CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs

From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes

“…However, since these tools do not integrate directly with a knowledge model but only focus on solving a specific domain problem, the integration between tools and the existing knowledge models is rarely addressed. This causes issues when discussing the usage of these models as an ontology to store user-tracking data in a knowledge graph [21], explore it [8] and perform analysis over it. For instance, the interactivity taxonomy of von Landesberger et al [47] cannot be used within the descriptive ontology of Sacha et al [40].…”

“…KGs expand knowledge models' ontology by defining a specific way of structuring knowledge as relationships through a graph-based framework, which allows the ontologies discussed so far to use KGs as a means to store information within a graph database, such as neo4j [36]. Compared to typical databases, the structure of KGs focuses less on the usual transactional or row-based structure [8]. It focuses more on the types of knowledge properties and relationships between these properties.…”

“…To design such structures, one must use Web Ontology Language (OWL) or a similar ontology language [12]. By designing VAKG's ontology with KGs and TKGs in mind, we enable easier ways for both temporal and atemporal visualization and analysis to be done with VAKG through existing works [8,13,23,24,49].…”

“…There are tools available [8,24,27,36] that simplify the process of using these graph analysis techniques, including graph summarization processes [22] to simplify large graphs, exploration of KGs through VA [8] and KG embedding [50]. To use KG embedding, one must embed the classes and relationships of a KG into a continuous vector space, which allows for further operations over the KG, such as KG completion and relation extraction.…”

“…Despite the significant impact and popularity of systems created using such works, the lack of a formal ontology that singlehandedly captures and compares a VA system's workflow to how and with what reason its users transform data into knowledge is a missed opportunity. Even works which pairs ontologies with Knowledge Graphs (KGs) for analytical purposes [5,8,22] can't analyse the VA workflow in its entirety. In other words, even though we have access to literature with well-built models and applications, no methodology provides a way to simultaneously understand on a per-user basis how past knowledge aided in generating new knowledge, to identify the workflow which lead to such knowledge, to compare multiple user's intentions and behavior patterns, to relate them to a VA workflow's state space, to generate the full user and computer interactivity patterns during the workflow and which allows its further customization and analysis.…”