Creating an ontology for Business Analysis is an unusual practice. Few companies have a space in their data where the concepts that permeate their business are explicitly defined and categorized for all employees.
But before that, let’s define:
What is an ontology
An ontology is a formal specification that provides a reusable, shareable knowledge representation. Examples of ontologies include:
An ontology specification includes descriptions of concepts and properties in a domain, relationships between concepts, constraints on how relationships can be used, and individuals as members of concepts.
Notes and thoughts about the course Ontologies for Business Analysis an always incomplete and up-to-date article.
Onthology: a formal, explicit specification of a shared conceptualization. – Studer et al., 1997
In a translation of my own:
Ontology is a formal, shared conceptualization of an explicit specification. – Studer et al., 1997
Analyzing this sentence according to the theme we can say that an ontology is formal because it can be read by computers, it is explicitly specified because of its precise representation derived from a consensus about a given subject, that is, a shared conceptualization.
The Structure of an Ontology
Instances: individual occurrences (instances)
Taxonomy classification technique
Inheritance: classification where the elements of a class inherit the characteristics of their “parents”.
Universe (or Domain) of Discourse
The discourse domain, also called discourse universe or quantification domain, is an analytical tool used in deductive logic, especially in predicate logic.
Indicates the relevant set of entities to which the quantifiers refer.
The term “universe of discourse” usually refers to the complete set of terms used in a specific discourse, that is, the family of semantic or linguistic terms that are specific to a certain area of interest.
In Model Theory, the term “universe of discourse” refers to the set of entities on which a model is based. A database is a model of some aspect of an organization’s reality. It is conventional to call this reality a “universe of discourse” or “domain of discourse.”
What are Ontologies from a business point of view?
Ontologies are “anabolic information models”, and can help us overcome many of the challenges of domain models with loose interpretations – which makes ontologies a great addition to the business analyst’s toolkit.
Ontologies make it possible to explicitly define the logicalstructure of concepts, their relations, as well as the axioms* that govern their interpretation in a universe of discourse.
There is a connection between ontologies and mathematical logic, as well as formal representation – used in defining models that can be encoded and recognized by computers.
Representation languages are fundamental to modeling ontologies. These languages can be graphic and/or formal in nature.
Ontologies can be modeled as either light or heavy representations, depending on the level of formality required. Visual and less formal models fall on the light spectrum, while models that are encoded using expressive knowledge representation languages are considered heavy.
Ontologies have witnessed a growing interest over the past decade.
*gram.gener in a system or theory linguistic theory or system a formula that is presumed to be correct, although not susceptible to demonstration.
What are the benefits of developing and using an ontology?
From a business point of view, especially if it is in a market where technical terms are used a lot, ontologies can be a very useful tool. ontologies are very useful because they allow to generate common understanding about specific sets of information.
This factor alone makes the creation of ontologies for business analysis is fundamental, because it eliminates assumptions and makes explicit the definitions and relationships among the entities of your business, supporting all the areas involved.
The common understanding of explicit information and assumptions about a semantic domain are valuable in themselves.
The ontologies support data integration data for analysis and apply knowledge knowledge to data They facilitate the interoperability of data model-driven applications.
They therefore reduce development time and cost and improve data quality by creating metadata(data about data) and a sort of relational “family tree.
Automating knowledge about data with Ontology
In the process of running a high-performance business you need to automate processes. The analysis and decision making processes are rarely automated, largely because of the difficulty in standardizing data entry, interpretation, and insight generation.
Creating an ontology for your business enables the creation of semantic repositories, which use these ontologies as schemas to create meaning about this data.
This makes automated reasoning about the data possible and easy to implement, since the most essential relationships between concepts are incorporated into the ontology.
When we create an ontology for business analysis, we connect data to each other by structuring their connections. Having information about your products, services, and field of operation makes systems dependent on that information, use the connected data you have created, use you as a source.
Among these services is Google!
Strategies on how Content Modeling, together with data structuring, will be the step of the future for SEO
But what is Content Modeling and how does it help SEO?
Content Modeling helps you define how your content will be organized and divided so that it can be managed and used in all the environments you operate in.
Dividing all the content your organization produces, into common elements and distributing the access to the creators makes the access and use of this content free of access platforms, programs and protocols.
There is a process of training the content creators, who must be aware that everything they create will be deconstructed and “driven” by a technology system, to be applied on various platforms, by the Content Model manager.
How does Content Modeling work?
Content engineering fills in the gaps that may exist between strategy and development of your content.
Working with content strategy and engineering transforms your content, which today is static, dependent on platforms and programs, into an independent, intelligent, structured and optimized format. Everything you need to be loved by search engines!
Structured Content is the SEO of the Future!
Structuring content with the help of Content Modeling allows your company to adapt to changes in your market, the creation of new products or services, design changes, or new platforms and social networks, for example.
Most large companies already use some form of content strategy and operations, even if they do not use that name. But small and micro-sized companies may feel that they don’t need to worry about this or won’t have the resources to use content management through Content Modeling.
But this is not true. Non-centralized content modeling saves resources for companies that have money to invest. Small and micro businesses can also invest to take control of their content and get rid of rework, saving money and time in the construction of their marketing pieces.
And on top of that, by using structured data with content modeling, your SEO project will elevate your company far beyond the competition!
And this is why creating an ontology for business analytics will help build the next level of your SEO.