Expanding commonsense knowledge bases by learning from image tags

I present a method for learning new commonsense facts to augment existing commonsense knowledge bases by using the metadata of large online image collections. Online image collections present a source of knowledge that is supported by many contributors, has good representation of objects and their properties, and is visual. The collection's broad support of objects and object properties ensure the relevance and quality of the commonsense knowledge collected, while the visual focus provides a different subset of knowledge than typical text corpora. Using the image metadata provides a text representation of the visual information. Therefore, I can use classifiers trained on existing text-based knowledge bases to learn relationships between concepts represented in the images. I collect two datasets of more than 1 million images each, one consisting of animal images, one of room interiors. The images are tagged with relevant concepts by their owners. I train classifiers using facts from two popular commonsense knowledge bases, ConceptNet and Freebase, to classify the relationships between frequent concept pairs. The output is a list of more than 90,000 proposed facts, which are in neither source knowledge base

Machines with human-like commonsense

I will review the main problems concerning commonsense reasoning in machines and I will present resent two different applications - namaly: the Dual PECCS linguistic categorization system and the TCL reasoning framework that have been developed to address, respectively, the problem of typicality effects and the one of commonsense compositionality, in a way that is integrated or compliant with different cognitive architectures thus extending their knowledge processing capabilities In doing so I will show how such aspects are better dealt with at different levels of representation and will discuss how the adoption of a cognitively inspired approach be useful in the design and implementation of the next generation AI systems mastering commonsense

Cultivated cure, regenerated affliction

In this think piece, I interrogate the notion of cure in order to address the idea of disease. My intention is to show how emerging biotechnological modalities that cultivate an idea of ‘cure as regeneration’ dislocate expert knowledge, descriptions of disease, and its representation into contested new terrains. In approaching disease from the vantage point of the ‘cultivated cure’ I seek to trouble our commonsense view of afflictions. Drawing on ethnographic data from a longitudinal project engaged in mapping stem cell technologies in India, I conceptualize how ‘cure as regeneration’ reanimates the figures of disease and medical knowledge. I take up Veena Das’s challenging query: is it necessary to define terms – illness, disease, diagnosis, health – that defy neat characterization

Commonsense knowledge representation and reasoning with fuzzy neural networks

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is employed as a basic element for the connectionist model. The representation and reasoning ability of the model is described through examples