IS Week 2
This week in IS2000 we largely discussed ontologies. An ontology is the design of formal conceptual structures for a given domain of inquiry. The objective of ontologies, and building them, is to represent abstract, real world physical concepts and their relations in a tangible manner. OOIs represent ontologies within and with regard to information systems. Ontologies describe entities, their attributes, and the relationships between them. Entities are things, physical objects, and abstract concepts. Attributes are properties of entities, as well as measurements of quantitative data with regard to them. Examples of relationship matrices include taxonomies, partonomies and associations. Multiplicities and cardinalities are both examples of constraints.
We also sought to answer the question of why we use ontologies. The creation of ontologies forces rigorous analysis and facilitates unambiguous discourse about domains. This is critical for information science, and within the field it is generally done using UML. An example of an ontology is as follows.
A book contains pages. This is a partonomy. An ebook is a kind of book. This is an example of a taxonomy. A book contains at least three pages. This is an example of a multiplicity constraint. A book is written by a given author. This is an association. A book may not have any one author. This is another instance of a multiplicity constraint. A book also has a publication date. This is an attribution. Taxonomies are represented by open arrows. This is used to indicate something is a kind of something else. An open diamond means something is composed of something else. A filled in diamond means something is permanently composed of something else.
In order to build an ontology, first determine your domain context and scope. Then consider whether or not existing ontologies can serve your purpose, thus saving you time, resources, and effort. If this is not the case, or more work is needed, as is often the case, then you need to enumerate important concepts, roles, and events. Then determine what will make up your classes, as well as any and all class hierarchies within which said classes exist. Then define the properties, parts, and roles of these classes, as well as the data within them. Finally, define the facets of the slots within which these classes will exist. Once all this has been done, instances and examples should be generated so as to analyze the ontology that has been built.
An information model is a definition of objects in a domain. When building an information model it important to use only info that is pertinent to your specific question, assignment, or goals. Information modeling can be defined as the process of capturing information, constraints and rules. Examples of constraints and rules include things such as "a loan can only be given to a specific student", or "a student is provided only a specific loan, but can be received annually." In this case, an entity would be a student, while other features of this model would be attributes, relationships, and identifiers. An attribute is a property of an object. Types of attributes include simple (non-decomposable), composite (an entity with attributes), multi-valued (more than one value for an attribute), and derived (computed using the values of other attributes) attributes.
https://www.w3.org/standards/semanticweb/ontology
We also sought to answer the question of why we use ontologies. The creation of ontologies forces rigorous analysis and facilitates unambiguous discourse about domains. This is critical for information science, and within the field it is generally done using UML. An example of an ontology is as follows.
A book contains pages. This is a partonomy. An ebook is a kind of book. This is an example of a taxonomy. A book contains at least three pages. This is an example of a multiplicity constraint. A book is written by a given author. This is an association. A book may not have any one author. This is another instance of a multiplicity constraint. A book also has a publication date. This is an attribution. Taxonomies are represented by open arrows. This is used to indicate something is a kind of something else. An open diamond means something is composed of something else. A filled in diamond means something is permanently composed of something else.
In order to build an ontology, first determine your domain context and scope. Then consider whether or not existing ontologies can serve your purpose, thus saving you time, resources, and effort. If this is not the case, or more work is needed, as is often the case, then you need to enumerate important concepts, roles, and events. Then determine what will make up your classes, as well as any and all class hierarchies within which said classes exist. Then define the properties, parts, and roles of these classes, as well as the data within them. Finally, define the facets of the slots within which these classes will exist. Once all this has been done, instances and examples should be generated so as to analyze the ontology that has been built.
An information model is a definition of objects in a domain. When building an information model it important to use only info that is pertinent to your specific question, assignment, or goals. Information modeling can be defined as the process of capturing information, constraints and rules. Examples of constraints and rules include things such as "a loan can only be given to a specific student", or "a student is provided only a specific loan, but can be received annually." In this case, an entity would be a student, while other features of this model would be attributes, relationships, and identifiers. An attribute is a property of an object. Types of attributes include simple (non-decomposable), composite (an entity with attributes), multi-valued (more than one value for an attribute), and derived (computed using the values of other attributes) attributes.
https://www.w3.org/standards/semanticweb/ontology
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