Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture
- URL: http://arxiv.org/abs/2509.09775v2
- Date: Tue, 16 Sep 2025 09:05:52 GMT
- Title: Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture
- Authors: Aleksandr Boldachev,
- Abstract summary: We demonstrate that integrating semantic event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management systems.<n>The boldsea-engine's architecture interprets semantic models as executable algorithms without compilation.<n>It enables the modification of event models at runtime ensures transparency, and seamlessly merges data and business logic within a unified semantic framework.
- Score: 51.56484100374058
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper presents boldsea, Boldachev's semantic-event approach -- an architecture for modeling complex dynamic systems using executable ontologies -- semantic models that act as dynamic structures, directly controlling process execution. We demonstrate that integrating event semantics with a dataflow architecture addresses the limitations of traditional Business Process Management (BPM) systems and object-oriented semantic technologies. The paper presents the formal BSL (boldsea Semantic Language), including its BNF grammar, and outlines the boldsea-engine's architecture, which directly interprets semantic models as executable algorithms without compilation. It enables the modification of event models at runtime, ensures temporal transparency, and seamlessly merges data and business logic within a unified semantic framework.
Related papers
- The Auton Agentic AI Framework [5.410458076724158]
The field of Artificial Intelligence is undergoing a transition from Generative AI to Agentic AI.<n>This transition exposes a fundamental architectural mismatch: Large Language Models (LLMs) produce unstructured outputs, whereas the backend infrastructure they must control requires deterministic, schema-conformant inputs.<n>The present paper describes the Auton Agentic AI Framework, a principled architecture for the creation, creation, and governance of autonomous agent.
arXiv Detail & Related papers (2026-02-27T06:42:08Z) - Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence [13.062618208633483]
This paper proposes AgentOS, a holistic conceptual framework that redefines the Large Language Models as a "Reasoning Kernel" governed by structured operating system logic.<n>By mapping classical OS abstractions such as memory paging interrupt handling and process scheduling onto LLM native constructs, this review provides a rigorous roadmap for architecting resilient scalable and self-evolving cognitive environments.
arXiv Detail & Related papers (2026-02-24T14:12:21Z) - Ontology-to-tools compilation for executable semantic constraint enforcement in LLM agents [0.0]
We present a proof-of-principle mechanism for coupling large language models (LLMs) with formal domain knowledge semantics.<n>Ontological specifications are compiled into executable tool tools that LLM-based agents must use to create and modify knowledge graph instances.<n>We show how executable ontological semantics guide LLM interfaces and reduce manual schema and prompt engineering, establishing a general paradigm for embedding formal knowledge into generative systems.
arXiv Detail & Related papers (2026-02-03T12:03:26Z) - Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling [51.56484100374058]
We argue that Executable Ontologies (EO) represent a transition from algorithmic behavior programming to semantic world modeling.<n>We show how EO achieves prioritybased task interruption through dataflow conditions rather than explicit preemption logic.
arXiv Detail & Related papers (2026-01-12T19:57:35Z) - Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms [51.56484100374058]
We compare two approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO)<n>BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules.
arXiv Detail & Related papers (2025-11-19T09:38:01Z) - Affordance Representation and Recognition for Autonomous Agents [64.39018305018904]
This paper introduces a pattern language for world modeling from structured data.<n>The DOM Transduction Pattern addresses the challenge of web page complexity.<n>The Hypermedia Affordances Recognition Pattern enables the agent to dynamically enrich its world model.
arXiv Detail & Related papers (2025-10-28T14:27:28Z) - Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
arXiv Detail & Related papers (2025-10-09T05:28:28Z) - Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text [75.77648333476776]
This paper introduces an automated pipeline for extracting BPMN models from text.<n>A key contribution of this work is the introduction of a newly annotated dataset.<n>We augment the dataset with 15 newly annotated documents containing 32 parallel gateways for model training.
arXiv Detail & Related papers (2025-07-11T07:25:55Z) - Contextually Guided Transformers via Low-Rank Adaptation [14.702057924366345]
Large Language Models (LLMs) based on Transformers excel at text processing, but their reliance on prompts for specialized behavior introduces computational overhead.<n>We propose a modification to a Transformer architecture that eliminates the need for explicit prompts by learning to encode context into the model's weights.
arXiv Detail & Related papers (2025-06-06T01:34:39Z) - Object-Spatial Programming [2.8374498376407877]
We introduce Object-Spatial Programming (OSP), a programming model that extends Object-Oriented Programming.<n>OSP fundamentally inverts the traditional relationship between data and computation, enabling computation to move to data through four specialized archetypes.<n>This semantic enhancement enables runtime systems to make informed decisions about data locality, parallel execution, and distribution strategies.
arXiv Detail & Related papers (2025-03-20T02:55:40Z) - Semantic Communication and Control Co-Design for Multi-Objective Correlated Dynamics [33.18378000044136]
This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems.
By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space.
Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules.
arXiv Detail & Related papers (2024-10-03T08:38:54Z) - From Dialogue to Diagram: Task and Relationship Extraction from Natural
Language for Accelerated Business Process Prototyping [0.0]
This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER)
We utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding.
The system adeptly handles data transformation and visualization, converting verbose extracted information into BPMN (Business Process Model and Notation) diagrams.
arXiv Detail & Related papers (2023-12-16T12:35:28Z) - Towards More Unified In-context Visual Understanding [74.55332581979292]
We present a new ICL framework for visual understanding with multi-modal output enabled.
First, we quantize and embed both text and visual prompt into a unified representational space.
Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
arXiv Detail & Related papers (2023-12-05T06:02:21Z) - Guiding the PLMs with Semantic Anchors as Intermediate Supervision:
Towards Interpretable Semantic Parsing [57.11806632758607]
We propose to incorporate the current pretrained language models with a hierarchical decoder network.
By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks.
We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines.
arXiv Detail & Related papers (2022-10-04T07:27:29Z) - The Whole Truth and Nothing But the Truth: Faithful and Controllable
Dialogue Response Generation with Dataflow Transduction and Constrained
Decoding [65.34601470417967]
We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation.
Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
arXiv Detail & Related papers (2022-09-16T09:00:49Z) - Enterprise Architecture Model Transformation Engine [0.0]
This paper presents a transformation engine to convert enterprise architecture models between several languages.
The transformation process is defined by various pattern matching techniques using a rule-based description language.
It uses set theory and first-order logic for an intuitive description as a basis.
arXiv Detail & Related papers (2021-08-15T11:10:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.