate the feasibility of the large-scale application of the technology, for example, knowledge related to the lifecycles of rare materials required for the construction of the system and knowledge on the effects of large-scale introduction of the system on existing systems for providing the same kinds of services (Kraines et al, 2005; Kraines et al, 2006b; Fukushima et al, 2004)
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