Digital Humanities

This approach focuses on how terms are understood and processed in the human mind. It examines the relationship between language, thought, and cultural context, providing insights into how terms acquire meaning and are used.

Semantic analysis of terminology establishes the idea that each term represents a notion. Notions are the results of human categorisation of the world representing the abstract ideas or mental constructs. They structure human experience converting it into systematised knowledge. To describe a notion, logical school of language studies and cognitive linguistics apply the terms  

"volume" and "content" of a notion that refer to the scope and the specific information that a notion encompasses.

Volume of a notion refers to its breadth or scope. It encompasses how broad or narrow, general or specific, a notion is.

For example, the notion of "vehicle" has a large volume as it includes a wide range of items (cars, bikes, boats, planes, etc.). In contrast, the notion of "sedan" has a smaller volume, referring more specifically to a type of car. The volume of a notion determines how many objects or ideas can be classified under it.

Content of a notion refers to the specific attributes, characteristics, or information that define a particular notion. It includes the defining features or the essential qualities that make up the notion. For instance, the content of the notion "bird" might include attributes like feathers, beak, laying eggs, and the ability to fly (though not all birds fly, it's often a perceived characteristic). The content of a notion is what distinguishes it from other notions and helps in identifying and categorizing specific items or ideas within that concept.

The content and the volume of a notion are interdependent.

Content Determines Volume: The content of a notion, with its defining features and characteristics, essentially determines its volume. For example, the notion of "animal" has a broad volume because its content includes the fundamental characteristics of animals (living, breathing, moving organisms) but is not overly specific. This allows for a wide range of entities (from insects to whales) to be categorized under this notion.

Volume Influences Perception of Content: Conversely, the perceived volume of a notion influences our understanding of its content. A broader volume might lead to a more generalized or abstracted perception of content, whereas a narrower volume might result in a more detailed or specific understanding.

The application of cognitive approach to terminology are in their combination with IT and AI. There are two directions: ontologies and semantic networks. These are instruments developed by artificial intelligence, computer science, and information science to represent knowledge in structured forms. These are used for organizing and structuring knowledge about terms, showing the relationships between different terms and concepts. They play a crucial role in enabling machines to process, understand, and respond to complex information.

An ontology in the context of computer science is a structured framework for organizing information and represents formal knowledge as a set of concepts within a domain, and the relationships between those concepts. An ontology is an explicit specification of a conceptualization. It provides a shared vocabulary for a domain and defines the meaning of terms and the relationships between them.

Components:

Classes (or Concepts): Categories or types of objects or ideas within a domain.

Attributes: Features or properties that the objects can have.

Relations: The ways in which objects and classes can be related to one another.

Individuals: Instances or actual objects in the domain that the ontology describes.

In this way, the conceptual base or frame of ontology is taxonomy. Taxonomy provides a structured and systematic way to categorize and organize diverse entities based on shared characteristics or attributes is a classification system, and it plays a crucial role in various fields, including biology, information management, and commerce.

Taxonomy is a system or method of classifying and organizing things, typically living organisms, into hierarchical categories or groups based on their shared characteristics or attributes. The primary purpose of taxonomy is to provide a systematic and structured way to understand and categorize the diversity of life on Earth, making it easier for scientists and researchers to study and communicate about different species and their relationships.

The Linnaean taxonomy, developed by Carl Linnaeus in the 18th century, is one of the most well-known and widely used systems of taxonomy. It categorizes living organisms into a hierarchical structure consisting of several levels or ranks, from broad to specific:

Kingdom

Phylum

Class

Order

Family

Genus

Species

Each level represents a progressively more specific grouping of organisms. For example, all species within a particular genus share more characteristics in common than species in different genera within the same family. This hierarchical classification helps scientists organize and categorize the vast diversity of life.

Modern taxonomy also incorporates molecular and genetic data to complement traditional morphological characteristics, which has led to revisions and refinements in the classification of certain organisms.

Taxonomy is not limited to the classification of living organisms. It can be applied to other fields as well, such as:

Document and Information Management: Taxonomy is used to classify and organize documents, files, and information in a structured manner, making it easier to search, retrieve, and manage data within organizations.

Web Content and Information Architecture: In web development and content management, taxonomy is used to create hierarchical structures for organizing website content, improving user navigation and search functionality.

Library Science: Taxonomy is essential in library cataloguing systems to classify books, publications, and other library materials, ensuring efficient organization and retrieval of information.

Botanical Gardens and Zoos: Taxonomy is applied to organize and label plants and animals on display, helping visitors understand the relationships and characteristics of different species.

Business and Product Classification: In business and commerce, taxonomy is used to categorize products, services, and inventory, facilitating inventory management, e-commerce, and supply chain operations.

Creating an ontology involves a systematic process of defining concepts, relationships, and properties within a specific domain of knowledge. Here are the general steps to create an ontology, including conceptual part and IT part:

Conceptual part

1) Define the Scope and Purpose

Clearly define the scope and purpose of your ontology. What is the specific domain of knowledge you want to model, and what are the goals you want to achieve with your ontology?

2) Identify Concepts

Identify and list the key concepts or entities within your chosen domain. These concepts represent the building blocks of your ontology.

3) Define Relationships

Determine how the concepts are related to each other. Consider the different types of relationships that exist between concepts, such as "is a," "part of," "has property," "related to," etc.

4) Specify Properties

Define the properties or attributes associated with each concept. Properties describe the characteristics, features, or attributes of the concepts. These properties may include data types (e.g., string, integer) and constraints.

5) Create a Taxonomy or Hierarchy

Organize the concepts into a hierarchical structure or taxonomy. This hierarchy should reflect the relationships between concepts and their levels of specificity. Typically, this structure follows a tree-like or parent-child relationship.

IT part depends on the choice of a formal representation language to express your ontology. Common languages for ontology development include OWL (Web Ontology Language), RDF (Resource Description Framework), and RDFS (RDF Schema).

6) Create Ontology Classes

Create classes for each concept in your ontology. Classes serve as the formal representation of concepts and their properties. Specify the class hierarchy using subclass and superclass relationships.

7) Define Properties and Restrictions

Define properties and attribute restrictions for each class. This includes specifying which properties are applicable to each class and defining any domain and range restrictions on properties.

8) Add Instances

Create instances or individuals of the ontology classes. Instances represent specific real-world objects or entities within your domain. Link instances to their respective classes and specify their property values.

9) Establish Relationships

Define relationships between instances using the defined relationship properties. Connect instances to other instances based on the relationships they have in the real world.

10) Test and Validate

Test your ontology to ensure that it accurately represents the knowledge within the domain. Check for consistency, completeness, and logical correctness. Validation tools and reasoners can help with this step.

11) Document

Document your ontology thoroughly. Provide descriptions, definitions, and examples for concepts, relationships, and properties. Clear documentation is essential for users and future maintainers of the ontology.

12) Publish and Share

If your ontology is intended for broader use, consider publishing and sharing it with the relevant community or stakeholders. Make it available in a format that others can access and use.

13) Maintain and Evolve

Ontologies are not static; they may need to evolve over time as knowledge in the domain changes. Regularly update and maintain your ontology to ensure its relevance and accuracy.

Ontologies have various key applications across different domains, and they are used by a wide range of professionals and industries. Some of the key applications of ontologies include:

Knowledge Representation: Ontologies are primarily used for representing knowledge in a structured and standardized format. They provide a formal and explicit way to define concepts, their relationships, and properties, making it easier to share and understand knowledge.

Semantic Web: Ontologies play a crucial role in the Semantic Web, enabling machines to understand and interpret the meaning of web content. They facilitate data integration and interoperability by providing a common vocabulary for describing data and information on the web.

Information Retrieval: Ontologies can improve information retrieval systems by allowing for more precise and context-aware search queries. They help in matching user queries with relevant documents and data by considering semantic relationships.

Natural Language Processing (NLP): Ontologies are used in NLP applications for disambiguation, entity recognition, and sentiment analysis. They help machines understand the meaning of words and phrases in context.

Healthcare: Ontologies are applied in healthcare to standardize medical terminologies, define relationships between medical concepts, and support decision support systems, clinical data integration, and medical knowledge management.

Bioinformatics: Ontologies are used to represent biological and genomic data, aiding in the integration of various biological databases and enabling researchers to make sense of complex biological relationships.

Robotics and AI: Ontologies are employed in robotics and artificial intelligence to represent domain knowledge, making it easier for robots and AI systems to understand and interact with the real world.

E-commerce: Ontologies help in product classification, recommendation systems, and product search by providing a structured way to describe products, their features, and relationships.

Information Governance: Enterprises use ontologies to manage and categorize their data, ensuring data consistency, quality, and compliance with industry standards and regulations.

Geography and Geospatial Applications: Ontologies are used to represent geospatial information, enabling systems to understand and process location-based data, such as maps, GPS data, and geographic information systems (GIS).

Education and E-Learning: Ontologies support the development of intelligent tutoring systems and personalized learning platforms by modelling educational content, learning objectives, and learner profiles.

Industry-specific Applications: Various industries, such as finance, manufacturing, aerospace, and energy, use ontologies to model domain-specific knowledge and facilitate data integration and decision-making.

Who Uses Ontologies:

Researchers and scientists in various fields, including computer science, biology, and medicine, use ontologies to formalize and share their knowledge.

Software developers and engineers use ontologies to build semantic applications and systems that can understand and process data more intelligently.

Knowledge engineers and experts design and maintain ontologies to capture and represent domain-specific knowledge.

Businesses and organizations in various industries leverage ontologies to improve data management, decision support, and knowledge sharing.

Government agencies use ontologies for data integration, policy modelling, and information retrieval.

Semantic web developers and architects work on projects related to the Semantic Web, where ontologies are fundamental to achieving the web's vision of machine-readable data and interconnected information.

In summary, ontologies are versatile tools with applications in a wide range of domains and are used by professionals, researchers, and organizations to represent, share, and leverage knowledge effectively.

Semantic Networks

Definition: A semantic network is a graphical representation of knowledge that depicts relationships between concepts. It is a form of knowledge representation that visualizes concepts (or nodes) and the connections (or edges) between them.

Characteristics: Semantic networks are often used for associative representations, where the links between nodes represent the relationship between the ideas. These networks can be simple, with only one kind of relationship, or complex, with multiple relationship types.

Uses: They are used in natural language processing, cognitive science, and knowledge representation. They help in understanding and modelling how human beings process and structure knowledge.

Open access products based on semantic networks provide access to knowledge and data in various domains. Here are some examples:

Wikidata: Wikidata is an open knowledge base that uses a semantic network to store structured data about a wide range of topics, including people, places, and concepts. It serves as a central hub for linked data and is used by Wikipedia and other projects.

DBpedia: DBpedia is a project that extracts structured information from Wikipedia and represents it as a semantic network. It provides structured data about people, places, and things described in Wikipedia articles.

Linked Open Data Cloud: The Linked Open Data (LOD) Cloud is a collection of linked datasets from various sources that are interconnected through semantic relationships. These datasets cover diverse domains, such as culture, science, government, and more.

Freebase (Now part of Wikidata): Freebase was a community-driven knowledge graph that aimed to organize information about the world's people, places, and things. It was acquired by Google and later contributed to Wikidata.

YAGO: YAGO (Yet Another Great Ontology) is a knowledge base and ontology that contains information about millions of entities and their relationships, making it suitable for tasks like entity linking and knowledge retrieval.

WordNet: WordNet is a lexical database of the English language that is structured as a semantic network of words and their relationships, including synonyms, hypernyms (is-a relationships), and hyponyms.

SNOMED CT: SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) is a comprehensive clinical terminology and ontology that represents relationships between medical concepts, making it valuable for healthcare informatics.

Linked Data from Government Sources: Various government agencies and organizations worldwide publish linked open data, including information about government services, statistical data, and geographic information.

BabelNet: BabelNet is a multilingual semantic network that connects words, phrases, and concepts in multiple languages, providing a valuable resource for natural language processing and machine translation.

Cyc: Cyc is a knowledge base and ontology that aims to capture common-sense knowledge about the world. It includes information about everyday concepts and their relationships.

Differences and Similarities

Common Ground: Both ontologies and semantic networks are used to represent knowledge, but they do so in slightly different ways. Ontologies are more rigid in structure and are concerned with formalizing the types of objects and their interrelations in a domain, while semantic networks are often more flexible and visually oriented.

Purpose: Ontologies are typically used for larger, more complex systems that require a detailed and formal representation of knowledge in a domain, such as in the Semantic Web. Semantic networks are more suited for tasks like cognitive modelling and understanding language processing.

Representation: Ontologies often require a more formal language for definition, like OWL (Web Ontology Language), while semantic networks can be represented using simpler graphical forms.

In summary, ontologies and semantic networks are powerful tools in organizing and representing knowledge. They enable machines to process complex information and are fundamental in fields like AI, natural language processing, and knowledge management.

Looking at the application of the basics of cognitive approach, we can see the interdisciplinary approach of this paradigm. The study of terms often involves a blend of linguistics, information science, cognitive psychology, and domain-specific knowledge. This interdisciplinary approach ensures a comprehensive understanding of terminology in context.

Another trend is crowdsourcing and employment of collaborative platforms, such as Wiktionary or specialized forums that allow for the collective creation and refinement of terminological databases, benefiting from the knowledge of a vast and diverse user base. The use of social media and online communities is in the same vein. The study of language and terms now often includes analysis of informal and evolving usage patterns as seen in social media, forums, and online communities.