From Discovering Conceptual Structures to Cross-Domain Knowledge Exploration and Decision Support
Our Vision for ConceptMiner
Organizations accumulate vast amounts of data and knowledge through research and development, marketing, quality management, production, strategic planning, and other business activities. However, classifying and aggregating this information does not necessarily reveal the underlying structures, relationships, and opportunities hidden within it.
ConceptMiner aims to combine machine learning-based conceptual structure analysis with large language models (LLMs), supporting activities ranging from data classification and visualization to pattern discovery, cross-domain knowledge exploration, and hypothesis generation.
While conventional classification systems primarily answer the question, “Which category does this data belong to?”, ConceptMiner addresses broader questions: “What conceptual structures exist within the data?”, “What relationships connect different categories or domains?”, and “What opportunities might lie beyond existing classification systems?”
Our initial application is ConceptMiner R&D, currently under development to support technology exploration and hypothesis generation in research and development.
Core Technologies
ConceptMiner is built upon machine learning technologies, including conceptual structure modeling with Growing Neural Gas (GNG), structural analysis using Minimum Spanning Trees (MST), clustering, and profile analysis.
By combining these technologies with LLM-based information extraction, conceptual abstraction, analogical exploration, and hypothesis generation, ConceptMiner seeks to bring together the complementary strengths of traditional data mining and generative AI.
Our future development vision includes the following capabilities:
- Conceptual Structure Analysis: Analyze similarities, differences, distributions, and cluster structures within textual and numerical data.
- Classification and Pattern Discovery: Combine analysis based on established classification systems with the discovery of previously unidentified patterns.
- Cross-Concept Relationship Analysis: Explore relationships between different groups of concepts, such as technologies and applications, customer needs and product features, or quality defects and production conditions.
- Cross-Domain Exploration: Discover relevant knowledge through structural and functional similarities without being restricted by existing disciplines or classification systems.
- Hypothesis Generation and Evaluation: Generate new hypotheses from discovered relationships and patterns, and support their evaluation using relevant information and supporting evidence.
- Continuous Change Analysis: Track changes in conceptual structures and relationships as new data becomes available.
Potential Application Areas
ConceptMiner’s underlying technologies have potential applications across a wide range of business and scientific disciplines.
The following areas represent our current vision for future applications. They do not constitute commitments to develop or release separate commercial products.
ConceptMiner’s underlying technologies have potential applications across a wide range of business and scientific disciplines.
The following areas represent our current vision for future applications. They do not constitute commitments to develop or release separate commercial products.
| Application Area | Potential Capabilities |
|---|---|
| ConceptMiner R&D | Extraction of technical elements from scientific papers and patents, technology mapping, cross-domain exploration, and generation of new R&D themes and application hypotheses |
| ConceptMiner Strategy | Competitive structure analysis, alignment of organizational capabilities with market opportunities, new business opportunity exploration, and strategic scenario comparison |
| ConceptMiner Marketing | Voice of Customer (VoC) analysis, customer needs classification, identification of unmet needs, and product concept generation |
| ConceptMiner Quality | Quality defect classification and structural analysis, discovery of similar defects, analysis of relationships with production factors, and reuse of corrective action knowledge |
| ConceptMiner Production | Multivariate process data analysis, operating state classification, anomaly pattern discovery, and analysis of relationships between manufacturing conditions and product quality |
| ConceptMiner Life Sciences | Structuring research knowledge about molecules, cells, and diseases; exploring similarities in mechanisms of action; and generating hypotheses for pharmaceutical and life sciences research |
| ConceptMiner Knowledge | Structuring internal documents and technical knowledge, discovering similar cases, facilitating knowledge reuse, and enabling cross-domain knowledge exploration |
| ConceptMiner IP | Conceptual analysis of patent portfolios, identification of changes in technology landscapes, competitive technology comparisons, and exploration of technological white spaces |
| ConceptMiner Supply Chain | Classification of suppliers and procurement items, structural analysis of supply risks, and exploration of alternative technologies and sourcing options |
Beyond Classification: Analyzing Relationships Between Concepts
A central element of ConceptMiner’s future vision is the ability to analyze relationships between different groups of concepts, rather than simply classifying individual datasets.
In R&D, for example, this may involve analyzing relationships between technical elements and application hypotheses. In marketing, it may involve customer needs and product features. In quality management, it may involve defect characteristics and manufacturing conditions. In strategic planning, it may involve organizational capabilities and market opportunities.
By analyzing these relationships, ConceptMiner could help identify connections that are difficult to discover through conventional classification or keyword-based searches.
Another potential direction is comparing conceptual structure models developed from different products, organizations, or industries. Even when terminology and classification systems differ, these models may reveal common characteristics, functional similarities, or analogous structural patterns.
This approach represents an extension of Concept Exploration, a research philosophy and methodology that Mindware Research Institute has pursued since the 1990s.
Rather than restricting exploration to established categories or predefined keywords, Concept Exploration seeks to discover meaningful connections through abstraction, structural comparison, and the examination of relationships across different knowledge domains.
Development and Delivery Strategy
Our vision is to develop ConceptMiner as a common analytical platform rather than a collection of independently developed applications.
The platform would provide shared capabilities for data processing, conceptual structure modeling, classification, pattern discovery, relationship analysis, and hypothesis generation. Domain-specific functionality would be implemented through specialized data structures, analytical methods, evaluation criteria, and user interfaces.
This architecture could allow analytical capabilities developed for R&D to be extended into quality management, marketing, strategic planning, and other areas, while also enabling knowledge from different business functions to be analyzed together.
Our immediate priority remains the development and practical deployment of ConceptMiner R&D. Other applications will be explored progressively, based on technical feasibility, validation results, and actual user needs.
Collaboration and Early Access Opportunities
Mindware Research Institute welcomes discussions with companies, research institutions, and technology partners interested in exploring potential applications of ConceptMiner beyond R&D.
We are particularly interested in challenges involving complex classification problems, relationships between different types of data and knowledge, the discovery of previously unidentified patterns, and the exploration of new analytical approaches.
If your organization faces analytical challenges that conventional classification, search, or data mining systems cannot adequately address, we would welcome the opportunity to discuss potential applications and collaborative exploration.
For information about our current R&D-focused development, please visit the ConceptMiner R&D product page.
Note: This page presents the technological direction and future application vision for ConceptMiner. Some capabilities described here have not yet been implemented or validated. References to potential applications do not imply confirmed product development plans, availability, or release dates.