In order for the Semantic Web to become a reality and success, there needs to be data on the web published as Linked Data. However, data on the web is not a new thing. People have been publishing raw data for a long time as XML, CSV or even spreadsheets. Data can also be accessed through APIs.  But where does most of the data on the web come from? Relational Databases!

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The Semantic Tech & Business Conference (SemTechBiz) is coming to San Francisco on June 3-7! Join us for case studies, innovative panels, tutorials, and keynotes that will provide you with practical advice, hands-on guidance, and breakthrough approaches to solving business problems with semantic technology. Passes go up $200 at the door. Sign up now and save !

Introduction to the AXIS-CRM and to its implementation

1. The AXIS-Conceptual Reference Model

1.1 Generalities

A new modular and tailorable approach for the semantic modeling of static and dynamic knowledge has been elaborated under the name “AXIS Conceptual Reference Model” (AXIS-CRM). AXIS organizes that modeling as networks of Autonomous Semantic Objects (ASO). In turn, each ASO is expressed as a network of Elementary Semantic Entities (ESE). The ASO wraps the instances and their models to becomes ‘autonomous’. At Elementary Semantic Entity level (simply called ‘Entities’) the modeling uses four leveled constructors: Term; Document; Relation; Profile. The knowledge models and their instances are represented by a collection of Documents (among with the OWL files expressing the models) bundled by a Configuration Management Document (based on RDF). These collections are semantic Entities that can represent any topical subject or object. These Entities are linked through typed Relations. The dynamic aspects (events) and the imports / exports are also managed by dedicated Entities.

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With the Web 2.0, ontologies are being used to improve search capabilities and make inferences for improved human or computer reasoning. By relating terms in an ontology, the user doesn’t need to know the exact term actually stored in the document. Data Rationalization is a Managed Meta Data Environment (MME) enabled application which creates/extends an ontology for a domain into the structured data world, based on model objects stored in various models (of varying levels of detail, across model files and modeling tools) and other meta data. Ontology is “the study of the categories of things that exist or may exist in some domain”1. An ontology is comprised of “a collection of taxonomies and thesauri”2 about a domain. Data Models, often unknowingly, express many aspects of ontology, even though they are not stored in OWL or RDF.

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Palm oil is a multi-billion dollar industry, yet to date no attempt has been made for complex knowledge modeling within the Palm Oil industry. As oil palm plantation industries contain numerous changing conditions and since relevant decision making parameters are dynamic as well, an intelligent Decision Support System (DSS) that is context sensitive, environment specific and localized for the user is needed. Our proposed solution empowers the end-users through semantic and ontology development methods, where the involvement of domain experts and end-users drive the application development.

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