The lack of commitment to help construct empirical domain ontologies, compared to schemas (see below), has created a serious problem for the development of sophisticated intellectual digital tools that are meaningful, shareable and reusable in these domains and that do not reinforce existing issues. There is now a legacy of integration projects in cultural heritage and the humanities costing many millions in different world currencies over the last 30 years with little progress in representing interconnected knowledge which is held by community and subject experts, not documentation systems.
Creating information without ontological references creates a low level of sustainability and efficiency. Vocabularies are epistemological, not ontological. They are abstractions of the mind, and terminology, particularly in History and Cultural Heritage, are not sustainably reconcilable, and in many cases it is not beneficial to use them this way. Even if a universal taxonomy is produced as an intermediate means of integration, this tends towards artificiality, lack of diversity, reductive datasets and ongoing data management issues limiting the type of complex data representation that would otherwise make computers more useful to knowledge workers and scholars. This has been evidenced by many European projects. It is not readily apparent to many of us that computers aren’t already doing this because we live in what seems like a networked world, but when you delve deeper the quality and extent of these connections are exposed as lacking.
Knowledge representation is concerned with capturing ‘knowledge’, rather than the basic fields of intrinsic information that databases produce. Knowledge representation models represent more sophisticated patterns of information not available in databases which is one reason for the limited scope of use and lack of sustainability of databases outside core organisational functions. Databases have no semantics and cannot represent the relations that are inherent in the real world. The real digital network hubs are not the servers, infrastructures, vocabularies, fixed models or functions, but carefully constructed ontologies which are scientifically founded. Historians of all types have a commitment to uncovering the reality of history using empirical methods supporting interpretative thinking, but their textual narratives lack the ability to create a synthesis of different vantage points at different levels of generality.
Parallel to this ongoing issue in computer science, cultural heritage institutions adopted computer database systems at a relatively early stage (the first conference on Cultural Heritage and Computing was in 1968) and realised that computers and networks could be used to unite knowledge across different institutions and create a totality that was much greater than the sum of its parts. One of the main benefits of a cultural heritage sector is (or should be) to maintain knowledge about human history which is interconnected – not separate and fragmented – which explains the constant investment of money into this problem.
However, databases are designed to solve discrete problems and unlike knowledge representation approaches, do not incorporate semantics. Different museums, archives and galleries invested in new computer technology and software. It gradually became clear that even if they used the same software, the content, structure, conventions and terminologies would be different. Over a period of 30 years, without reference to the debate on knowledge representation in computer science, many cultural heritage professionals, often not subject experts, decided that the only way to integrate cultural heritage data systems was to create standard sets of vocabularies that all museums adhered to. However, computer scientists, whether they followed the ontological road, or deliberately concentrated on reasoning using epistemological data without an interest in wider networks of connected knowledge, all already knew that epistemological integration was practically impossible. There is ample evidence for this from project archives like the EU project database CORDIS.
The vocabulary approach was flawed because it again relied on artificial concepts. Over the last 30 years large amounts of public investment into many different systems and technologies (all based on databases or database mindsets) using artificial modelling and Western vocabulary integration has provided no great innovation. The SKOS standard explicitly says: