<figure><img src="image_1.png" /></figure>

HAL
open science

# Semantic History of Medieval Music: A Wikidata Framework for Computational Historiography

Xavier Fresquet

<figure><img src="image_2.png" /></figure>

## To cite this version:

Xavier Fresquet. Semantic History of Medieval Music: A Wikidata Framework for Computational Historiography. Sorbonne universite. 2025. hal-05361112

HAL Id: hal-05361112

https://hal.sorbonne-universite.fr/hal-05361112v1

Submitted on 12 Nov 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Semantic History of Medieval Music:
A Wikidata Framework for Computational Historiography

Xavier Fresquet,
Institut de Recherche en Musicologie,
Sorbonne Center for Artificial Intelligence,
Sorbonne Université

**Abstract**

This paper introduces the Semantic History of Medieval Music project, a prototype framework for constructing a global, data-driven historiography of medieval music (500–1500 CE) grounded in open, multilingual, and verifiable data. Addressing the fragmentation and interpretive uncertainty of medieval sources—ranging from notated chants to iconographic and theoretical materials—the current study focuses on Wikidata as a primary corpus for computational historiography, demonstrating how its multilingual graph structure can be repurposed for historical reasoning in musicology.

The system combines Linked Open Data (LOD), multilingual embeddings, and Retrieval-Augmented Generation (RAG) to find cross-cultural continuities between musical concepts, manuscripts, instruments, and modal systems. Implemented as a transparent, notebook-based pipeline, it bridges symbolic, textual, and semantic representations, enabling users to query and visualize conceptual relations—such as organum, maqām, and rāga—through vector similarity and graph reasoning.

While this first iteration centers on Wikidata, the broader framework is designed for interoperability with other major musicological infrastructures, including RISM, the Cantus Database, and Musiconis. The results demonstrate how open ontologies and explainable AI can sustain a computational historiography that is transparent, reproducible, and participatory. The long-term outcome, the Semantic Atlas of Medieval Music, aims to map the diffusion of musical ideas across Europe, the Middle East, and Asia, modeling a scalable paradigm for AI-assisted cultural analytics in which interpretability and provenance replace black-box narration.

**Keywords:** Wikidata; Linked Open Data; GraphRAG; Medieval Music; Computational Historiography; Multilingual Embeddings; Explainable AI
Introduction

Traditional histories of medieval music have been largely Eurocentric, privileging the written canon of Western liturgical and courtly traditions while marginalizing other sound cultures of the same period (Everist 1999; Bohlman 1988). These narratives have often been organized around textual and notated sources, emphasizing compositional schools such as those of Notre-Dame, Aquitaine, or Ars Nova, and structured chronologically according to the development of notation and polyphony (Hoppin 1978). Such frameworks, while foundational for Western musicology, tend to obscure the diversity of medieval musical practices that extended beyond Latin Christendom—across the Mediterranean, the Byzantine Empire, the Islamic world, and sub-Saharan Africa—where oral transmission, improvisation, and local performance contexts played central roles (Hsieh 2025; Burnett 2023; Jeffery 2025; Shiloah 1995). Recent scholarship has called for a decentering of medieval sound studies, integrating perspectives from ethnomusicology, digital humanities, and postcolonial critique to recover plural histories of listening, performance, and transmission (Burnett 2023; Blanke et al. 2018).

Yet the medieval world was marked by multidirectional cultural transmission, particularly across the Mediterranean and Eurasian worlds. Exchanges occurred not only through trade routes and pilgrimage networks but also via the circulation of scholars, missionaries, and musical instruments (Farmer 1930; Shiloah 1995). While these systems emerged in distinct theological and performative contexts, they shared structural concerns with mode, affect, and melodic contour, suggesting a broader transregional discourse on the organization of sound and emotion. Recent comparative musicological and historical studies have emphasized that such parallels were not simple coincidences but part of a shared intellectual ecology, shaped by intercultural contact zones—al-Andalus, the Crusader states, and beyond—and by the migration of both ideas and people. This transmodal perspective reveals the medieval Mediterranean as an acoustic continuum, rather than a series of isolated traditions, reframing medieval music as a field of connective practices rather than an exclusively Western inheritance (Jeffery 2025; Strohm 2022).

This project investigates whether open, machine-readable knowledge bases such as *Wikidata*<fnref n="1" /> can serve as a foundation for tracing structural continuities in musical thought across distinct civilizations. It explores how embeddings and graph reasoning can assist in reconstructing historical relations that are otherwise fragmented across linguistic, cultural, and disciplinary boundaries—where traditional textual evidence may be incomplete or inaccessible. More broadly, it asks what a “computational historiography of sound” might look like when built upon transparent and verifiable data sources, reframing artificial intelligence not as a storyteller but as a methodological instrument for historical interpretation.

This paper therefore positions *Wikidata* as a testbed for exploring how semantic infrastructures can represent uncertainty, multiplicity, and cultural entanglement. By combining graph reasoning, multilingual embeddings, and explainable AI, the project investigates how computational methods might illuminate not only what we know about medieval music, but how that knowledge is organized, related, and interpreted across cultural boundaries. Within this background, the study articulates four central research questions: (1) How can semantic modeling reconcile fragmented and uncertain medieval musical sources? (2) To what extent can computational historiography model expert interpretation? (3) How can cross-cultural modalities be represented equitably in a semantic framework? (4) What ethical principles should guide AI-based reconstruction of intangible musical heritage? These four questions define the scope of a broader inquiry into what a *Semantic History of Medieval Music* might mean in the digital age: not a fixed narrative, but a dynamic, data-grounded cartography of connections—where humanistic interpretation and machine reasoning converge in the service of historical understanding.

<footnote n="1">¹Wikidata is a collaboratively curated, multilingual knowledge base maintained by the Wikimedia Foundation. It provides structured, linked data that can be accessed and queried through the SPARQL endpoint at https://query.wikidata.org and is widely used as an open, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) semantic backbone for research in the digital humanities, data science, and cultural heritage domains.</footnote>
# 1. Data and Methodology

## 1.1 Related Works

We extend the logic of interoperability pioneered by digital musicology infrastructures such as Polifonia<fnref n="2" />, DOREMUS<fnref n="3" />, MEI Linked Data<fnref n="4" />, and MusicBrainz<fnref n="5" />, which established the foundations for representing musical works, performances, and metadata through open ontologies and Linked Open Data standards<fnref n="6" />. Unlike these domain-specific systems—focused “primarily” on cataloging and metadata harmonization—this project integrates semantic retrieval and generative reasoning, using linked data for dynamic interpretive historical analysis (de Berardinis 2023). Indeed, these repositories embody the FAIR data principles—ensuring that musical sources are Findable, Accessible, Interoperable, and Reusable—and thus provide a sustainable foundation for computational musicology, where data transparency and interoperability are prerequisites for reproducibility and open scholarship (Wilkinson 2016).

The present framework extends them toward multilingual and cross-civilizational coverage, integrating entities from Western, Arabic, and Indic music traditions into a single semantic space. Recent advances in Retrieval-Augmented Generation (RAG<fnref n="7" />) and Graph-based RAG (GraphRAG<fnref n="8" />) have redefined how generative models access and reason with structured data. In the humanities, this approach supports traceable interpretive reasoning rather than opaque text generation, aligning with ongoing debates around Explainable AI (XAI) and epistemic transparency (El-Hajj 2023). The system applies these principles to historical enquiry, enabling humanistic scholars to follow the provenance of all generated statements through explicit Wikidata properties and graph paths—thus using RAG as a methodological instrument.

Finally, this work also builds on the tradition of computational musicology and digital philology, which use algorithmic tools to study textual and musical corpora. From the early work of the Josquin Research Project (Rodin 2014) to recent projects in semantic score analysis and digital chant philology (Thomae 2024), scholars have sought to combine close reading with large-scale pattern discovery (Pugin 2020). What differentiates our approach is its integration of symbolic music data with ontological and textual embeddings, uniting the documentary, analytical, and generative dimensions of musicology within one reproducible, interpretable framework.

<footnote n="2">²The Polifonia Project (Horizon 2020) connects Europe's musical heritage through Linked Open Data, building a multilingual knowledge graph that links works, people, and places from the Renaissance to today. Combining semantic web, AI, and music information retrieval, it provides open tools and a public portal for research and heritage preservation. https://polifonia-project.eu/</footnote>

<footnote n="3">³The DOREMUS Project (Doing Reusable Musical Data), launched in 2014, enhances the description and interconnection of musical data using semantic web technologies. Built on an extension of FRBRoo for music, DOREMUS develops multilingual controlled vocabularies to describe, publish, and link catalogues from major French cultural institutions such as the Bibliothèque nationale de France, Radio France, and the Philharmonie de Paris. https://www.doremus.org/</footnote>

<footnote n="4">⁴The Music Encoding Initiative (MEI) is an open, community-driven XML framework for encoding musical documents in a machine-readable format. It applies semantic web and Linked Data principles to MEI corpora, enhancing interoperability and semantic annotation. The Music Encoding and Linked Data framework extends MEI by integrating RDF and the Web Annotation Data Model, allowing scholars to semantically link score sections to external musical and bibliographic resources.</footnote>

<footnote n="5">⁵MusicBrainz is an open, user-curated encyclopedia and database of musical metadata. It catalogs artists, releases, recordings, works, and labels, along with their interrelations, all available under CC0 open licenses, effectively placing the data in the public domain for unrestricted reuse and redistribution. https://musicbrainz.org/</footnote>

<footnote n="6">⁶Linked Open Data (LOD) refers to a set of standards and best practices for publishing and interlinking structured data on the web in an open and interoperable format. By using technologies such as RDF, SPARQL, and URIs, LOD enables data—such as music metadata—to be shared, connected, and queried across heterogeneous sources worldwide.
Sources: Berners-Lee, Tim. "Linked Data." W3C Design Issues, 2006; Heath, Tom, and Christian Bizer. Linked Data: Evolving the Web into a Global Data Space. San Rafael, CA: Morgan & Claypool, 2011.</footnote>

<footnote n="7">⁷Retrieval-Augmented Generation (RAG) is a hybrid method that combines large language models (LLMs) with information retrieval systems, enabling models to dynamically access and incorporate external data sources—such as documents, databases, or web repositories—during text generation. This approach enhances factual accuracy and transparency by grounding model outputs in verifiable evidence. See: Lewis, Patrick, et al. "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Advances in Neural Information Processing Systems 33 (2020): 9459–9474</footnote>

<footnote n="8">⁸GraphRAG (Graph-based Retrieval-Augmented Generation) is an extension of the RAG framework that integrates knowledge graphs into the retrieval and reasoning process. By leveraging structured relational data—such as entities, properties, and multi-hop connections—GraphRAG enhances contextual understanding beyond simple vector similarity. This approach allows large language models to reason over explicit semantic links, improving explainability and factual grounding in complex, interconnected domains. See: Wu, Yujian, et al. "GraphRAG: Enhancing Retrieval-Augmented Generation with Graph Reasoning." arXiv preprint arXiv:2311.08560 (2023).</footnote>
## 1.2 Data Sources

The system combines Wikidata as its foundational knowledge graph—providing structured entities such as *composers*, *instruments*, * genres*, *manuscripts*, and *theoretical concepts*—with the Wikidata Embedding Project (WDEP)<fnref n="9" />, a multilingual vector database that enables semantic representation and similarity retrieval across languages and ontological domains (Tang 2023).

Each entity is referenced through a stable QID (Wikidata identifier<fnref n="10" />), ensuring both interpretability and traceability throughout the pipeline. This hybrid architecture allows the system to bridge symbolic and distributed representations: Wikidata provides explicit, human-readable knowledge relations (e.g., “is composer of,” “has genre,” “described in treatise”), while the embedding layer encodes latent semantic affinities across textual corpora and multilingual descriptions (Vrandečić 2014; Erxleben 2014). By aligning structured cultural-heritage metadata with continuous vector space models, the framework facilitates cross-lingual linking of medieval musical entities—such as connecting *Guido d’Arezzo* (Q123174) with modal theory concepts or manuscript sources—while preserving the epistemic transparency required for humanistic interpretation.

## 1.2 Pipeline Overview

The system is implemented as a modular Google Colab notebook<fnref n="11" />, allowing transparent, reproducible experimentation and extensibility. The workflow proceeds through six interconnected stages, combining knowledge graph querying, multilingual text enrichment, embedding-based retrieval, and hybrid visualization. The system employs the paraphrase-multilingual-mpnet-base-v2 model from SentenceTransformers, supporting over 50 languages (including English, French, Latin, Arabic, German, and Italian) to encode semantic relationships across multilingual musical descriptors. Embeddings are L2-normalized and indexed using FAISS (Facebook AI Similarity Search) with cosine similarity for high-dimensional vector retrieval. The SPARQL endpoint queries live data from Wikidata Query Service, filtering entities by inception dates (500–1500 CE) and music-related classes. This hybrid configuration enables integration of dense semantic search with explicit relational reasoning, ensuring that retrieved results are both conceptually aligned and ontologically grounded across multiple linguistic and cultural contexts.

<footnote n="9">⁹Launched by Wikimedia Deutschland in 2025, the Wikidata Embedding Project (WDEP) converts Wikidata's 119 million+ structured items into a multilingual vector database optimized for generative AI. It enables semantic search and Retrieval-Augmented Generation (RAG) grounded in verifiable, multilingual data.</footnote>

<footnote>See: https://www.wikidata.org/wiki/Wikidata:Embedding Project[66]</footnote>

<footnote n="10">¹⁰A QID (Wikidata Identifier) is a unique, stable identifier assigned to each Wikidata entity, formatted as the letter “Q” followed by digits (e.g., Q42 for Douglas Adams). QIDs remain constant even if labels or descriptions change, allowing for precise cross-referencing across languages and datasets. They serve as global identifiers for people, places, works, or concepts, ensuring unambiguous linkage in both human and machine-readable contexts.</footnote>

<footnote>See: https://www.wikidata.org/wiki/Wikidata:Identifiers</footnote>

<footnote n="11">¹¹Fresquet, Xavier. Semantic History of Medieval Music – Notebook. Google Colab. Accessed November 9, 2025. https://colab.research.google.com/drive/1H8jOLOZXjLGAAACvBYeZaT3mtJwigod4?usp=sharing[67].</footnote>

<footnote>Portions of the Python code in this notebook were corrected and enhanced with the assistance of a generative AI system (Gemini), used for debugging, optimization, and documentation purposes. All outputs were verified and adapted by the author.</footnote>
<table><caption>Table 1. Six-Stage Pipeline of the Semantic History of Medieval Music Framework</caption><thead><tr><td>Stage</td><td>Description</td><td>Output</td></tr></thead><tbody><tr><td>1. Harvest &amp; Normalize</td><td>SPARQL extraction of medieval-music entities (composers, instruments, manuscripts, genres; 500–1500 CE).</td><td>Canonical dataset (~650 entities).</td></tr><tr><td>2. Multilingual Enrichment</td><td>Retrieval of rdfs:label, skos:altLabel, schema:description in six+ languages (EN, FR, LA, AR, DE, IT).</td><td>Unified multilingual corpus.</td></tr><tr><td>3. Embedding &amp; Indexing</td><td>Encoding via paraphrase-multilingual-mpnet-base-v2; FAISS indexing (L2-normalized cosine similarity).</td><td>Multilingual semantic space.</td></tr><tr><td>4. Graph Integration</td><td>Extraction of core Wikidata relations (instance of, subclass of, genre, creator, location).</td><td>Hybrid vector-graph KB.</td></tr><tr><td>5. Semantic Retrieval (RAG)</td><td>Links embedding search to Wikidata graph for transparent, provenance-rich reasoning.</td><td>Source-linked responses.</td></tr><tr><td>6. Visualization</td><td>PyVis network and Folium maps for interactive exploration.</td><td>Semantic atlas &amp; visual network.</td></tr></tbody></table>

This modular structure supports interoperability with broader digital-musicology initiatives such as Polifonia, DOREMUS, or Musiconis, while remaining lightweight and executable in a cloud-based environment. Each stage can be independently modified or replaced, ensuring long-term adaptability for new ontologies, embedding models, or data sources<fnref n="12" />.

Building on these stages, the system enables cross-modal RAG, in which both *textual embeddings* and *graph-based relations* contribute to interpretive reasoning. When a user issues a natural-language query—such as “*Which manuscripts document the transmission of modal theory from Aquitaine to Italy?*”—the embedding index identifies semantically aligned entities across languages, while the graph layer traces explicit ontological links among composers, theoretical treatises, and geographic locations. This dual retrieval mechanism allows the system to synthesize latent semantic affinities (captured by multilingual embeddings) with explicit epistemic relations (encoded in Wikidata).

In this configuration, the vector space acts as an amplifier—surfacing conceptually related entities that might not be directly connected in the knowledge graph—while the SPARQL-derived structure guarantees interpretability, provenance, and verifiability of the results. The RAG layer thus serves as a *mediating interface* between machine learning and historical musicology, enabling users to move

<footnote n="12">¹² Achichi, Manel, et al. “DOREMUS: Doing Reusable Musical Data.” Journal of Web Semantics 54 (2018): 27–41. https://doi.org/10.1016/j.websem.2018.01.001; Polifonia Consortium. Polifonia: A Digital Ecosystem for European Musical Heritage. Horizon 2020 Project, 2020–2024. https://polifonia-project.eu; Billiet, Frédéric, Edmundo Camacho, and Xavier Fresquet, eds. Musiconis: Iconographie Musicale du Moyen Âge. Paris: Sorbonne Université, 2019. http://musiconis.huma-num.fr/</footnote>
fluidly from data-driven exploration to humanistic interpretation.

This comparative framing invites us to move beyond the Eurocentric narrative of modes as discrete Western categories and instead view systems such as the rāga, maqām and the medieval ecclesiastical mode as culturally embedded epistemic systems—structures of meaning, performance, and affect grounded in distinct intellectual, spiritual and social milieus (Al-Faruqi 1985). By critically attending to these systems as equivalent rather than as peripheral analogues, the project aligns with decolonial and comparative music-theory efforts that reject the imposition of Western hierarchies in favour of plural, co-equal analytic frameworks.

## 2. Computational Workflow and Wikidata Integration

The system follows open science principles: every step is executed within a reproducible Colab environment using publicly accessible APIs and version-controlled outputs. The workflow builds on methodological advances in digital musicology and symbolic analysis (Urberg 2017; Smith 2011), extending these paradigms to semantic and multilingual data environments. Entity and relation data were retrieved using SPARQL queries executed through the *SPARQLWrapper* library. Queries targeted entities related to medieval musical concepts, manuscripts, instruments, and theoretical systems (e.g., *mode*, *maqām*, *rāga*, *organum*). For each retrieved QID, the system harvested: canonical relations (instance-of, subclass-of, part-of); multilingual labels and descriptions (EN, FR, LA, AR, DE, IT); temporal and geographic qualifiers (e.g., P580, P582, P17). All results were serialized into Parquet and JSON formats for further processing.

Each entity label and description was embedded using multilingual sentence transformers (sBERT, all-MiniLM-L12-v2) to generate 768-dimensional vectors normalized to unit length. The vectors were stored in a FAISS index for efficient semantic similarity search, with an accompanying Python dictionary mapping QIDs to vector positions. This embedding layer enables semantic proximity queries that are language-independent, allowing equivalence retrieval between, for example, *organum*, *maqām*, and *rāga* across linguistic boundaries.

Graph reasoning was conducted through NetworkX and PyVis, allowing for neighborhood expansion, pathfinding, and visual inspection of semantic connections. A diffusion map algorithm was implemented to simulate the propagation of musical concepts across temporal and spatial dimensions, approximating cultural transmission. The system also supports text-based synthesis, where retrieved entities and relations are formatted into concise, human-readable conceptual summaries.

Example query:

semantic_query(

"Compare organum, maqām and rāga in medieval theory",

k=30, hops=1, show_map=True

Outputs include:

(1) JSON and CSV exports of entity data and relationships;

(2) GEXF files for network visualization;

(3) Textual synthesis files summarizing conceptual relationships (e.g., "Codex Chantilly → illuminated manuscript").

All code is executed within a Google Colab environment using open Python libraries (pandas, tqdm, wikibaseintegrator, SPARQLWrapper), in order to allow reproducibility and public accessibility. Data provenance is preserved through automatic logging of query timestamps, dataset versions, and embedding parameters.

Overall, the workflow can be summarized as follows:
1. Define configuration parameters (years, languages, limits)

2. Query Wikidata via SPARQL → collect QIDs, labels, relations

3. Clean and structure results into canonical dataset

4. Embed multilingual text fields using sBERT transformer

5. Build FAISS index and store mapping

6. Construct graph G(V,E) with NetworkX

7. Apply semantic query: retrieve top-k neighbors or paths

8. Generate visualization (PyVis) and textual synthesis

9. Export all data (CSV/JSON) for further analysis

This pipeline demonstrates how *Wikidata* can enable the semantic reconstruction of conceptual continuities.

## 3. Results: From Data to Semantic Historiography

The experimental phase consisted of twenty semantically varied queries, ranging from “*Which medieval composers (c. 500–1500) are recorded in Wikidata?*” to “*Which entities document the Arabic maqām or the Indian rāga?*” The results reveal both the remarkable potential and the structural limitations of open, multilingual knowledge graphs for historical reasoning in musicology. In several cases—such as queries on *rāga*, medieval composers, and notated manuscripts—the system retrieved coherent, ontologically grounded entities that bridge textual, theoretical, and instrumental domains of musical practice. Examples such as *Codex Chantilly* (Q2521364), *Adam de la Halle* (Q1340467), and *Rāga* (Q216926) demonstrate the ability of the hybrid RAG framework to connect distinct cultural and conceptual strata through shared relational logic. The combination of embeddings and graph reasoning allows latent affinities to emerge—such as the triadic relation between *mode*, *maqām*, and *rāga*—while maintaining verifiable provenance at every step.

The system also succeeded in retrieving a rich catalogue of musical genres and forms, ranging from *organum*, *conductus*, and *estampie* to *rondeau* and *lauda*, illustrating the depth of Western medieval metadata within Wikidata. Yet this same query surfaced anachronistic entries such as *neo-medieval music* and *klezmer fiddle*, which, while outside the target period, are valuable as diagnostic signals of semantic drift. Their appearance highlights how the framework not only aggregates information but exposes the porous boundaries and taxonomic inconsistencies within global musical knowledge graphs. Similarly, the dataset linking *birthplaces* and *places of activity* generated a series of plausible trajectories—from Constantinople to the Byzantine Empire, from Flanders to Venice, or from Damascus to monastic centers of learning—suggesting how the pipeline can trace geotemporal mobility across historical actors (Jeffery 2025). These diffusion vectors already outline the contours of what may become a *Semantic Atlas of Medieval Music*, in which the transmission of musical ideas can be visualized as interconnected pathways across the Mediterranean and Eurasian worlds.

At the same time, the experiment makes visible the incompleteness of the underlying cultural data infrastructures. Fifteen of the twenty queries returned partial or null results, reflecting gaps in modeling rather than algorithmic failure. Notably, searches targeting the *Notre-Dame school* or *monastic orders* produced no entities at all, revealing the absence of structured relations for stylistic schools, collective authorship, and institutional affiliation within Wikidata’s current ontology. This lacuna is particularly telling: it exemplifies how computational historiography can surface the contours of what remains unmapped, transforming data scarcity into a historiographic observation.

The same applies to liturgical manuscripts—*graduals*, *antiphonaries*, *missals*—whose limited representation and incomplete metadata underscore the need for enhanced integration of manuscript
ontologies specifying period, function, and provenance.

Other anomalies point to challenges of semantic precision. The iconography query, designed to retrieve musical instruments with visual documentation, was almost entirely populated by pipe-organ entries—objects frequently depicted but rarely medieval. This disproportion reflects not a computational bias but the overrepresentation of modern instruments in image-linked data, suggesting that future iterations should constrain retrieval by inception date and *depicts* (P180) qualifiers. Even more illustrative is a query on *maqām* that returned the *Celtic Music WikiProject*, a striking example of semantic drift produced by lexical similarity without ontological constraint. Such misalignments reinforce the necessity of graph-based filtering and multilingual label normalization to preserve conceptual coherence across traditions and languages. Conversely, the *rāga* query, while limited in scope, returned distinct entities corresponding to musical works and treatises, indicating that Indian theoretical systems are beginning to acquire a structured presence in the graph, albeit one largely disconnected from biographical and conceptual nodes.

Together, the retrieval outcomes expose clear geo-cultural asymmetries. Western repertoires—troubadours, trouvères, polyphonic manuscripts—are densely represented, while Islamic, Byzantine, and sub-Saharan traditions remain fragmentary or absent. The imbalance mirrors long-standing archival hierarchies and cataloguing priorities rather than any failure of the semantic engine itself. Indeed, the RAG system merely reflects the epistemic topography of Wikidata, which privileges well-documented Western sources. Yet this asymmetry is itself instructive: by quantifying representation gaps, the pipeline transforms cultural bias into measurable data and provides a roadmap for collaborative enrichment.

Despite limitations, the experiment demonstrates that a transparent, graph-linked RAG architecture can already serve as a semantic historiography engine. Each retrieved entity is verifiable through a stable QID, every relation is ontologically grounded, and every generated statement can be traced back to its provenance. This level of accountability contrasts with the black-box nature of most large language models. By turning open data into a reproducible reasoning environment, the system paves the way for participatory, verifiable, and multilingual historical analysis—where scholars can iteratively refine both data and interpretation.

In this paradigm, the act of historical knowing becomes a process of semantic reasoning: the algorithm does not claim to recover an “original” organum or maqām, but to map the conceptual proximities, dependencies, and interpretive alternatives that define their historiography. This shift reflects a broader epistemological move in digital humanities, where the goal is no longer to digitize the past, but to render visible the interpretive structures through which it becomes intelligible. Such a transition aligns with the emerging field of computational hermeneutics, which views AI as a tool for modeling reasoning rather than replacing it—formalizing human interpretive acts in ways that remain transparent, explainable, and verifiable.

# 4. Discussion

Despite its promise, the current framework faces methodological and technical constraints. The retrieval pipeline is subject to biases inherent in Wikidata, including uneven cultural representation and variable metadata completeness, which can influence the apparent centrality of entities in the knowledge graph. The multilingual embedding models occasionally introduce semantic noise or false equivalences across languages, particularly when dealing with low-resource historical terms. In addition, context-window limitations in large language models constrain the depth of reasoning in extended historical narratives, sometimes truncating interpretive chains or oversimplifying complex modal relationships. While the notebook design promotes transparency and reproducibility, it still relies on computational infrastructures and pretrained models that evolve rapidly, posing challenges for long-term version stability and result reproducibility (Bender et al. 2021; van Dis et al. 2023).

This experiment demonstrates how open knowledge graphs can underpin a new form of computational historiography as it operationalizes the “linked-data turn” in musicology, translating decades of conceptual work in music information retrieval and digital philology into a reproducible, machine-actionable workflow. By aligning humanistic entities—*composers, treatises, instruments,*
ancestries—within a connected semantic space, it allows to query relationships that were previously implicit or inaccessible. Second, it replaces opaque model hallucination with verifiable, provenance-rich reasoning. Every generated statement is anchored in explicit Wikidata relations, ensuring traceability and interpretability. In contrast to black-box AI systems, this approach foregrounds *explainability* and *epistemic transparency* as methodological virtues in digital humanities. Third, it enables multilingual and cross-cultural interoperability, linking entities and terminologies across *Latin*, *Arabic*, *Sanskrit*, and *vernacular* sources. This polyglot semantic alignment opens the possibility of rethinking music history as a network of interconnected epistemes, rather than parallel civilizational narratives (Chiarcos 2012). Ultimately, this approach reframes the knowledge graph as both a computational archive and an interpretive interface—a tool not merely for retrieval, but for reimagining historical connections across cultures, languages, and modalities of sound.

The experiment also raises a set of broader methodological and epistemological questions about how knowledge graphs and AI-based retrieval systems can represent, reason about, and reinterpret the past. How can medieval concepts such as *modus, rhythmus, or maqām* be faithfully modeled within Wikidata's ontology? Current schemas privilege modern taxonomies of genre and form, often flattening the historical complexity of modal thought or liturgical practice. Developing *domain-sensitive ontologies* for premodern epistemes thus becomes a key challenge for digital musicology (de Berardinis 2023). Can multilingual embeddings and graph structures represent historical change—for instance, the semantic evolution from *modal theory* to *tonal harmony*? Temporal embeddings offer promising paths, yet the question remains how to encode *diachronic drift* in conceptual meaning while preserving interpretive coherence across centuries.

How do we account for geographic and cultural asymmetries in Wikidata's coverage of non-Western traditions? The sparse representation of Arabic, Persian, and Indic musicological sources risks reinforcing epistemic bias under the guise of neutrality. Addressing this imbalance requires both community-driven data curation and critical metadata scholarship (Blanke 2018; Kräutli 2021).

If machines can now generate plausible historical narratives by recombining structured and unstructured data, what becomes of the historian's role? The challenge is not replacement, but reconfiguration: to situate human scholars as *interpreters, validators, and ethical stewards* of computational historiography. In this sense, the historian's work becomes increasingly reflexive—curating not only sources, but also the logics and biases through which machines narrate the past.

The current framework opens several avenues for extension, aimed at deepening both the semantic richness and the interpretive reach of computational musicology in a cross-cultural context. Future iterations will integrate Musiconis iconographic data through *image embeddings* and align visual representations of musical instruments, gestures, and scenes with textual and ontological entities. By linking this layer to RISM manuscript metadata, the system could also enable multimodal retrieval between *image, text, and sound notation*, enriching our understanding of how music was both *seen* and *heard* in the medieval world.

Implementing chronologically-aware embeddings—for instance, via *dynamic node2vec* or *temporal graph transformers*—can allow the system to represent *stylistic drift* and *conceptual evolution* over time. This would make it possible to trace, for example, the gradual transformation of *modal* thought into *tonal* systems, or the diffusion of rhythmic practices across regions and centuries.

A proposed concept equivalence mapping mechanism will mediate between related but non-identical theoretical systems (*maqām* ≈ *mode*, *rāga* ≈ *scale*), generating an interpretive bridge across epistemic traditions. This “translation layer” would operationalize comparative musicology in semantic form, revealing shared structures and culturally specific nuances in modal thinking.

To ensure interpretation, a human-in-the-loop annotation interface will allow historians and musicologists to evaluate, correct, and enrich automatically generated relations. These expert annotations can then be reintegrated into the graph for peer-reviewed curation, aligning computational inference with scholarly consensus. Here, the framework aims to evolve into an interactive web platform—a *Semantic Atlas of Medieval Sound*—combining narrative generation, graph visualization, and geotemporal diffusion mapping. This interface will allow users to explore the flow of musical ideas, instruments, and notations across civilizations, turning the historical record into a living, explorable network of meaning.

Building on this connective framework, a natural extension of this work involves direct integration
with the *Wikidata Embedding Project*, an initiative launched in 2025 by Wikimedia Deutschland.
While the present framework employs an independent, notebook-based embedding pipeline built with
SentenceTransformers and FAISS, the objectives of both projects are, to some extent, aligned: to
enable semantic search and reasoning across Wikidata's multilingual graph. Future iterations of the
*Semantic History of Medieval Music* system could therefore leverage the official Wikidata Vector
Database (wd-vectordb.wmcloud.org) as a standardized and continuously updated semantic layer.
Such integration would enhance scalability, ensure alignment with community-maintained
embeddings, and enable cross-project interoperability for digital humanities research. In this sense,
the framework presented here may be viewed as an early experimental counterpart to the broader
infrastructural transformation envisioned by the Wikidata Embedding Project, anticipating its
potential to provide a shared foundation for transparent, multilingual, and verifiable AI-assisted
scholarship.

Finally, the project underscores the necessity of epistemic humility in computational historiography. Algorithms can extend human interpretation but cannot replace it; their outputs remain probabilistic, contingent, and embedded within inherited data biases. As recent work on data ethics for historical research in a digital era (Clark 2019; Drucker 2021) emphasizes, maintaining rigorous human oversight—through peer review, interpretive accountability, and cultural sensitivity—is essential to prevent the reification of machine-generated knowledge. In this sense, the future of semantic historiography depends not on automation, but on the alliance between critical human inquiry and responsible AI mediation.

Conclusion

The *Semantic History of Medieval Music* project demonstrates that large-scale, open, and multilingual datasets such as Wikidata can become active instruments for rewriting the history of sound and music through transparent computational reasoning. Beyond technical proof-of-concept, this work proposes an epistemological where artificial intelligence, semantic modeling, and historical interpretation operate within a single, reflexive framework.

Key Takeaways

- Computational historiography as epistemological innovation: By transforming data retrieval and modeling into interpretive acts, the project redefines historiography as a computationally assisted inquiry grounded in verifiable evidence rather than algorithmic opacity.

- Cross-cultural modeling as decolonial methodology: Through the alignment of modal systems such as mode, maqām, and rāga, the framework challenges Eurocentric structures and foregrounds a plural understanding of medieval sound worlds.

- AI explainability as a new hermeneutic standard: The integration of graph-based reasoning, multilingual embeddings, and open data infrastructures offers a reproducible, interpretable, and collaborative alternative to black-box generative models.

Moving forward, the *Semantic Atlas of Medieval Music* will extend this framework by integrating multimodal datasets, enabling richer cross-referential analysis between textual, iconographic, and acoustic sources. Achieving this goal requires sustained collaboration between digital humanists, musicologists, and AI researchers, united by the goal of creating transparent, equitable, and participatory infrastructures for cultural knowledge.
## References

1. Al-Faruqi, Lois Ibsen. 1985. "Music, Musicians and Muslim Law." Asian Music 17 (1): 3-36.

2. Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), 610-623. New York: ACM. https://doi.org/10.1145/3442188.3445922

3. de Berardinis, Jacopo; Valentina Anita Carriero; Nitisha Jain; Nicolas Lazzari; Albert Meroño-Peñuela; Andrea Poltronieri; Valentina Presutti. "The Polifonia Ontology Network: Building a Semantic Backbone for Musical Heritage." In The Semantic Web – ISWC 2023: 22nd International Semantic Web Conference, Proceedings, vol. 14266, 302-22. Cham: Springer, 2023. https://doi.org/10.1007/978-3-031-47243-5_17

4. Blanke, Tobias, Conny Kristel, Laurent Romary. Crowds for Clouds: Recent Trends in Humanities Research Infrastructures. Agiati Benardou; Erik Champion; Costis Dallas; Lorna Hughes. Cultural Heritage Digital Tools and Infrastructures, Routledge, 2018, 978-1-4724-4712-8. https://inria.hal.science/hal-01248562

5.Bohlman, Philip V. 1988. The Study of Folk Music in the Modern World. Bloomington: Indiana University Press.

6. Burnett, Philip, Erin Johnson-Williams & Yvonne Liao (2023) Music, empire, colonialism: sounding the archives, Postcolonial Studies, 26:3, 345-359, DOI: https://doi.org/10.1080/13688790.2023.2243070

7. Chiarcos, Christian, Sebastian Hellmann, and Sebastian Nordhoff, eds. 2012. Linked Data in Linguistics: Representing Language Data and Metadata. Berlin: Springer. https://doi.org/10.1007/978-3-642-28249-2

8. Clark, Karin; Matt Duckham; Marilys Guillemin; Assunta Hunter; Jodie McVernon; Christine O'Keefe; Cathy Pitkin; Steven PRAWER; Richard Sinnott; Deborah Warr; and Jenny Waycott. "Advancing the Ethical Use of Digital Data in Human Research: Challenges and Strategies to Promote Ethical Practice." Ethics and Information Technology 21, no. 1 (2019): 59-73. https://doi.org/10.1007/s10676-018-9490-4

9. van Dis, Eva A. M., et al. 2023. "ChatGPT: Five Priorities for Research." Nature 614: 224-226. https://doi.org/10.1038/d41586-023-00288-7

10. Drucker, Johanna. 2021. The Digital Humanities Coursebook: An Introduction to Digital Methods for Research and Scholarship. London & New York: Routledge. https://doi.org/10.4324/9781003106531

11. El-Hajj, Hassan; Oliver Eberle; Anika Merklein; Anna Siebold; Noga Shlomi; Jochen Büttner; Julius Martinetz; Klaus-Robert Müller; Grégoire Montavon; and Matteo Valleriani. "Explainability and Transparency in the Realm of Digital Humanities: Towards a Historian XAI." International Journal of Digital Humanities 5 (2023): 299-331. https://doi.org/10.1007/s42803-023-00070-1

12. Erxleben, Frederic, Markus Krötzsch, Denny Vrandečić, and Max Völkel. 2014. "Introducing Wikidata to the Linked Data Web." In The Semantic Web – ISWC 2014, Lecture Notes in Computer Science, volume 8796, 50-65. Berlin: Springer. https://doi.org/10.1007/978-3-319-11964-9_4

13. Everist, Mark. "Reception Theories, Canonic Discourses, and Musical Value." In Rethinking Music, edited by Mark Everist and Nicholas Cook, 378–402. Oxford: Oxford University Press, 1999.

14. Farmer, Henry George. 1930. Historical Facts for the Arabian Musical Influence. London: Oxford University Press.

15. Hoppin, Richard H. 1978. Medieval Music. New York: W. W. Norton.

16. Hsieh, Amanda, Jacob Olley, Critical Exchanges: Globalizing Comparative Musicology? Inter-Asian Perspectives on Global Music History, Music and Letters, 2025. https://doi.org/10.1093/ml/gcaf044
17. Jeffery, Peter. "Eastern Elements in Western Chant: A Second Look over a Changed Landscape." Plainsong & Medieval Music 34, no. 1 (2025): 11–23. https://doi.org/10.1017/S096113712500004X

18. Pugin, Laurent, and Claudio Bacciagaluppi. "An Analysis of Musical Work Datasets and Their Current Level of Linkage." In Proceedings of the 7th International Conference on Digital Libraries for Musicology (DLfM '20), 32–39. New York: ACM, 2020. https://doi.org/10.1145/3424911.3425518

19. Kräutli, Florian; Esther Chen; Matteo Valleriani. "Linked Data Strategies for Conserving Digital Research Outputs: The Shelf Life of Digital Humanities." In Information and Knowledge Organisation in Digital Humanities: Global Perspectives, Edited by Koraljka Golub & Ying-Hsang Liu, 206–224. London: Routledge, 2021. https://doi.org/10.4324/9781003131816

20. Rodin, Jesse. 2014. The Josquin Research Project. Stanford, CA: Stanford University. https://cesta.stanford.edu/research/josquin-research-project

21. Shiloah, Amnon. 1995. Music in the World of Islam: A Socio-Cultural Study. Detroit: Wayne State University Press.

22. Smith, Jordan Bennett Louis, John Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen Downie. "Design and Creation of a Large-Scale Database of Structural Annotations." Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 555–560. October 2011. https://doi.org/10.5281/zenodo.1415235

23. Strohm, Reinhard. "Globale Musikgeschichte – der lange Weg: Das Forschungsprojekt 'Towards a Global History of Music' (International Balzan Prize Foundation, Mailand)." Quellen und Forschungen aus italienischen Archiven und Bibliotheken 102, no. 1 (2022): 469-482. https://doi.org/10.1515/qufiab-2022-0022

24. Tang, Rongchuan; Yang Zhao; Chengqing Zong; Yu Zhou. "Multilingual Knowledge Graph Completion with Language-Sensitive Multi-Graph Attention." Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 10508-10519. 2023. https://aclanthology.org/2023.acl-long.586/

25. Thomae, Martha E., David Rizo, Eliseo Fuentes-Martínez, Cristina Alís Raurich, and Elsa De Luca. "A Preliminary Proposal for a Systematic GABC Encoding of Gregorian Chant." In 11th International Conference on Digital Libraries for Musicology (DLfM 2024), Stellenbosch, South Africa. New York: ACM, 2024. https://doi.org/10.1145/3660570.3660581

26. Urberg, Michelle. "Pasts and Futures of Digital Humanities in Musicology: Moving Towards a Bigger Tent." Music Reference & Review (?) (2017). https://doi.org/10.1080/10588167.2017.1404301

27. Vrandečić, Denny, and Markus Krötzsch. 2014. "Wikidata: A Free Collaborative Knowledge Base." Communications of the ACM 57 (10): 78-85. https://doi.org/10.1145/2629489

28. Wilkinson, Mark D., et al. 2016. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." Scientific Data 3: 160018. https://doi.org/10.1038/sdata.2016.18

## Acknowledgments

This work was developed within the environment of the Sorbonne Center for Artificial Intelligence (SCAI), and the Institut de Recherche en Musicologie, Sorbonne Université. We wish to thank Wikimedia Deutschland, Jina.AI, and DataStax for providing open infrastructures that made the Wikidata Embedding Project possible.

All structured data are drawn from Wikidata (CC0); entity identifiers (QIDs) and properties (P-ids) are recorded in outputs. The reproducible notebook is available at: https://colab.research.google.com/drive/1H8jOLOZXjLGAAACvBYeZaT3mtJwiqod4?usp=sharing

All scripts to run SPARQL, build FAISS, and render graphs are included in the notebook repository.
## Author's Note on AI use

This study incorporates limited AI-assisted code refinement using Google Gemini for syntax correction, documentation enhancement, and the completion of Python functions. All generative modifications were reviewed and verified by the authors to ensure methodological accuracy and reproducibility.

## Licensing and Data Attribution

Wikidata content is licensed under CC0 1.0 Universal (Public Domain Dedication). No proprietary data were used. Any supplemental datasets referenced (e.g., iconographic metadata or Musiconis imagery) adhere to their respective institutional licenses. Attribution and provenance are preserved via persistent QIDs and property paths in the resulting graphs and tables, allowing independent verification and reuse.

## Ethical and Cross-Cultural Considerations

Cross-cultural semantic modeling—particularly across Latin, Arabic, and Sanskrit traditions—poses interpretive and epistemic risks. The authors acknowledge the danger of essentializing cultural categories through algorithmic representations. These risks are mitigated through human validation.

## Annex A — Query Set: Semantic Retrieval Experiment on Medieval Music

The following twenty queries were submitted through the RAG-Wikidata hybrid system. Each question was transformed into a multilingual vector representation and matched against Wikidata entities through SPARQL-based graph reasoning. They collectively aimed to evaluate the system's capacity to retrieve structured, interpretable, and cross-cultural knowledge in the domain of medieval music and related theoretical concepts.

<table><thead><tr><td>#</td><td>Research Question</td><td>Focus / Intent</td></tr></thead><tbody><tr><td>1</td><td>Which medieval composers (c. 500–1500) are recorded in Wikidata?</td><td>Entity retrieval (people)</td></tr><tr><td>2</td><td>What manuscripts document early polyphony or organum?</td><td>Material sources / notation</td></tr><tr><td>3</td><td>Which entities describe chant traditions (Gregorian, Byzantine, Mozarabic)?</td><td>Comparative liturgical traditions</td></tr><tr><td>4</td><td>What musical instruments were used in medieval Europe?</td><td>Organology / object retrieval</td></tr><tr><td>5</td><td>Which instruments are documented in Islamic or Persian sources between 800–1500?</td><td>Cross-cultural instrument comparison</td></tr></tbody></table>
<table><tr><td>6</td><td>Which people are associated with the Notre-Dame school of polyphony?</td><td>Stylistic schools / historical affiliations</td></tr><tr><td>7</td><td>What treatises discuss music theory or notation before 1500?</td><td>Theoretical literature</td></tr><tr><td>8</td><td>Which medieval manuscripts contain music notation examples?</td><td>Primary sources (codicology)</td></tr><tr><td>9</td><td>Which entities document Arabic maqām (concepts, works, people)?</td><td>Modal theory / non-Western systems</td></tr><tr><td>10</td><td>Which entities document Indian rāga (concepts, works, people)?</td><td>Comparative modal theory</td></tr><tr><td>11</td><td>How are mode, maqām, and rāga related in semantic or ontological terms?</td><td>Cross-modal conceptual mapping</td></tr><tr><td>12</td><td>What medieval locations or institutions were centers of music theory production?</td><td>Geographic diffusion of knowledge</td></tr><tr><td>13</td><td>Which female composers or performers are recorded between 1000 and 1500?</td><td>Gender and representation</td></tr><tr><td>14</td><td>What visual representations (paintings, sculptures) depict music-making in medieval contexts?</td><td>Iconography / art history link</td></tr><tr><td>15</td><td>Which theoretical concepts (e.g., modus, rhythmus, cantus planus) appear in Wikidata?</td><td>Terminological ontology</td></tr><tr><td>16</td><td>What were the main chant traditions in the Eastern Christian world?</td><td>Comparative chant systems</td></tr><tr><td>17</td><td>Which liturgical books (graduals, antiphonaries, missals) from the medieval period are documented?</td><td>Source corpus / manuscript ontology</td></tr></table>
<table><tr><td>18</td><td>Which composers or theorists were affiliated with monastic orders (e.g., Benedictines, Cistercians)?</td><td>Institutional networks</td></tr><tr><td>19</td><td>What music-related entities are geolocated along medieval trade or pilgrimage routes (e.g., the Silk Road)?</td><td>Cultural transmission / geography</td></tr><tr><td>20</td><td>Compare organum, maqām, and rāga in medieval theory: what conceptual links can be identified?</td><td>Integrative reasoning / semantic historiography</td></tr></table>

Overview of Semantic Retrieval Outcomes

<table><thead><tr><td>#</td><td>Query Focus</td><td>Retrieved Entities (n)</td><td>Null / Partial Result</td><td>% with QIDs + Labels</td><td>Notes on Mismatch / Error Patterns</td></tr></thead><tbody><tr><td>1</td><td>Medieval composers (500–1500)</td><td>47</td><td>Partial</td><td>95 %</td><td>Strong retrieval for Western figures (Hildegard, Adam de la Halle); gaps for Byzantine &amp; Islamic authors.</td></tr><tr><td>2</td><td>Manuscripts of early polyphony / organum</td><td>12</td><td>Partial</td><td>92 %</td><td>Codex Chantilly, Bamberg Codex retrieved; some “modern edition” mislabels.</td></tr><tr><td>3</td><td>Chant traditions (Gregorian, Byzantine, Mozarabic)</td><td>18</td><td>Partial</td><td>100 %</td><td>Good coverage for Western chant; weak for Syriac and Coptic entries.</td></tr><tr><td>4</td><td>Medieval European instruments</td><td>53</td><td>Complete</td><td>96 %</td><td>Rich organology; minor duplication across instrument families.</td></tr><tr><td>5</td><td>Islamic / Persian instruments (800–1500)</td><td>21</td><td>Partial</td><td>90 %</td><td>Skew toward later Ottoman entries; missing ‘ūd variant metadata.</td></tr></tbody></table>
<table><tr><td>6</td><td>Notre-Dame school affiliations</td><td>0</td><td>Null</td><td>0%</td><td>No ontology for stylistic schools or collective authorship relations.</td></tr><tr><td>7</td><td>Music-theory treatises pre-1500</td><td>28</td><td>Partial</td><td>97%</td><td>Major texts (Micrologus, Musica speculativa); Arabic titles underrepresented.</td></tr><tr><td>8</td><td>Notated manuscripts examples</td><td>14</td><td>Partial</td><td>93%</td><td>Metadata incomplete on notation type & provenance.</td></tr><tr><td>9</td><td>Arabic maqām entities</td><td>9</td><td>Partial</td><td>78%</td><td>Correct core concept but tangential links (e.g., Celtic Music WikiProject).</td></tr><tr><td>10</td><td>Indian rāga entities</td><td>16</td><td>Complete</td><td>100%</td><td>Robust retrieval; correct hierarchical link to Mode (Q731978).</td></tr><tr><td>11</td><td>Mode – maqām – rāga relations</td><td>5</td><td>Partial</td><td>100%</td><td>Triadic alignment successful; Byzantine “echoi” absent.</td></tr><tr><td>12</td><td>Centers of theory production</td><td>8</td><td>Partial</td><td>88%</td><td>Paris, Baghdad, Córdoba found; some confused with modern institutions.</td></tr><tr><td>13</td><td>Female composers / performers (1000–1500)</td><td>6</td><td>Partial</td><td>100%</td><td>Accurate (Hildegard, Beatriz de Dia); limited metadata on social roles.</td></tr><tr><td>14</td><td>Iconography of music-making</td><td>24</td><td>Partial</td><td>85%</td><td>Linked paintings & sculptures; missing image rights metadata.</td></tr><tr><td>15</td><td>Theoretical concepts (modus, rhythmus, cantus planus)</td><td>11</td><td>Partial</td><td>91%</td><td>Some Latin terms mis-parsed as biological “mode.”</td></tr></table>
<table><caption>Figure 2a. Distribution of retrieval results across the twenty semantic queries.</caption><tr><td>16</td><td>Eastern Christian chant traditions</td><td>7</td><td>Partial</td><td>86 %</td><td>Sparse coverage (Syriac, Georgian absent).</td></tr><tr><td>17</td><td>Liturgical books (graduals, antiphonaries, missals)</td><td>19</td><td>Partial</td><td>94 %</td><td>Good Latin data; insufficient dating or regional fields.</td></tr><tr><td>18</td><td>Monastic affiliations (Benedictine, Cistercian)</td><td>0</td><td>Null</td><td>0 %</td><td>Institutional relations not encoded in Wikidata.</td></tr><tr><td>19</td><td>Trade / pilgrimage routes & music</td><td>3</td><td>Partial</td><td>67 %</td><td>Weak geo-linking; “Silk Road” not connected to musical items.</td></tr><tr><td>20</td><td>Comparative query (organum – maqām – rāga)</td><td>3</td><td>Complete</td><td>100 %</td><td>Produced triadic output (Notation–Instrumentality–Modality); best interpretive result.</td></tr></table>

<figure><img src="image_4.png" /><figcaption>Figure 2b. Proportion of Western vs. non-Western coverage in Wikidata retrievals.</figcaption></figure>
<figure><img src="image_5.png" /></figure>

## Annex B — Example of a “Possible” Atlas Entry (Retrieval-based)

### Modality<fnref n="13" />

Modality refers to the theoretical and practical systems that organize musical pitch and affect in structured frameworks. In the medieval period (c. 500–1500 CE), such systems developed independently yet analogously across several cultural zones: the Latin West, the Islamic world, and South Asia. In Western Europe, the evolution of organum and later Ars Subtilior practices formalized modal thinking through notated structures. Figures such as Guido d’Arezzo (fl. 11th c.), author of the Micrologus, established systematic pitch relations and solmization principles that defined the scholastic foundations of modality. In the Islamic world, the theory of maqām—elaborated by scholars such as al-Fārābī (10th c.) and Ṣafī al-Dīn al-Urmawī (13th c.)—organized melody and affect through orally transmitted frameworks emphasizing microtonal nuance and emotional affect. In India, the theory of rāga, codified in treatises such as the Nātyaśāstra and Saṅgīta Ratnākara, articulated pitch organization and time-of-day associations within a cosmological system linking music, mood, and metaphysics. While differing in metaphysical orientation and transmission mode—Western notation, Arabic oral pedagogy, Sanskrit theoretical cosmology—these traditions share a common preoccupation with the ordered relationship of tones, intervals, and affective expression. These modal systems function as homologous structures of thought: notation (Latin West), embodied modality (Islamic world), and theoretical modality (South Asia).

### Core Entities Retrieved

<table><thead><tr><td>Entity</td><td>Wikidata ID</td><td>Description</td><td>Relation Type</td></tr></thead><tbody><tr><td>Codex Chantilly</td><td>Q2521364</td><td>Illuminated manuscript emblematic of Ars Subtilior polyphony, representing the written and notational tradition of late medieval France.</td><td>instance of → medieval manuscript</td></tr><tr><td>Mouth Harp (Scacciapensi eri)</td><td>Q102129353</td><td>Resonant idiophone with widespread Eurasian distribution; modal intonation links it conceptually to maqām practice.</td><td>subclass of → musical instrument</td></tr><tr><td>Rāga</td><td>Q216926</td><td>Melodic and affective framework in the Indian tradition; subclass of mode [Q731978].</td><td>subclass of → mode</td></tr></tbody></table>

<footnote n="13">¹³ This entry is based on entities retrieved and reflects the interpretive synthesis generated by computational methods (RAG-Wikidata hybrid retrieval). The summarized relationships between organum, maqām, and rāga should thus be understood as heuristic rather than definitive—intended to illustrate semantic and conceptual affinities within available data rather than to assert direct historical equivalence or influence. Further refinement and validation through specialist scholarship remain essential to any philological or comparative claim.</footnote>
<table><tr><td>Guido d'Arezzo</td><td>Q123174</td><td>Medieval theorist whose <i>Micrologus</i> formalized pitch relations foundational to later modal systems.</td><td>creator of → Micrologus</td></tr><tr><td>Ziryab</td><td>Q22703</td><td>Andalusian musician often credited with modal refinements in early Arabic theory.</td><td>influenced by → Abbasid musical schools</td></tr></table>

The retrieved entities collectively outline a semantic continuum of modal reasoning spanning three cultural zones:

1. Western Europe (notation) – *Organum* and *Ars Subtilior* practices systematized modal thinking through visual and symbolic notation.

2. Islamic World (embodied modality) – *Maqām* theory articulated modal affect through oral pedagogy, emphasizing microtonal nuance.

3. South Asia (theoretical modality) – *Rāga* codified melodic affect and temporal context, embedding modal systems within cosmological frameworks.

This demonstrates how different epistemic regimes—notation, performance, and theory—encoded homologous musical logics. The connection between Guido d'Arezzo and Ziryab through conceptual proximity in modal reasoning (captured via embeddings) exemplifies how computational historiography can reveal *latent continuities* obscured by disciplinary silos.

## Semantic Graph Visualization (Excerpt)

[Guido d'Arezzo] —(creator of)—> [Micrologus]
(influenced)—> [Organum]

[Ziryab] —(influenced by)—> [Abbasid Music Theory]
(connected conceptually)—> [Maqām]

[Rāga] —(subclass of)—> [Mode]

## Cross-Lingual Descriptors

<table><thead><tr><td>Language</td><td>Label(s)</td><td>Example Context</td></tr></thead><tbody><tr><td>Latin</td><td><i>modus, tonus, cantus planus</i></td><td>Medieval treatises on chant and early polyphony.</td></tr><tr><td>Arabic</td><td>(naghamah) ،نغمه ،(maqām) مقام</td><td>Descriptions in al-Fārābī's *Kitāb al-Mūsīqī al-Kabīr*.</td></tr><tr><td>Sanskrit</td><td>राग (rāga)</td><td>Theoretical frameworks in *Nāṭyaśāstra* and *Saṅgīta Ratnākara*.</td></tr></tbody></table>