The Inescapable Duality of Data and Knowledge

ion and Analogies One of the key issues underlying the debate about the effectiveness of data-driven approaches vs knowledge-based approaches is the source and the creator of abstractions [Mitchell 2021]. Data-driven approaches derive abstractions utilizing the trends and patterns explicit or implicit in the copious amounts of data, and its utility is tied to the representativeness of the data with respect to the domain of discourse. In contrast, knowledge-based approaches typically present abstractions that are obtained directly or indirectly by end-user applications and goals. Insofar as these two orthogonal approaches yield overlapping abstractions, we can use them synergistically. Further, their hybridization presents a viable strategy to leverage their complementary strengths. Data-driven abstractions can group values into coarser categories on the basis of data usage pattern but labelling them sensibly and developing abstractions with specific purpose usually requires human-in-the-loop. For instance, abstractions of temperature ranges and temperature thresholds that are relevant to different states of matter of water (such as ice, melting point, water, boiling point, and steam) are different from those relevant to human health (such as normal temperature, fever, mild fever, high fever, and chill). In circuit design, we can describe a full adder at different levels of abstraction in terms of two half-adders, or using logic gates (either using AND, OR and NOT gates, or NAND/NOR gates), or using flip-flop circuits, or using transistor netlists, etc. In the context of image recognition and transfer learning, the later stages of neural network layers capture higher-level abstractions/features that mirror the nature/components of the images in the training data and the end-user application, and probing them can improve our confidence in its working and provide insights about the potential limitations that can be addressed and remedied. Our effort in deep knowledge-infused learning (within the shallow, semi-dep and deep infusion variety [Sheth et al 2019]) is based on this intuition. Analogical reasoning involving primitive shapes (e.g., circle, square, and triangle) and their relationships (e.g., inside, outside, adjacent, above, below, left, right, and overlapping) in an image requires non-trivial human insights and geometric reasoning [Evans 1987]. In fact, this example presents an interesting situation where we can use deep learning algorithms to recognize primitive shapes and then use geometric analogical reasoning to solve non-trivial puzzles. As discussed above, there are several different motivations and applications for combining deeplearning powered neural networks with knowledge-based systems. Developing a modular framework to combine data-driven approaches that are effective at extracting useful features from low-level sensory and linguistic data, with declarative models of normal behavior such as through the incorporation of physical laws, medical knowledge, and linguistic knowledge, as well as anticipated failure modes and exceptions, can go a long way in leveraging insights from and exploiting the duality of data and knowledge.