Logic Augmented Generation
Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Larg...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Web Semantics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1570826824000453 |
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| author | Aldo Gangemi Andrea Giovanni Nuzzolese |
| author_facet | Aldo Gangemi Andrea Giovanni Nuzzolese |
| author_sort | Aldo Gangemi |
| collection | DOAJ |
| description | Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) may overcome those limitations, making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are hardly interpretable and often unreliable. To take the best out of LLMs and SKGs, we envision Logic Augmented Generation (LAG) to combine the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. LAG uses SKGs to inject a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results. |
| format | Article |
| id | doaj-art-5ca1d0bf93a243f9b8d900358ec49886 |
| institution | DOAJ |
| issn | 1570-8268 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Web Semantics |
| spelling | doaj-art-5ca1d0bf93a243f9b8d900358ec498862025-08-20T02:52:23ZengElsevierWeb Semantics1570-82682025-05-018510085910.1016/j.websem.2024.100859Logic Augmented GenerationAldo Gangemi0Andrea Giovanni Nuzzolese1University of Bologna, Bologna, Italy; CNR - Institute of Cognitive Sciences and Technologies, Bologna, ItalyCNR - Institute of Cognitive Sciences and Technologies, Bologna, Italy; Corresponding author.Semantic Knowledge Graphs (SKG) face challenges with scalability, flexibility, contextual understanding, and handling unstructured or ambiguous information. However, they offer formal and structured knowledge enabling highly interpretable and reliable results by means of reasoning and querying. Large Language Models (LLMs) may overcome those limitations, making them suitable in open-ended tasks and unstructured environments. Nevertheless, LLMs are hardly interpretable and often unreliable. To take the best out of LLMs and SKGs, we envision Logic Augmented Generation (LAG) to combine the benefits of the two worlds. LAG uses LLMs as Reactive Continuous Knowledge Graphs that can generate potentially infinite relations and tacit knowledge on-demand. LAG uses SKGs to inject a discrete heuristic dimension with clear logical and factual boundaries. We exemplify LAG in two tasks of collective intelligence, i.e., medical diagnostics and climate projections. Understanding the properties and limitations of LAG, which are still mostly unknown, is of utmost importance for enabling a variety of tasks involving tacit knowledge in order to provide interpretable and effective results.http://www.sciencedirect.com/science/article/pii/S1570826824000453Knowledge graphsLarge language modelsLogic augmented generation |
| spellingShingle | Aldo Gangemi Andrea Giovanni Nuzzolese Logic Augmented Generation Web Semantics Knowledge graphs Large language models Logic augmented generation |
| title | Logic Augmented Generation |
| title_full | Logic Augmented Generation |
| title_fullStr | Logic Augmented Generation |
| title_full_unstemmed | Logic Augmented Generation |
| title_short | Logic Augmented Generation |
| title_sort | logic augmented generation |
| topic | Knowledge graphs Large language models Logic augmented generation |
| url | http://www.sciencedirect.com/science/article/pii/S1570826824000453 |
| work_keys_str_mv | AT aldogangemi logicaugmentedgeneration AT andreagiovanninuzzolese logicaugmentedgeneration |