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Knowledge Representation Formalisms for AI Applications
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Knowledge Representation Formalisms for AI Applications

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2947

Special Issue Editors

Dr. Gianvincenzo Alfano

E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics and System Engineering (DIMES), University of Calabria, Rende, Italy
Interests: nonmonotonic reasoning; argumentation theory and its applications in artificial intelligence
Dr. Alejandro Javier García

E-Mail Website
Guest Editor
Department of Computer Science & Engineering, Institute for Computer Science and Engineering, Universidad Nacional del Sur, Bahía Blanca, Argentina
Interests: argumentation; knowledge representation; automatic reasoning; logic programming; belief dynamics; multi-agent systems
Dr. Francesco Parisi

E-Mail Website
Guest Editor
Department of Informatics, Modeling, Electronics and System Engineering (DIMES), University of Calabria, 87036 Rende, Italy
Interests: artificial intelligence and databases; particularly in the areas of inconsistency management; knowledge representation; reasoning under uncertainty

Special Issue Information

Dear Colleagues,

In the complex challenge of designing intelligent systems in the Big Data era, an adequate representation of knowledge, sometimes considering uncertainty and incompleteness, and an easy-to-understand approach to automated reasoning are required. These are notable aspects of formal representation systems, suitable for making decisions through software agents trained in solving real problems of different natures such as explainability and interpretability of results, hybrid KR&R-Machine Learning, query answering, cybersecurity, the semantic web, and multi-agent systems. The growing demand for the explainability of AI systems operating in the aforementioned domains is also confirmed by the increasing demand that humans can clearly understand the decisions provided by these systems.

The overall aim of this Special Issue is to collect state-of-the-art research findings on the latest developments, up-to-date issues, and challenges in the field of knowledge representation formalisms in support of AI domains. Proposed submissions should make significant methodological or application contributions. This Special Issue should be of interest to the AI community.

Dr. Gianvincenzo Alfano
Dr. Alejandro Javier García
Dr. Francesco Parisi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • knowledge representation formalisms
  • formal argumentation
  • machine learning
  • interpretability of results
  • query answering
  • cybersecurity
  • semantic web
  • multi-agent systems

Published Papers (1 paper)

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Research

22 pages, 5909 KiB  
Article
Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models
by Jesus Insuasti, Felipe Roa and Carlos Mario Zapata-Jaramillo
Big Data Cogn. Comput. 2023, 7(4), 182; https://doi.org/10.3390/bdcc7040182 - 14 Dec 2023
Viewed by 2443
Abstract
Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic [...] Read more.
Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic pre-conceptual schemas made by humans. To do that, the construction of a linguistic corpus is required to work with large language models—LLM. The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. As a result, the first version of a computational solution was built to consume the language models based on BERT and Llama 2-Chat and thus automatically interpret pre-conceptual schemas by computers via natural language processing, adding, at the same time, generative capabilities. The validation of the computational solution was performed in two phases: the first one for detecting sentences and interacting with pre-conceptual schemas with students in the Formal Languages and Automata Theory course—the seventh semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. The second phase was for exploring the generative capabilities based on pre-conceptual schemas; this second phase was performed with students in the Object-oriented Design course—the second semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. This validation yielded favorable results in implementing natural language processing using the BERT and Llama 2-Chat models. In this way, some bases were laid for future developments related to this research topic. Full article
(This article belongs to the Special Issue Knowledge Representation Formalisms for AI Applications)
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