Imagine for a moment that you have a detailed map for every information search you make? Every time you need to conduct research, you'll have a personalized guide. This guide not only points the way but also explains the terrain, the shortcuts, and the main points of interest. This is, in essence, the promise of knowledge organization, and domain analysis emerges as a primary tool to realize this vision.

In Information Science , Domain Analysis can be defined as the study of specific areas of knowledge, also called "domains," to understand how information is produced, organized, disseminated, and used within these areas. This analysis considers the social, historical, and epistemological aspects of the domain, with the goal of developing more effective information systems that are better suited to users' needs.

Domain analysis, in essence, consists of creating a meticulous "map" of a specific area of ​​knowledge. However, this is no ordinary map; it doesn't simply catalog information. It goes further, also mapping the people who interact with this knowledge, their relationships, and the dynamics that shape this universe. Think of it as creating a geographical map, but instead of rivers and mountains, you will find concepts, theories, trends, and even the communities that hold the knowledge.

This sophisticated approach transcends the superficiality of keyword searches. It delves into the depths of knowledge, considering multiple crucial aspects:

• Fundamental Concepts and Theories: What are the central ideas that underpin this field of knowledge? What theories and concepts form the basis upon which everything else is built? Identifying these elements is crucial to understanding the logical structure of the domain.
• Research Trends: Where is this field heading? What questions are driving current research? Understanding trends allows us to anticipate developments and identify promising areas for investigation.
• Communication Patterns: How do scholars and experts within this domain share their ideas and findings? Which communication channels are predominant? Analyzing these patterns reveals how knowledge is disseminated and collectively constructed.
• Social Structures: Who are the authority figures within this field? How is knowledge validated, and who holds the power to influence the direction of research? Mapping social structures helps us understand the power dynamics that shape the domain.
• Specialized Language: What specific terminology is used in this field? How does this language reflect the values ​​and beliefs of the community? The language of a domain is a reflection of its identity and its particular way of seeing the world.
• Historical Evolution: How has this field developed over time? What events and discoveries were crucial in its trajectory? Understanding historical evolution provides a deeper perspective on the current state of the field.

SEO perspective . I asked myself some questions while studying this subject and decided to write this text as a way of learning and sharing it with you, the reader.

The main question was:

How can using Birger Hjørland's eleven approaches to Domain Analysis help SEO professionals during the process of researching terms for projects, building taxonomies or ontologies for use in various processes of building optimized digital projects, and serve as a methodological basis for building more robust work processes?

Before attempting to answer this question, I want to talk a little about what domain analysis is for Information Science (IS) and how this perspective differs from the perspective that other areas have on the subject.

We can say that in Information Science, domain analysis stands out as a fundamental methodology for understanding the organization and retrieval of information, allowing us to visualize science and its evolutions ¹ and understand the organization and retrieval of knowledge in specific areas of knowledge.

In 2002, Birger Hjørland, a prominent researcher in the field, proposed eleven distinct approaches to analyzing a specific domain. It is based on this article, which aims to present and explore each of these approaches, that I found inspiration to write this text, where I attempt to connect Hjørland's vision with my current work in SEO.

But how is all this related to our work?

In a world flooded with information, domain analysis becomes an indispensable compass. It offers tangible and significant benefits, such as:

• Filtering Out Excessive Search Results: Tired of getting lost in mountains of irrelevant results? Domain analysis helps you refine your searches, directing you to the most relevant and reliable sources of information within a specific field.
• Demystifying Complex Topics: Feeling overwhelmed by an intricate subject? Domain analysis offers a framework for organizing and making sense of complex information, transforming confusion into understanding.
• Identifying Knowledge Gaps: Want to go beyond what is already known? Domain analysis helps you identify unexplored areas and unanswered questions within a domain, opening doors to new discoveries and innovations.
• Understanding Power Dynamics: Interested in understanding the forces that shape a field of study? Domain analysis reveals the power dynamics at play, showing who influences decisions and how knowledge is constructed and validated.
• Designing More Effective Information Systems and Educational Programs: Need to create an information system or a training program? Domain analysis provides valuable insights for designing solutions that meet the specific needs of a domain, making information more accessible and learning more effective.
• Making Informed Decisions about Research Priorities and Resource Allocation: Who is responsible for defining research priorities or allocating resources? Domain analysis provides a solid foundation for making strategic decisions, directing investments to areas of greatest potential and impact within a domain.

To make my job easier, allow me to give you an example:

Let's say you have a new client. They sell natural cannabis-based products. The products this client sells are regulated, subject to specific laws, but they are not yet widely known among the public who would benefit from their use. And you know nothing about the subject, but you need to optimize this project.

Using domain analysis at the beginning of your research, you could create a map (or diagram) of everything involved in the world of cannabis use as medicine, not only to understand your client's service, but also to guide content , your list of terms to be used, the creation of a taxonomy, and all other aspects of your SEO project.

That's what caught my attention about this methodology.

Therefore, in my view, applying domain analysis in our work context allows information professionals (and for me, an SEO specialist can become a professional in this category) to understand the specific needs of each area and develop solutions that promote access to and use of information in an efficient and effective way.

I recommend reading the article " Domain analysis in information science" published in the Journal of Documentation in 2002, because it provides a fairly comprehensive overview of Hjørland's contribution to this area of ​​knowledge and illustrates how domain analysis can be applied in practice. After reading it, you will be able to draw your own conclusions.

The concept of "domain" in Information Science

Before asking you to focus on the eleven approaches, I need to use the literature to conceptualize "domain" in the context of Information Science. I do this so that we don't risk starting the process without the necessary understanding of what we are talking about and from what point of view.

For Information Science, a domain can be understood as a specific area of ​​knowledge, a discursive community, or a field of professional activity ^a segment of reality that has its own characteristics, language, practices, and communication structures ^. The areas of specialization in the division of labor, which constitute the domains, must be theoretically compatible or socially established ^.

Medicine is a field, but a hobby like fishing or camping is also a field.

Domain analysis, proposed by Hjørland and Albrechtsen in 1995 ² seeks to understand the social, ecological and content-oriented nature of knowledge within a specific domain ² .

In the history of the term, computing began by using it to define issues relevant to the moment and the field of informatics. In the 1980s, ^domain analysis was already being discussed, but the meaning of the term was different from what we are dealing with here. The approach of the 1990s shifts from a more formal and computational view and points to more comprehensive analyses; therefore, we speak of studying the language of groups belonging to a certain domain.

For example, how do fishermen in the state of Rio de Janeiro speak? What are their practices and how do they communicate? What I want to highlight is the fact that ^subject matter knowledge .

Dynamic nature of the domains

As I mentioned earlier, domains are not entities , but rather dynamic segments. And a domain presupposes a theory that explains the nature of this domain, its functioning, language, etc. This means that the boundaries and characteristics of a domain can evolve over time, influenced by various factors.

It's important to mention that this theory, which seeks to explain the dynamics of a domain's functioning, generally comes from the academic community that studies a particular subject and encounters that domain. Then it seeks to understand, explain, and define it.

Again, let's use an example: think about how the domain of technology has evolved over the last 50 years:

Imagine what the practices of groups working in this field of human knowledge were like in 1974, how they dealt with computers the size of a small room, before the emergence of the graphical interface we use today, as if it had always existed. The language used, the terms, the relationships between people, and everything else.

As I said before, it is necessary to recognize that domains, unlike divisions of the world, are dynamic and dependent on the theory ^that underlies them. This means that the boundaries and characteristics of a domain can change over time, being influenced by factors such as:

• The development of new theories;
• The evolution of professional practices and;
• Changes in the social context.

Some of the factors that contribute to the dynamic nature of domains include:

• Scientific and technological advances: New discoveries, theories, and technologies can redefine the boundaries of a domain, create subdomains, or even merge previously separate domains.
• Social and cultural changes: Changes in social values, beliefs, and practices can influence how knowledge is produced, organized, and used in a domain.
• Influence of other domains: Interaction between different domains can lead to the exchange of knowledge, the creation of new areas of research and work, and the redefinition of the boundaries of the domains involved.
• Evolution of language and terminology: The language used in a domain is constantly changing, with the creation of new terms, the redefinition of concepts, and the adaptation of language to new realities.

I can't help but elaborate a bit more on this third point. It's the clear connection between domain analysis methodology and SEO work, especially in the workflow I've developed and use in my work. Studying the language and terminology of the knowledge domain to which my client's business relates allows me to go far beyond simply researching words, terms, or concepts.

We were able to create (and keep updated) a map of the collective knowledge present in a certain area of ​​knowledge, and with this map we guide SEO strategies, content creation, website development, and much more.

As you may have already noticed, this dynamic nature of domains presents challenges for my work. But this also happens in Information Science, especially in the organization and retrieval of information. Classification systems, thesauri, and other knowledge organization tools need to be constantly updated to reflect changes in the domains.

It's no coincidence that I've incorporated these tools from Library Science into my workflow: thesauri, controlled vocabularies, taxonomies. They help me understand domains, map them, and keep them updated as needed for each project. I therefore need to consider this dynamic when studying a specific domain.

In my work, it's impossible to execute the semantic workflow without analyzing not only the current state of each domain, but also its history, its trends, and the forces that can influence its evolution, and when that might happen.

Therefore, in my view, understanding the dynamic nature of domains allows us to:

• Develop flexible and adaptable information systems: Systems that can keep up with changes in the domains and continue to provide access to relevant information. Develop a more efficient internal search tool, for example.
• Create knowledge organization tools that reflect the dynamic structure of domains: Classifications, thesauri, taxonomies, or ontologies that can be updated and expanded according to the evolution of the domain.
• Promoting interdisciplinarity and communication: Facilitating the exchange of knowledge and collaboration between myself, the client, and everyone involved in the business operation, regardless of their area within the company.

By recognizing this dynamic, we can develop more effective solutions for our projects, for the organization that hires us, and for the organization that facilitates access to and use of information, which is constantly changing.

Hjørland's eleven approaches to domain analysis

If you've made it this far, you must be genuinely interested in using domain analysis for your work. To help me understand, and to be able to explain it to you, I've created a table with each approach, a brief description, and an example of each.

Hjørland (2002) ^ways in which CI can address a domain in a specific way:

I won't go into detail analyzing each of the eleven approaches, but I know you've probably already had some ideas on how to use some of them in your projects, right? I've already created a controlled vocabulary for a project related to the field of Law, which was quite a lot of work. At the time, I didn't know about domain analysis, and it would have been very useful in guiding my work, saving me days and days of fruitless research.

In summary, the eleven approaches, ranging from the production of literature guides to the application of artificial intelligence methods, provide a comprehensive set of tools and perspectives for domain analysis, which you can reflect upon and develop your own ideas about.

By considering the social, epistemological, and historical characteristics of each domain, Information Science can contribute to the development of more effective information systems and services that meet user needs and promote the advancement of knowledge. I invite you to make use of this knowledge.

Domain analysis is generally applied in these contexts:

• Libraries: In the organization of collections, in the creation of catalogs and databases, in the development of reference services, and in user training.
• Archives: In the classification and description of documents, in the preservation of historical collections, and in access to archival information.
• Museums: In the organization of exhibitions, in the cataloging of objects, and in the creation of educational materials.
• Digital environments: In the development of search systems, in the organization of online content, and in the management of information on digital platforms.

Each of these examples represents several ideas that can generate practical applications, products, or services. Sometimes we come across a highly scientific or academic methodology or technique and find that it has no practical application, and we don't try to draw analogies between it and our professional practice.

This is my job: to make these analogies and bring them to your attention so you can also think about the subject, and if it makes sense, generate applications in your work. If we can understand how to analyze a domain of knowledge and extract from it the set of knowledge contained within it, we will have an extremely useful tool for our work.

Domain analysis and SEO

After briefly going through domain analysis, according to Hjørland's view of Information Science, let's move on to our specific area and try to answer the questions I had when I started studying this subject:

How using domain analytics helps us to:

• How to improve the processes for researching terms for projects?
• In the construction of taxonomies or ontologies for use in various optimized digital project development processes?
• And does it serve as a methodological basis for building more robust work processes?

I will try to use the practical application of the method I created for Semantic SEO ( Semantic Workflow ) as a basis for conducting my work in creating analogies between Information Science, Domain Analysis, and SEO. Since writing the book and continuing to apply the methodology daily, I have been creating more experiences that allow me to refine the work model, but also to include new knowledge from my studies in Library Science and Information Science.

How does domain analysis help us improve our term research processes for projects?

Of the three questions, this is the easiest to answer: it helps us understand the domain in which the project we are optimizing is situated. If we are going to optimize a dental practice website, it is necessary to understand how it "works." Creating a list of forty keywords and delivering twenty texts based on those words is not enough, at least not for me. I go further.

Studying the field of dentistry, its specific language, the regulatory bodies, the main organizations, the terms used, and documenting all this research allows me to generate (and approve with the client) the controlled vocabulary and taxonomy that form the basis for the semantic workflow I use in all my projects.

Before learning about domain analysis, my initial research method was much less structured and more empirical.

How does domain analysis help us build taxonomies or ontologies?

In the answer above, I already indicated how I started using domain analysis to build taxonomies. I must confess that I've only created one ontology for one project in all these years. But I use taxonomies in all projects. And domain analysis helps me a lot to identify the most important and relevant concepts within the project's domain. And then I create the basis for the taxonomy's structure.

Furthermore, I can map something very important: the relationships between concepts. This methodology helps me establish a hierarchy and organize the terms in a logical and coherent way within the taxonomy.

Validating the taxonomy is much simpler; using this methodology helps me validate the taxonomy, ensuring that it is consistent with how knowledge is organized and used in the domain. This greatly facilitates getting the work approved by subject matter experts, in most cases, the client.

In summary, domain analysis allows me to understand the structure of the domain I am entering for the first time. It allows me to gain a comprehensive view of the domain structure, providing the basis for creating taxonomies that reflect the organization of knowledge in the area.

As I said before, I don't usually use ontologies in projects. They are more complex constructs and their use is more useful in very complex projects. In the vast majority of cases, a taxonomy and a controlled vocabulary are sufficient. But anyway, I'll answer the question.

Domain analysis can be used to extract ontologies from texts and other informational resources, identifying relevant concepts, relationships, and properties present in books, documents, and other textual materials. With modern tools (many based on Natural Language Modeling), we can extract this same information from videos and audios.

One of the strengths of using an ontology is in knowledge representation. Domain analysis is a powerful ally in knowledge representation. It allows, in a structured and formal way, the use of ontologies to describe the concepts, relationships, and rules within a domain. By considering the different aspects of the domain, such as terminology, processes, and interactions between concepts, domain analysis contributes to the development of more complete and robust ontologies.

Furthermore, we were able to integrate domain analysis, ontology creation, and tools like PoolParty to integrate data from different sources, allowing for better organization and access to information. Projects can benefit from the creation of more robust information retrieval tools that use data generated throughout the organization in an integrated way.

How does domain analysis serve as a methodological basis for building more robust work processes?

A practical example I saw was the creation of an internal search tool that mapped all the company's employees, their professional and academic backgrounds, as well as all the projects they participated in within the company, and the role they played in each project. This tool suggests work partners for a project that a particular employee wants to implement but doesn't know who they can count on within the organization.

To create the foundation for the tool, an ontology was generated where each employee was an entity, with their projects, training, and skills connected.

I need to pause to clarify a difference. Some people, more accustomed to using domain studies, might be thinking: "But I already do that, I know domain modeling!"

When I first encountered this methodology, I had that impression myself, but after studying it a bit more, I began to see the difference. That's why I want to briefly discuss the differences between the two.

Domain analysis versus domain modeling

Although both approaches, domain analysis in Information Science and domain modeling, aim to understand a specific domain, they have crucial differences in their objectives, methods, and applications.

Let's do a quick summary?

Domain analysis in Information Science , as proposed by Birger Hjørland, focuses on understanding the social, ecological, and content-oriented nature of knowledge within a domain.

Its aim is to analyze how knowledge is produced, organized, disseminated, and used in a specific area, considering its social, historical, and epistemological aspects.

Domain modeling, on the other hand, is a process commonly used in software engineering to create an abstract representation of a problem domain.

Its goal is to identify the relevant concepts, entities, relationships, and behaviors within the domain for software development purposes.

Domain modeling typically results in diagrams and models that represent the structure and functionality of the domain, serving as a basis for the design and implementation of software systems.

In summary, domain analysis in Information Science seeks a deep understanding of the knowledge domain itself, while domain modeling focuses on representing the domain for software development purposes.

Here is a table summarizing the main differences:

Can you see the point of confusion?

"Its objective is to identify the concepts, the entities , the relationships..." This is where, apparently, the relationship between them lies, but the concept of entity is different for each of the approaches. For use in SEO, I propose using the Information Science approach.

It is worth noting that, despite the differences, domain analysis in Information Science can be used as a basis for domain modeling in software development projects, platforms, digital tools, or whatever else is needed. It provides valuable information about the structure of knowledge and the needs of users in a specific domain, and is therefore very valuable.

To conclude this article, my fellow SEOs, I want to reaffirm that Hjørland's eleven approaches to domain analysis offer a very rich and comprehensive set of tools and perspectives to be used in our projects, especially in our initial research and its necessary updates.

By considering the specific characteristics of each domain, Information Science contributes to the organization, access, and effective use of information in diverse areas of knowledge. Domain analysis is therefore our ally in developing information systems and services that meet the needs of your clients and your clients' clients.

Universal Applications of Domain Analysis

The beauty of domain analysis lies in its versatility. It is not limited to complex academic areas; it is applicable to any domain of knowledge. From scientific fields such as molecular biology and information science, to hobbies such as stamp collecting, and even to social movements such as veganism, domain analysis adapts to every context.

Each domain, however unique, has its own internal structure, its own unique "DNA," and its own "discourse communities"—groups of people united by a common interest in that domain. For example, scientists dedicated to the study of climate change form a discourse community, sharing knowledge and debating ideas. Similarly, enthusiasts of a specific video game also constitute a community, with their own slang and forms of interaction.

Understanding these discourse communities is fundamental for effective domain analysis. By delving into the particularities of each community, we can uncover their forms of communication, their underlying values, and how they construct and share knowledge.

By adopting a systematic approach to domain analysis, you will be equipped with a powerful tool to uncover the deepest layers of any area of ​​knowledge you wish to explore. Whether you are a librarian, archivist, information scientist, database designer, researcher, educator, or policymaker, domain analysis offers a new perspective for approaching the organization of knowledge.

For the research in this article, I used a new tool created by Google Gemini called Deep Research. It greatly helped me find relevant information to guide my questions about the term and pointed me in new directions in my study. It uses only validated references and searches the web and databases. I suggest you test it to see if it can help you (as it helped me) study the subjects you need to study.

References cited

1. HJØRLAND, B. Domain analysis in information science. Journal of Documentation , vol. 58, no. 4, p. 422–462, 1 Aug. 2002.
2. HJØRLAND, B.; GNOLI, C. Domain analysis (IEKO) . Available at: Accessed on: December 20, 2024.