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Ontological Model for Describing the Art Market in the Semantic Web

Core Concepts
This dissertation presents the first version of an ontology, ZAMO (Zeri Art Market Ontology), aimed at modeling the art market domain by capturing the peculiarities of this sector and leveraging data collected by the Fondazione Federico Zeri during its research activities.
The dissertation presents the development of ZAMO, an ontology for modeling the art market domain. It begins by recognizing the existing conceptual models relevant to this field, such as CIDOC-CRM, the Organization Ontology, and previous work on modeling the art auction domain. The ontology is structured into three main modules: Agents: This module describes the key actors in the art market, including individuals (e.g. art dealers, experts) and organizations (e.g. art galleries, auction houses). It captures aspects like family businesses, professional roles, and membership in associations. Events: This module focuses on the core transactions in the art market, such as purchases, loans, and donations of artworks. It also models the expertise and value attribution processes that influence the pricing of artworks. Sources: This module represents the various sources used to reconstruct the history of the art market, including archival materials, bibliographic references, and art collections. The ontology was developed following the SAMOD methodology, which involves iterative refinement based on competency questions and motivating scenarios. The final ontology aims to provide a comprehensive and interoperable model for representing and integrating data related to the art market domain.
The art market entails a number of resistances to the dissemination of data on buyers, dealers, and sale prices of works. The Fondazione Federico Zeri preserves a photo archive of 290,000 photographs of artworks and monuments, a library of 46,000 books, and a collection of 37,000 auction catalogues. The ontology aims to represent over 500 names of individuals active in the art market during the last two centuries.
"The nature of the art market entails a number of resistances to the dissemination of data on buyers, dealers, and sale prices of works." "The lack of reliable data about transactions has proven to be the first in a series of aspects that characterize this domain and distinguish it from other business sectors."

Deeper Inquiries

How can the ontology be extended to model the complex social networks and power dynamics within the art market?

To model the complex social networks and power dynamics within the art market, the ontology can be extended by introducing new classes and properties that capture the relationships between different agents, organizations, and events. Agent Relationships: Define new properties to represent the relationships between agents, such as "collaboratesWith" to show partnerships or collaborations between art dealers, "advises" to indicate advisory roles, and "belongsToNetwork" to illustrate membership in specific art market networks. Organizational Structure: Introduce classes to represent hierarchical structures within organizations, like "Subsidiary" to show subsidiary relationships between different branches of a gallery or auction house, and "BoardMember" to denote individuals holding positions of power within an organization. Event Influence: Create properties to capture the influence of events on agents and organizations, like "impactsValue" to show how specific events affect the value of artworks or "influencesDecision" to depict how events shape business decisions within the art market. Historical Context: Incorporate a class for historical context to provide a framework for understanding the evolution of social networks and power dynamics over time, allowing for the analysis of trends and shifts in influence within the art market. By expanding the ontology with these elements, researchers can gain a more comprehensive understanding of the intricate relationships and power structures that shape the art market.

How might the ontology be used to uncover biases and inequities in the art market, such as the underrepresentation of certain artists or dealers?

The ontology can be leveraged to uncover biases and inequities in the art market by enabling researchers to analyze data and relationships in a structured and systematic manner. Here's how it can be applied: Representation Analysis: Use the ontology to identify patterns of representation, such as the frequency of certain artists or dealers in transactions, exhibitions, or collaborations. By analyzing these patterns, researchers can uncover disparities in visibility and recognition. Attribution Assessment: Utilize the ontology to track attributions and value propositions made by different agents. By examining how attributions are assigned and how values are determined, researchers can identify biases in the assessment of artworks and artists. Network Evaluation: Apply network analysis techniques to the data modeled in the ontology to identify clusters of influence and power within the art market. By examining the connections between agents, organizations, and events, researchers can pinpoint areas where biases may be present. Historical Comparison: Compare data over time using the ontology to detect shifts in representation and valuation. By analyzing changes in trends and practices, researchers can uncover historical biases and inequities that have persisted or evolved in the art market. By using the ontology as a tool for structured data analysis, researchers can uncover hidden biases and inequities that may impact the representation and valuation of artists and dealers in the art market.

What insights about the evolution of artistic tastes and trends could be gleaned by applying network analysis techniques to the data modeled in this ontology?

Applying network analysis techniques to the data modeled in the ontology can provide valuable insights into the evolution of artistic tastes and trends in the art market. Here's how this analysis could offer insights: Influence Mapping: By analyzing the connections between agents, organizations, and events in the art market network, researchers can map out the flow of influence and identify key players who shape artistic tastes and trends. This can reveal the pathways through which new ideas and styles gain traction. Community Detection: Network analysis can help identify distinct communities or clusters within the art market network based on shared interests, collaborations, or affiliations. By examining these communities, researchers can uncover subcultures and niche trends that influence artistic preferences. Centrality Analysis: Evaluating the centrality of agents and organizations in the network can highlight influential nodes that drive the adoption of certain artistic styles or movements. Understanding the central players in the network can provide insights into the dissemination of trends and the formation of artistic consensus. Temporal Analysis: By tracking changes in network structures over time, researchers can observe how artistic tastes and trends evolve. Network analysis can reveal shifts in influence, the emergence of new trends, and the decline of established practices, offering a dynamic view of the art market's evolution. By applying network analysis techniques to the data modeled in the ontology, researchers can gain a nuanced understanding of how artistic tastes and trends develop, spread, and transform within the complex ecosystem of the art market.