An analogical proportion is a statement of the form "A is to B as C is to D", written "A : B :: C : D". The underlying idea is that the relation between A and B is similar to the one between C and D.
On the one hand, analogical proportions ground analogical inference that has been used in various machine learning tasks such as classification, decision making, and automatic translation, with competitive results. On the other hand, they support analogical extrapolation that can be used to solve hard reasoning tasks, such as IQ tests. It can also be used in data augmentation, especially in learning environments with few labeled samples.
What makes reasoning with analogies special is its ability to process simultaneously similarities and dissimilarities. This characteristic establishes bridges between the two main axes of AI: knowledge representation and reasoning (KRR) and machine learning (ML). Analogical reasoning contributes to a transparent AI as it is close to human reasoning and provides explanations based on examples and counter-examples. The objective of the ANNa project is to provide an online platform to detect, solve, and reason on analogies, with multiple applications in various domains, for instance, in NLP, biomedical sciences, as well as in industry.
The first application of the ANNa project is on morphology, i.e., on the structure of words. Two tools have been developed, namely ANNa-MD and ANNa-MR, respectively for analogy detection and resolution.