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Semantic Technologies as Enabler
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
RDF4J (Sesame) is an official fork of the OpenRDF Sesame project. OpenRDF Sesame is a Java-based open-source framework for storage, inference, and querying of RDF and RDF Schema. It can be used as a stand-alone server—i.e., a database for RDF and RDF Schema—and also as a Java library. Sesame is a lightweight yet powerful API with reasoning and transactional support.Storage: Eclipse RDF4J, or Sesame, is an open-source, database-independent storage for RDF data. It can be combined with a variety of DBMSs like MySQL, PostgreSQL, or Oracle 9i or newer. In its architecture it contains a layer named SAIL (Storage and Inference Layer) for managing communication with the database in use. It provides support for all three types of storage: in-memory and native data stores and APIs for integrating with RDF databases.Query: Sesame can accept only queries written in SeRQL (an RDF query language) and converts them into queries suitable to run on the underlying repository.Reasoning: It does not have any support for OWL, so it is not suitable for ontologies.14
Design for invention: a framework for identifying emerging design–prior art conflict
Published in Journal of Engineering Design, 2018
Pingfei Jiang, Mark Atherton, Salvatore Sorce, David Harrison, Alessio Malizia
The generated RDF files were then uploaded to an Eclipse RDF4J server (RDF4J n.d.). RDF4J is an open-source framework, formerly known as Sesame, for querying and analysing RDF data. In our case, we deployed it over an instance of an Apache-Tomcat web server. The RDF4J server can then be accessed both from a web interface (browser-based access) and from a URI (Uniform Resource Identifier – for programmatic access), both for querying and managing. The server accepts queries in different languages, and in our study SPARQL Protocol and RDF Query Language was used due to its broad application and popularity. SPARQL enables the designer to describe the working principles of an emerging design in the form of one or multiple queries such that possible matches from the database can be retrieved. For instance, in these queries the designer can specify emerging design geometric features, FGI or multiple FGI. By doing this the designer is able to obtain information of any potential conflicted prior art. Figure 7 illustrates an overview of the patent data coding process, starting from the patents in their common form (e.g. PDF), to their representation in RDF along with supplementary semantic data, in order to allow SPARQL querying for potential conflicts. In the next section several example queries were conducted to demonstrate the emerging design–prior art comparison method.