Developing FAIR Ontological Pathways: Linking Evidence of Movement in Lidar to Models of Human Behaviour

Abstract

This paper proposes an ontological approach to connect the archaeological topographic evidence for movement in the landscape which can be derived from interpretation and spatial analysis of airborne lidar data with models of movement derived from modeling exercises such as Agent Based Modelling or Cost Path Modelling. This computational ontology enables the investigation of movement and its topographic manifestations in the landscape at various spatio-temporal scales. It creates an explicit framework for accessing meaningful information about movement generated through research using both detection and modelling-led approaches. Developing explicit computational frameworks to provide meaningful context is critical, particularly as remote sensing and modelling projects increase in scale and complexity. The process of developing a computational ontology exposes a deeper underlying issue, and one applicable to many topics we address as archaeologists: if we begin to unpack the concept of 'movement' it is readily apparent that it is a complex phenomenon, like many human habits, and studying it requires drawing together a variety of types of physical evidence and multiple, often competing, theoretical models of human processes and practices. If we wish to make archaeological 'data' on movement available, how do we create appropriate contextual information -really useful metadata -so that this data can be incorporated into the variety of studies for which knowledge of movement is relevant? This is essentially the challenge posed broadly by the FAIR principles, and in particular by the principle of interoperability, which suggests that we "use a formal, accessible, shared, and broadly applicable language for knowledge representation". Rather than simply seeking to fulfill the requirements of an arbitrary standard, attempting to meet the challenge of interoperability provides an impetus and opportunity to attempt to bridge the gap between data and model, and to reconsider how we conceive and represent knowledge in archaeological digital data and modelling projects. This kind of computational ontology, we suggest, can serve as the key for making the data from both these sources actually FAIR.

Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

“…By formalizing our expectations of what modifications may have taken place to cultural materials, we can improve our capacity to predict and identify these features within the modern landscape, as it widens our expectations of the form that these materials may take given changes over time. The same has recently been advocated by Nuninger et al (2020aNuninger et al ( , 2020b, who develop conceptual frameworks for identifying and tracing movement via "pathways" in the archaeological record. By formalizing our conceptions of these cultural phenomena, it allows for researchers to improve their interpretations of archaeological data by rethinking the scales at which they investigate certain concepts.…”

“…In brief terms, DhSMs are a type of ontological system (sensu Guarino et al, 2009), meaning that it constitutes a formalized model by which we can conceptualize the archaeological record in its many forms. There are numerous sources of bias that can affect data collection and the results of different research agendas, and formalized conceptual frameworks can aid in replicability of research and interoperability of datasets (Nuninger et al 2020a(Nuninger et al , 2020b). DhSMs are one form of formalized conceptual framework that are designed to create a link between theoretical notions of taphonomic effects and digital representations of these materials in remote sensing data (Magnini and Bettineschi 2019;also see Arvor et al 2019).…”

Documenting information making in archaeological field reports

“…Report writing is, similarly to the scholarly work Documenting information making described by Collins (1992), an exercise of piecing together a conceptual system within which the report writers are developing rather than applying rules. The conceptual interoperability (Nuninger et al, 2020) of the documentation depends on the coherence of this system. In this respect a closer attention to correspondences, i.e.…”

Linking Theories, Past Practices, and Archaeological Remains of Movement through Ontological Reasoning

“…Second, it allows us to consider the various relationships between the terms and concepts used: Hierarchical, topological, temporal, and spatial. In this way, we can create the metadata needed to describe and access meaningful information on movement, and strive towards interoperability, as defined under the FAIR principles [13].…”

“…An unintended consequence of this process is that features which were not considered important when designing the machine learning project could be further marginalized within the archaeological record, as the existing state of knowledge of the project team and their research biases are reproduced and reinforced at an unprecedented scale. This problem of "circular reasoning" and "finding more of what you expect to find", posed by the strong reinforcement through machine learning of implicit ideas and knowledge about what physical features represent different activities and processes, has been noted by researchers working in this domain [11][12][13], particularly in the context of the transfer of these algorithms between different environmental or archaeological contexts.…”