As We May Link: A model to support aggregated scientific knowledge
Today, researchers are bogged down by continually growing amount of complex and diverse scientific knowledge, fragmented and dispersed among various disciplines, communities and information resources. Contemporary digital tools are efficient in dealing with complexity and diversity of scientific knowledge and the process of science, but they have compartmentalized scientific knowledge among various disparate and disconnected systems. For example, databases are used to structure data to facilitate its easy retrieval; workflows are used to represent the process of experiments; analytical tools are used to support analyzing data and visualization tools to visualize data and results to gain better understanding. However, they rarely connect or join together to synthesize an integrated view. Our digital knowledge ecosystem is siloed and poses a challenge for researchers to search, comprehend and reproduce scientific experiments.
Vannevar Bush, in his article ‘As we may think’, discussed the huge data and information deluge and the challenge brought by the fragmentary nature of scientific knowledge. He proposed an imaginary machine – Memex – that could tie knowledge records in a mesh of associative trails, which can be reviewed and consulted as a form of graph search. This talk will discuss a model that adopts Bush’s associationist view to integrate scientific knowledge. Categories are commonly used in databases (in the form of logical schema) and ontologies (as concepts and properties), but often these artifacts are disconnected from eachother. The proposed model connects categories, along with their process of construction and evolution, with a database and ontology via tools that support their evolution. Connecting these knowledge artifacts (via their digital tools) explicitly not only provides an integrated view, but may also be capitalized to support mediation among these artifacts and keeping them consistent with new conceptualization. Such mediation among scientific artifacts will reconnect the computationally enabled science and the knowledge underpinning it.