Vasari Diagram (Revised 2025)

Vasari Project (November 2021)

How does the Italian Renaissance painter Masaccio relate to the twentieth century American photographer Kathy Grove? And how does the nineteenth century French painter Rosa Bonheur find herself related to the cyberfeminist organization subRosa? In fields as Art history, Wikipedia reveals broad spheres of thematic connexions and networks going far beyond traditional monographic approaches. Each artist can find himself connected to many personalities and topics, by the means of hyperlinks. The Vasari project aims to explore these vast thematic spheres and organize them in accessible didactic graphs. The progressive circles contain several categories of relations such as male and female personalities, (painters and non-painters), as well as lists of art museums, artistic themes, technics and others. A simple glance is enough to discover, for instance, all the museum collections, containing the art works of a particular painter, different Wikimedia lists in which his or her name are involved, or some unexpected cross-historical social links. The Vasari graphs try to reveal the underlying network of multiple cultural references and interdependences, proliferating around old masters in Wikipedia, but difficult to see directly. The simple visual organization of voluminous quantitative information is a short path to new insights in the field. Inspired among others by the theories of Edward Tufte, this project invites to explore the Wikipedia dimension of Art history and its benefits to didactics and research.

Temenuzhka Dimova

Vasari Project (Revised 2025)

How does the visual language of Renaissance painter Masaccio resonate with the conceptual practices of contemporary artists like Jenny Holzer? And in what ways can the legacy of Rosa Bonheur intersect with the feminist and digital aesthetics of the 21st century?

The 2025 revisited edition of the Vasari Project moves beyond static graph structures to embrace a new paradigm of semantic exploration in art history. By integrating Weaviate, a vector-based search engine, the project now enables contextual and conceptual connections across vast corpora of Wikipedia articles sections ( with a total of 445,668 data objects indexed ) .

Instead of relying solely on hyperlink structures, this new version leverages AI-driven embeddings to detect deep relationships between historical figures, artistic movements, iconographic themes, and institutions. Through semantic search, users can now query not just "who influenced whom", but "how artistic intentions, political gestures, and gendered narratives echo across centuries."

The interactive interface dynamically surfaces multi-layered relations: painters and non-painters, curators, places, techniques, artworks, and thematic categories — all projected in a visually intuitive circular diagram. These graphs are enriched by machine-learned associations, allowing for unexpected cross-temporal insights, such as the conceptual link between a 14th-century altarpiece and a 20th-century performance art piece.

Weaviate

Weaviate is an open-source, schema-aware vector database designed to support semantic search at scale. It integrates machine learning components natively, allowing both data objects and user queries to be vectorized using modular embedding models. These models may include popular architectures such as BERT, Sentence Transformers, Cohere, OpenAI embeddings, or locally hosted alternatives like BGE.

At its core, Weaviate enables the indexing and retrieval of high-dimensional vectors. Each object—such as a Wikipedia section, an image caption, or a textual description—is transformed into a numeric vector and stored within a vector index. Upon receiving a search query, Weaviate computes the query's vector and performs nearest-neighbor search to retrieve semantically similar items based on distance metrics like cosine similarity.

One of Weaviate’s distinguishing features is its ability to support named vectors and multi-vector configurations, enabling different fields (e.g., title, abstract, content) to be indexed using specialized models. This is particularly advantageous in domains where vocabulary is heterogeneous or where concepts are abstract and context-dependent, such as art history or the humanities.

To further enhance the quality of results, Weaviate supports re-ranking mechanisms. These apply a second-stage model—such as Cohere’s rerank-english-v3.0—to reorder initial results based on contextual relevance. Such methods are crucial when the initial vector search returns semantically plausible, but not contextually precise, matches.

Weaviate also accommodates hybrid retrieval, combining classical keyword filtering (BM25-style) with semantic similarity, thereby ensuring both precision and recall in complex search tasks.