Find related topics. | Discover entities. | See connections. | Build a topical map.
Building topical map: Starting…
Semantic data model: History, Technology & Products
A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of…
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
More settings
Semantic data model topic overview
The analysis highlights History, Technology and Products as prominent areas in the source structure around Semantic data model.
Related topics
22
Source areas
3
Connected nodes
25
Extracted relationships
114
Concept neighborhoods
14
Bridge connections
25
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
In software engineering · 8 topics
Overview · 8 topics
History · 6 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Leading companies
U.S. Air Force as Integrated Computer-Aided Manufacturing program
Main facilities
Planning of Data Resources, Building of Shareable Databases, Evaluation of Vendor Software, Integration of Existing Databases
Process type
semantics-based database description
Product(s)
Gellish (2005), ISO 15926-2 (2002)
Year of invention
mid-1970s
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Semantic data model connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Semantic data model shows recurring relationship patterns in the source. For example, Semantic data model → ACM SIGMOD Int’l, ACM Transactions, Alfonso, Austin, Bekke, Cardenas, Computer Science, Conf, Data, Data Base Applications, Database Description, Database Design, Database Systems, Dennis McLeod, Hammer, In, ISBN, Johan, June, Krishnarao Another extracted example is Semantic data model → Air Force, As, ICAM, ICAM Definition, ICAM Program, IDEF, IDEF0, IDEF1, IDEF1X, IDEF2, Integrated Computer-Aided Manufacturing, Methods, Program, The, The ICAM Program, Use. Use these groups to spot repeated connection types before inspecting the individual relationships.
Semantic data model
Top relations
related to Further reading· 52
Semantic data model → ACM SIGMOD Int’l, ACM Transactions, Alfonso, Austin, Bekke, Cardenas, Computer Science, Conf, Data, Data Base Applications, Database Description, Database Design, Database Systems, Dennis McLeod, Hammer, In, ISBN, Johan, June, Krishnarao
related to history· 16
Semantic data model → Air Force, As, ICAM, ICAM Definition, ICAM Program, IDEF, IDEF0, IDEF1, IDEF1X, IDEF2, Integrated Computer-Aided Manufacturing, Methods, Program, The, The ICAM Program, Use
has application· 14
Semantic data model → Air Force Integrated Information, Building, By, DBMS, Evaluation, I2S2, In, Integration, Planning, Since, Some, Support System, The, With
related to In software engineering· 9
Semantic data model → Eiffel Tower, Facts, For, It, Object-RelationType-Object, Paris, Such, This, Typically
related to overview· 9
Semantic data model → According, Artificial Intelligence, DBMS, Klas, Schrefl, That, The, Therefore, Thus
related to External links· 7
Semantic data model → Bekke, BI, Data, Semantic, Technical, WikidataSemantic Data Modeling Johan, Wiktionary-logo-en-v2
is a· 2
Semantic data model → abstraction that defines how the stored symbols, abstraction which defines how the stored symbols relate to the real world
Leading companies· 1
Semantic data model → U.S. Air Force as Integrated Computer-Aided Manufacturing program
Main facilities· 1
Semantic data model → Planning of Data Resources, Building of Shareable Databases, Evaluation of Vendor Software, Integration of Existing Databases
Process type· 1
Semantic data model → semantics-based database description
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Semantic data model relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 114 structured relationships around Semantic data model. Examples in this analysis include Semantic data model → Leading companies → U.S. Air Force as Integrated Computer-Aided Manufacturing program and Semantic data model → Main facilities → Planning of Data Resources, Building of Shareable Databases, Evaluation of Vendor Software, Integration of Existing Databases. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Semantic data model
Leading companies
U.S. Air Force as Integrated Computer-Aided Manufacturing program
1.00
infobox
Semantic data model
Main facilities
Planning of Data Resources, Building of Shareable Databases, Evaluation of Vendor Software, Integration of Existing Databases
1.00
infobox
Semantic data model
Process type
semantics-based database description
1.00
infobox
Semantic data model
Product(s)
Gellish (2005), ISO 15926-2 (2002)
1.00
infobox
Semantic data model
Year of invention
mid-1970s
1.00
infobox
Semantic data model
is a
abstraction that defines how the stored symbols
0.90
text
Semantic data model
is a
abstraction which defines how the stored symbols relate to the real world
0.90
text
Semantic data model
has application
Some
0.60
section
Semantic data model
has application
Planning
0.60
section
Semantic data model
has application
The
0.60
section
Semantic data model
has application
Building
0.60
section
Semantic data model
has application
DBMS
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Semantic data model bring nearby vocabulary together. In this analysis, examples include Semantic, Model and Modeling. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Semantic data model
Semantic
Model
Modeling
Models
Databases
Information
Environment
Description
Gellish
Building
Systems
Conceptual
semantic data model
Semantic
Model
Used
Modeling
Models
Databases
Database
System
Information
Environment
Description
Software
database model
Sdm
Semantic
Description
Specification
Used
Design
Systems
Conceptual
Database
Model
Modeling
Application
database management system
Serve
Used
Sdm
Within
Description
Specification
Design
Systems
Conceptual
Building
Semantics
Model
conceptual view
Database
Information
Relational
Software
Specification
Design
Management
Building
System
Model
Used
Data
conceptual data model
Semantic
Model
Used
Modeling
Database
Models
Information
Relational
Software
Specification
Databases
Design
data model
Semantic
Model
Used
Modeling
Models
Databases
Database
System
Information
Environment
Description
Software
data
Semantic
Model
Modeling
Models
Databases
Information
Building
Conceptual
Used
Database
Resources
Software
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Semantic data model, one of the stronger structural bridges in this analysis connects Semantic data model with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Min side: 3
Semantic data model — Overview· splits 17 ⟂ 9Semantic data model — In software engineering· splits 17 ⟂ 9Semantic data model — History· splits 19 ⟂ 7
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Semantic data model
Nodes26
Edges25
Triples114
Avg. degree1.92
Density0.076923
Components1
Source & methodology
TTTA analyzes the structure around Semantic data model to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Technology & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
