Discover everything you need to know about semantic knowledge models. Learn what they are and how they can elevate your enterprise information architecture.
What is a semantic knowledge model
Table of contents:
What is a semantic knowledge model?
A semantic knowledge model captures and represents the meaning of data by explicitly defining domain-relevant objects and concepts, and the relations between them. Semantic models enrich data within a knowledge graph with context and meaning that is both human-understandable and machine-interpretable.
It’s like a powerful lens that grants you a bird's eye view of your data, revealing intricate hierarchies, precise definitions and crucial context; it unlocks unparalleled access to the deepest layers of interpretation, empowering you to grasp the essence and true meaning of your data.
All organizations have their own vocabulary and concepts unique to their company and industry. Semantic models can capture this domain-specific knowledge — whether from structured text, unstructured text or stored in the minds of domain experts — into a central repository. This creates a shared understanding of these concepts and terminology, and bridges the semantic gap between departments or colleagues and especially between business and IT, thereby fostering better collaboration and decision-making.
Use cases for semantic models
Semantic models can, for example, be used to build a semantic layer for an organization’s Enterprise Information Architecture (EIA). The semantic EIA bridges the gap between business and IT by mapping business terminology and concepts to IT terminology and physical systems and adding relevant semantic context to data. End users are empowered to quickly find the information they are looking for using the terminology they are used to and that is mapped to their business processes.
Semantic knowledge model vs. ontology vs. knowledge graph
How does a semantic model differ from an ontology or a knowledge graph?
Semantic model vs. ontology
We sometimes use the terms semantic model, semantic knowledge model and ontology interchangeably as semantic models are achieved through an ontology and further enrichment with one or more hierarchical vocabularies to capture specific terminology. Ontologies refer to formal and explicit definitions of the objects, concepts and relations within a domain.
In this blog post, we define ontologies as:
“Semantic data models that define the types of entities that exist in your domain and the properties that can be used to describe them. An ontology combines a representation, formal naming, and definition of the elements (such as classes and relations) that define the domain of discourse.”
Semantic model vs. knowledge graph
A knowledge graph on the other hand is a “graph structure that visualizes the relations between interlinked entities representing real-world objects and concepts — such as people, places or even organizational structures.” A semantic model can be layered within a knowledge graph, which enhances the knowledge graph and turns it into a powerful tool that adds context and semantic meaning to the data, making it both human and machine-interpretable. By introducing these formal, explicit definitions of the concepts and relations within a domain, it enriches the knowledge graph and the data it holds.
However, not all knowledge graphs have a semantic model, which is why we often call the knowledge graphs described above as semantic knowledge graphs.
Role of semantic knowledge models in AI and machine learning
Knowledge graphs (and their underlying semantic model) and AI have a symbiotic relationship, where, when paired together, they enhance and advance the capabilities of each technology. For example, because semantic knowledge models create explicit definitions of data that are both human and machine-interpretable, this information can be used by LLMs to identify and extract causal relationships from unstructured text and further enrich the knowledge graph, as well as train and inform AI applications and enhance their trustworthiness, explainability and accuracy.
Converging a semantic model with AI ensures that insights generated or actions taken by LLMs, for example, are explainable, traceable, and trustworthy. The structured framework provided by semantic models makes outputs traceable within the model and helps explain why the LLM arrived at a particular conclusion or recommendation. The ability to trust AI-powered applications is especially critical in industries like healthcare and finance, but it is also essential for preventing misinformation and mitigating hallucinations in broader contexts.
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