Review Comment:
This paper entitled “Semanticizing Sociability: Documenting Relationships in the context of Cultural Heritage” proposes a linked data model for modeling social relations inferred from historical records. The paper is well-written, and clear. It outlines the importance of modeling relations but also the challenges in re-piecing the networks from the past, particularly when relational information is not specifically conserved. The proposed model is original and makes a contribution to the field by extending current models.
Modeling of real social networks is always a challenge, as relationships are often modelled from reported or inferred relationships. For example, a kinship relationship of siblings may be recorded in a birth record in connection to a shared birth mother or father, while relations are also inferred through affiliation- shared attendance at a event or board members of an organization, and so forth. As the authors so clearly explain in addition to not only having scattered records, there is also a lack of a compatible format for information that allows users – historians, curators or the public, to easily identify relations between items. In addition, the ways that networks evolve is dependent on the context. Thus in working to understand networks we not only need information about the entities – nodes, and relations – edges, but also other factors of time, place and attributes of the entities and relations. This makes modeling records and historical information of social networks extremely complex. The authors do a great job of explaining the shortcomings of minimalist approaches for conceptualizing social relations.
Although, I agree with the propositions, and I am very curious about how this model can be implemented in practice; it is notable that few methodological references of networks and network data collection are mentioned. Work on social network analysis as a method and the validity and reliability of conceptualizing relations is an entire sub-field of social networks that could be integrated into this work to further solidify the proposed arguments. I would suggest starting out by reviewing: 1) Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications and 2) Marsden, P. V. (1990). Network data and measurement. Annual review of sociology, 435-463. And specifically for historical social networks: 1) Bearman, Peter S., Moody, J. & Faris, R. (2002). “Networks and History.” Complexity, no. 8: 61–71. And 2) Van De Camp, M., & van den Bosch, A. (2011). A link to the past: constructing historical social networks.
In Proceedings of 2nd Workshop Computational Approaches to Subjectivity & Sentiment Analysis (pp. 61-69).
In addition, I would question the authors’ emphasis on structural parity in the proposed model and as a requirement for asserting or validifying social networks from historical records. I do not disagree that it would provide certainty in inferring networks and it is also a core practice for curators and researchers in confirming or asserting information; although incompleteness of information about social networks both missing nodes and relations is inherent in social networks. See: 1) Kossinets, G. (2006). Effects of missing data in social networks. Social networks, 28(3), 247-268. And 2) Wang, D. J., Shi, X., McFarland, D. A., & Leskovec, J. (2012). Measurement error in network data: A reclassification. Social Networks, 34(4), 396-409. And 3) Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social networks, 28(2), 124-136. Instead I would rather suggest to the authors to consider how to implement a confidence interval for relations based on the number or type of confirmed relations. This is also extremely important for valorization in making these data available for research, as there is a drastic difference in what role this interval may play in practice for research. For example, the importance of a confidence interval would be different for a researcher seeking a list of relations to decide on where to do a closer reading, versus a network researcher who would seek to analyze the structure and positions of the historical networks. Given this was a presentation of the proposed model the aspect of valorizing relational data of collections was not introduced, although I look forward to following the authors' future work to see how it is being implemented in practice and how the model will evolve considering the shapes of the data available and context of the networks recorded.
Please note there is something wrong with LaTeX file with figure 11 that cuts that page and thus columns oddly in half.
This paper presents a novel model for modeling relations and is thus useful for cultural heritage institutions and researchers in considering how to model and thus conserve, store and organize historical relational information of their collections. I recommend this paper for publication in this special issue; pending 1) the integration of aspects of social network methodology, and 2) the consideration of a proposal for integrating the provenance and thus a confidence interval for addressing the issue of structural parity, to further strengthen their arguments.
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