Michele Stingo

Hi there!

This is a digital space where I share my work, research, and ongoing exploration in the fields of Natural Language Processing, Large Language Models, and intelligent agent-based systems.

My interests revolve around cognitive agents, multi-agent architectures, reasoning systems, and the integration of structured knowledge within AI environments. I explore how language models can move beyond text generation to become components of systems capable of memory management, coordination, planning, and adaptive decision-making.

A significant part of my work focuses on knowledge representation, graph-based models, ontologies, and the interaction between symbolic reasoning and modern LLM-based architectures. More broadly, I am interested in designing intelligent systems that are reliable, interpretable, and operationally autonomous.

Here you will find research papers, technical articles, experimental projects, notes, and professional experiences that document the ideas and technologies shaping my work. The website also serves as a personal archive of ongoing experiments, reflections, and developments related to AI, computational linguistics, and cognitive systems engineering.

news

Sep 05, 2026	The site is now bilingual: every page has an Italian version, written rather than translated.
Aug 19, 2026	etimo v1 is out — it traces a word’s recorded ancestry through Wiktionary, one ancestor at a time.
Aug 04, 2026	Started Lost&Found, a place for notes, dead ends and things that lost their context. Here’s a misplaced manifesto to say what for.

latest posts

Oct 06, 2026	Festina lente
Aug 19, 2026	Invocation
Aug 04, 2026	A misplaced manifesto

selected publications

LLM-TEXT2KG
xch-MIND: Multi-Agent Interpretive Knowledge Graph Augmentation over Cultural Heritage Linked Data

Michele Stingo

In Joint Proceedings of the Fifth International Workshop on LLM-Integrated Knowledge Graph Generation From Text (TEXT2KG) and the Third International BiKE Challenge co-located with the Extended Semantic Web Conference (ESWC 2026), Dubrovnik, Croatia, 2026

CEUR Workshop Proceedings, Vol. 4233, pp. 65–83

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Institutional Linked Data repositories for cultural heritage, such as the Italian ArCo knowledge base, provide rich factual descriptions but lack interpretive layers connecting entities through spatial, temporal, and typological relationships. We present xch-MIND (eXtended Cultural Heritage — Multi-agent Interpretive Nexus Discovery), a multi-agent system that combines LLM-powered interpretive assertion generation with hybrid confidence scoring and non-invasive ontology layering over existing Linked Data. Four specialised agents analyse entities from ArCo, proposing clusters and relations that are validated through a hybrid score combining LLM self-assessment with deterministic domain-specific checks. All assertions are serialised as RDF triples in a purpose-built xch: ontology aligned with PROV-O, with an incremental multi-run strategy for progressive knowledge accumulation. Evaluated on 53 dolmen entities over six runs, xch-MIND discovers 175 unique interpretive assertions at 0.92 average confidence, serialised as 24,788 RDF triples with zero SHACL validation errors. Designed as a decision-support tool, the system provides full provenance and a tiered validation workflow that preserves expert authority over knowledge commitments.
@inproceedings{stingo2026xch, title = {xch-MIND: Multi-Agent Interpretive Knowledge Graph Augmentation over Cultural Heritage Linked Data}, author = {Stingo, Michele}, booktitle = {Joint Proceedings of the Fifth International Workshop on LLM-Integrated Knowledge Graph Generation From Text (TEXT2KG) and the Third International BiKE Challenge co-located with the Extended Semantic Web Conference (ESWC 2026)}, editor = {Tiwari, Sanju and Mihindukulasooriya, Nandana and D'Souza, Jennifer and Osborne, Francesco and Marx, Edgard}, series = {CEUR Workshop Proceedings}, volume = {4233}, pages = {65--83}, publisher = {CEUR-WS.org}, address = {Aachen, Germany}, issn = {1613-0073}, location = {Dubrovnik, Croatia}, urn = {urn:nbn:de:0074-4233-x}, note = {CEUR Workshop Proceedings, Vol. 4233, pp. 65–83}, year = {2026}, }