S2W builds domain-specific ontologies and knowledge graphs necessary for solving complex problems. This process follows a six-step methodology, ultimately forming a virtuous cycle between user and data after the initial design is completed.

Defining the core issues faced by an organization or user is the essential first step. This involves clarifying the scope and objectives of the analysis—specifying what type of information needs to be understood, uncovered, or connected.

At this stage, S2W determines which concepts are critical within the target domain and how they relate to one another. Based on this understanding, we design the initial structure of the ontology, which serves as a foundation for subsequent data analysis and knowledge graph construction.

Once the core issues are defined, the next step is to establish clear objectives and expected outcomes for the analysis. This includes determining how the identified problems should be approached and what concrete goals or strategies will guide the process.

Through this step, technical indicators such as precision, processing speed, scalability, and applicability are set. It also clarifies what results can be achieved by leveraging the defined ontology structure and data (e.g., insight analysis, security monitoring systems, domain-specific generative AI based on internal data).

Because analytical objectives are often not singular, it is important to outline multiple potential scenarios and use cases. Doing so ensures a flexible foundation that can be expanded and refined in subsequent stages.

To construct the key concepts, entities, and relationships needed for
ontology design, we begin by identifying the data required to achieve the
defined objectives.This includes assessing data availability, format (e.g.,
.docx, .pdf), and type.

We then establish a pipeline to automatically detect and classify entities
within the data,standardizing their representation and meaning to ensure
machine-readable semantic understanding.

Within each domain, we identify core entities and analyze their
relationships.By interpreting both the definitions of nodes and their
contextual relationships,we lay the foundation for a structured, context-
aware ontology and knowledge graph.

Ontology is a structured method of defining and connecting information and
knowledge. In particular, a domain-specific ontology serves as a blueprint
that organizes key concepts and their relationships within a specific field,
enabling machines to understand the semantics and connections between
data entities.

At this stage, the previously extracted entities and relationships are
modeled to reflect how each concept is semantically connected.
Relationships can be unidirectional or bidirectional, hierarchical or network-
based, and are informed by both domain expertise and data analysis.

The ontology functions as a schema for building the knowledge graph and
forms the foundation for semantic reasoning and advanced analysis.

The knowledge graph is a graph-based knowledge structure created by
populating the previously designed ontology with actual data.

In a domain-specific knowledge graph, both internal and external datasets
are mapped to the ontology framework and represented as nodes and
edges. Knowledge elements extracted from multiple domains are
semantically connected within a unified network, enabling advanced
analysis such as relationship inference and similarity detection.

The constructed knowledge graph is tested in real-world user environments
to evaluate its effectiveness and practical applicability. Feedback from
users plays a critical role in refining the ontology, enriching the data, and
reassessing analytical goals—initiating a virtuous cycle of continuous
improvement.

Based on validation results, the process returns to earlier stages such as
problem definition and goal setting. Through this iterative enhancement, the
ontology and knowledge graph are continuously refined and expanded,
enabling organizations and users to manage and operate data more
effectively and efficiently.