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At the intersection of digital humanities and artificial intelligence, knowledge graphs and multilingual corpora are becoming fundamental pillars for large language models (LLMs) to operate with rigor in disciplines such as sociology, history, or computational linguistics. The scientific community has been warning for years that generative systems trained mostly in English and with predominantly technical or journalistic data carry biases and gaps when faced with multilingual academic sources, specialized terminology, or epistemic frameworks specific to the social sciences and humanities (SSH). Faced with this challenge, building semantic infrastructures that organize knowledge in a structured way —and that also integrate documents in multiple languages— is emerging as a necessary path to adapt LLMs to real research contexts.
Knowledge graphs act as a conceptual map that relates entities, authors, concepts, and works through arcs of explicit meaning. When an LLM queries a well-designed graph, it not only retrieves text fragments but can also navigate causal relationships, thematic hierarchies, and disciplinary dependencies. This drastically reduces hallucinations and improves the traceability of responses, two of the most critical issues highlighted in evaluation protocols such as the one proposed by the LLMs4EU project. Furthermore, the incorporation of multilingual corpora —from conference proceedings in German to journal articles in Spanish or Italian— allows the model not to rely exclusively on automatic translations, but to learn directly from the expressive and terminological richness original to each field.
For this integration to be viable in professional environments, organizations need custom applications that connect bibliographic databases, document repositories, and semantic reasoning engines. Custom software allows designing specific interfaces for researchers, with source validation layers, ontology version control, and alert systems for potential inconsistencies between what the LLM generates and what the knowledge graph states. In this sense, artificial intelligence applied to academic research demands solutions that are not only powerful but also auditable and aligned with legal frameworks such as the European Artificial Intelligence Act.
From a technical perspective, orchestrating these systems requires AWS and Azure cloud services to deploy foundational models with sufficient computing power and distributed storage. Hybrid cloud allows scaling the processing of multilingual corpora without compromising the privacy of sensitive data often handled by digital humanities projects (such as personal correspondence or historical archives under access restrictions). Similarly, cybersecurity becomes an unavoidable requirement when these environments handle queries from researchers that could expose unpublished research lines or human subject data. Implementing penetration testing and role-based access controls is as important as the quality of the models themselves.
To monitor the performance of these infrastructures, research teams increasingly turn to business intelligence services and tools like Power BI to visualize metrics on retrieval, semantic precision, and density of detected hallucinations in real time. These dashboards not only facilitate decision-making about which corpus or subgraph needs refinement but also allow project managers to demonstrate the effectiveness of their adaptations to funding agencies or ethics committees.
On the horizon, AI agents specialized in synthesizing academic literature will be able to operate autonomously, traversing multilingual knowledge graphs to produce systematic reviews, compare findings from different research traditions, or answer complex questions that require crossing disciplines. For these agents to be reliable, they must be trained on curated corpora and governed by ontologies that respect the linguistic diversity and epistemic plurality of the social sciences and humanities. Collaboration between infrastructures like ALT-EDIC and technology companies offering custom applications for the academic sector emerges as the most promising path to realize this vision, always within a regulatory compliance framework that guarantees transparency and epistemic responsibility.
