Researching meaning under conditions of incoherence
BSI investigates the structures through which meaning is formed, stabilised and transformed. Our research lies at the intersection of semantics, philosophy of language, logic, law and artificial intelligence, with particular attention to situations in which conceptual systems must operate under conditions of ambiguity, contradiction or incoherence.
Our work combines theoretical investigation with the development of methods capable of improving conceptual precision, interpretive robustness and inferential traceability.
Meaning, Language & Interpretation
Our research examines how meaning emerges, stabilises and transforms across linguistic, conceptual and interpretive systems. Particular attention is given to the relations between semantics, pragmatics and philosophy of language, and to the ways in which context, categorisation and conceptual structures shape interpretation.
This work provides the theoretical foundation for investigating how both human and artificial systems construct, preserve and revise meaning when confronted with ambiguity, competing interpretations or conceptual instability.
Human–AI Interaction
Human–AI interaction raises a fundamental semantic problem: human and artificial systems do not necessarily construct, organise or stabilise meaning in the same way. Our research examines the conditions under which these heterogeneous systems can nevertheless establish sufficiently shared interpretive frameworks for reliable communication and reasoning.
Particular attention is given to semantic alignment, contextual interpretation and the management of ambiguity, as well as to the preservation of semantic and normative continuity across increasingly autonomous and agentic systems.
Concepts, Terminology & Normalization
Conceptual precision depends on terminological precision. BSI investigates how concepts are defined, differentiated and related within specialised domains, and how terminological systems can preserve these distinctions across disciplines, languages and computational environments.
Particular attention is given to normalization and knowledge representation as means of reducing semantic drift while preserving legitimate plurality, contextual variation and conceptual evolution.
Incoherence, Contradiction & Reasoning
Incoherence is not merely a failure of reasoning. It may arise when heterogeneous conceptual systems, incompatible assumptions or competing interpretive frameworks coexist within the same informational environment. BSI investigates how such forms of incoherence can be identified, described and managed without prematurely reducing them to a single framework.
Our research explores approaches to reasoning in which contradiction does not necessarily entail collapse. Particular attention is given to the articulation between consistency, paraconsistency and context-sensitive interpretation, and to their possible contribution to more robust forms of human and artificial reasoning.
A related line of investigation examines how reasoning systems can suspend unsupported claims without suspending productive inference, preserving uncertainty and disagreement while maintaining the possibility of further reasoning.
QualComputing
QualComputing explores a quality-first approach to computation in which the preservation of meaning, conceptual distinctions and inferential traceability is treated as a primary requirement rather than as a secondary consequence of computational performance.
Rather than assuming that greater computational power necessarily produces better interpretation, this research examines the conditions under which computational systems can remain faithful to the conceptual structures on which their reasoning depends.
Within this framework, BSI is exploring the possible articulation of consistency-oriented and paraconsistent approaches to reasoning. This includes the development of provisional research concepts such as ISOc and PARAc, whose relations and operational roles remain under investigation.
Research Programme
BSI develops its research programme through a combination of theoretical analysis, conceptual modelling and exploratory methodological development. The objective is not to impose a unified theory of meaning, but to investigate the conditions under which heterogeneous systems of knowledge can interact without erasing the distinctions that make them intelligible.
Current research focuses on the relations between semantic structure, interpretation, inconsistency and reasoning, with particular attention to their implications for artificial intelligence, knowledge representation and interdisciplinary communication.
Current investigations also examine provenance, epistemic authority and reasoning trajectories, particularly in systems where information is transformed across multiple human and artificial agents. Here, provenance refers not merely to the identification of a source, but to the traceable history of information across successive agents, representations and transformations.
These investigations are intended to generate concepts, methods and experimental frameworks that can subsequently be evaluated through specific applications and interdisciplinary collaborations.
Research & Applications
Although BSI’s research is theoretically grounded, it is oriented towards problems encountered in real informational environments. Potential fields of application include artificial intelligence, healthcare, law and other domains in which conceptual precision, terminological stability and interpretive reliability are critical.
Applications are therefore approached not as substitutes for theoretical research, but as environments in which semantic hypotheses and methods can be tested, refined and confronted with the complexity of actual practices.
Open Research
BSI is conceived as an evolving research environment. Its concepts and methods are intended to remain open to discussion, comparison and revision through dialogue with researchers, practitioners and institutions working across disciplines.
Discuss a research collaboration
BSI welcomes dialogue with researchers, practitioners and institutions interested in semantic engineering, artificial intelligence, knowledge representation and interdisciplinary research.