Searcharxiv⌕ Search

arXiv subjects

José Palazzo Moreira de Oliveira

Publications and source records attributed to José Palazzo Moreira de Oliveira.

2 recordsLinked to original sources

From Symbol to Meaning: Ontological and Philosophical Reflections on Large Language Models in Information Systems Engineering

The advent of Large Language Models (LLMs) represents a turning point in the theoretical foundations of Information Systems Engineering. Beyond their technical significance, LLMs challenge the ontological, epistemological, and semiotic assumptions that have long structured our understanding of in-formation, representation, and knowledge. This article proposes an integrative reflection on how LLMs reconfigure the relationships among language, meaning, and system design, suggesting that their emergence demands a re-examination of the conceptual foundations of contemporary information systems. Sketching on philosophical traditions from Peirce to Heidegger and Floridi, we investigate how the logic of generative models both extends and destabilises classical notions of ontology and signification. The discussion emphasises the necessity of grounding LLM-based systems in transparent, ethically coherent frameworks that respect the integrity of human-centred knowledge processes. Ultimately, the paper argues that LLMs should be understood not merely as tools for automation but as epistemic agents that reshape the philosophical and semiotic foundations of information systems engineering.

cs.SE↗

Modeling, comprehending and summarizing textual content by graphs

Automatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the summary will be used. Nonetheless, there are special cases where it is not found enough textual sources, and one possible alternative is to generate a summary from a different domain. One manner to summarize texts consists of using a graph model. This model allows giving more importance to words corresponding to the main concepts from the target domain found in the summarized text. This gives the reader an overview of the main text concepts as well as their relationships. However, this kind of summarization presents a significant number of repeated terms when compared to human-generated summaries. In this paper, we present an approach to produce graph-model extractive summaries of texts, meeting the target domain exigences and treating the terms repetition problem. To evaluate the proposition, we performed a series of experiments showing that the proposed approach statistically improves the performance of a model based on Graph Centrality, achieving better coverage, accuracy, and recall.

cs.CL↗