Structural conceptual modeling is a broad area within the field of computer science and information systems that focuses on representing and modeling the structure of data, information, and systems. It involves creating abstract models that capture the essential elements and relationships within a domain. Some of the key themes and concerns within structural conceptual modeling include:

1. Data Modeling: This is a fundamental aspect of structural conceptual modeling. It involves creating models to represent data and information within an organization or system. Common techniques include Entity-Relationship Diagrams (ERD), Unified Modeling Language (UML) class diagrams, and more specialized data modeling approaches like ontologies.
2. Entity-Relationship Modeling: Entity-relationship diagrams are used to represent entities (objects or concepts) and their relationships in a structured manner. This is especially useful in database design and information systems development.
3. Class Modeling: In the context of object-oriented programming and UML, class modeling is used to represent classes, their attributes, methods, and relationships. It’s a foundational concept for software design.
4. Ontology Modeling: Ontologies are formal representations of knowledge in a particular domain. Ontology modeling involves defining concepts, properties, and relationships within a specific knowledge domain. It’s often used in knowledge engineering and semantic web applications.
5. Schema Design: When working with databases, structural conceptual modeling includes designing database schemas that define the structure and organization of data tables, their attributes, and the relationships between them.
6. Information Architecture: This aspect focuses on organizing and structuring information within an organization to facilitate efficient retrieval and management. It often involves defining taxonomies, hierarchies, and metadata.
7. System Architecture: In the context of software and systems development, structural modeling can extend to system architecture, where the components of a system and their relationships are modeled to ensure a clear understanding of how the system works.
8. Hierarchical Modeling: Hierarchical structures are common in various domains, and modeling hierarchies is a key concern in structural conceptual modeling. This can involve representing organizational hierarchies, product hierarchies, or any other hierarchical relationships.
9. Normalization: In database design, the process of normalization is crucial to eliminate redundancy and ensure data integrity. It involves structuring data in a way that minimizes data duplication and anomalies.
10. Taxonomy and Classification: Creating taxonomies and classification systems to categorize and organize data or information is an important part of structural conceptual modeling, especially in domains like information retrieval and content management.
11. Graph Theory: In some cases, structural modeling involves the use of graph theory to represent complex relationships and structures, such as network topologies, social networks, or dependency graphs in software systems.
12. Semantic Modeling: This aspect of structural modeling deals with capturing the meaning or semantics of data and relationships, making it particularly relevant in the context of the semantic web and knowledge representation.

These themes are interrelated and can be applied in various combinations depending on the specific goals and requirements of a modeling project within a particular domain or discipline. The choice of modeling techniques and approaches will depend on the nature of the problem being addressed and the tools and methodologies available.