Mining Frequent Structures in Conceptual Models

• URL: http://arxiv.org/abs/2406.07129v3
• Date: Wed, 25 Dec 2024 08:59:25 GMT
• Title: Mining Frequent Structures in Conceptual Models
• Authors: Mattia Fumagalli, Tiago Prince Sales, Pedro Paulo F. Barcelos, Giovanni Micale, Philipp-Lorenz Glaser, Dominik Bork, Vadim Zaytsev, Diego Calvanese, Giancarlo Guizzardi,
• Abstract summary: We propose a general approach to the problem of discovering frequent structures in conceptual models. We implement our approach by focusing on two widely-used conceptual modeling languages. This tool can be used to identify both effective and ineffective modeling practices.
• Score: 2.625701175074974
• License:
• Abstract: The problem of using structured methods to represent knowledge is well-known in conceptual modeling and has been studied for many years. It has been proven that adopting modeling patterns represents an effective structural method. Patterns are, indeed, generalizable recurrent structures that can be exploited as solutions to design problems. They aid in understanding and improving the process of creating models. The undeniable value of using patterns in conceptual modeling was demonstrated in several experimental studies. However, discovering patterns in conceptual models is widely recognized as a highly complex task and a systematic solution to pattern identification is currently lacking. In this paper, we propose a general approach to the problem of discovering frequent structures, as they occur in conceptual modeling languages. As proof of concept, we implement our approach by focusing on two widely-used conceptual modeling languages. This implementation includes an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. The tool processes multiple conceptual models and identifies recurrent structures based on various criteria. We validate the tool using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective of our approach is to provide a support tool for language engineers. This tool can be used to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. Furthermore, it facilitates the reuse of accumulated expertise, ultimately supporting the creation of higher-quality models in a given language.

Related papers

• A Concept-Centric Approach to Multi-Modality Learning [3.828996378105142]
  We introduce a new multi-modality learning framework to create a more efficient AI system. Our framework achieves on par with benchmark models while demonstrating more efficient learning curves.
  arXiv  Detail & Related papers  (2024-12-18T13:40:21Z)
• Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models [80.32412260877628]
  We study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we show that concepts can be provably recovered from diverse data.
  arXiv  Detail & Related papers  (2024-02-14T15:23:59Z)
• Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach [25.927323251675386]
  We leverage the meta-features associated with each entity as a source of worldly knowledge and employ entity representations from the models. We propose using the consistency between these representations and the meta-features as a metric for evaluating pre-trained models. Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and image models.
  arXiv  Detail & Related papers  (2024-01-02T17:08:26Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• A Meta-Learning Approach to Population-Based Modelling of Structures [0.0]
  A major problem of machine-learning approaches in structural dynamics is the frequent lack of structural data. Inspired by the recently-emerging field of population-based structural health monitoring, this work attempts to create models that are able to transfer knowledge within populations of structures. The models trained using meta-learning approaches, are able to outperform conventional machine learning methods regarding inference about structures of the population.
  arXiv  Detail & Related papers  (2023-02-15T23:01:59Z)
• Language Model Cascades [72.18809575261498]
  Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities. Cases with control flow and dynamic structure require techniques from probabilistic programming. We formalize several existing techniques from this perspective, including scratchpads / chain of thought, verifiers, STaR, selection-inference, and tool use.
  arXiv  Detail & Related papers  (2022-07-21T07:35:18Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Artefact Retrieval: Overview of NLP Models with Knowledge Base Access [18.098224374478598]
  This paper systematically describes the typology of artefacts (items retrieved from a knowledge base), retrieval mechanisms and the way these artefacts are fused into the model. Most of the focus is given to language models, though we also show how question answering, fact-checking and dialogue models fit into this system as well.
  arXiv  Detail & Related papers  (2022-01-24T13:15:33Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts [39.99087114075884]
  We propose a novel two-view KG embedding model, JOIE, with the goal to produce better knowledge embedding. JOIE employs cross-view and intra-view modeling that learn on multiple facets of the knowledge base. Our model is trained on large-scale knowledge bases that consist of massive instances and their corresponding ontological concepts connected via a (small) set of cross-view links.
  arXiv  Detail & Related papers  (2021-03-15T03:24:37Z)

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.