"The specific technical challenges addressed in this project relate to how knowledge about the world is encoded, and how we can reason with such (messy) knowledge in a robust way.
Traditionally, in the field of artificial intelligence, logics have been used to represent and reason about information. An important advantage of logic is that the underlying reasoning processes are transparent. Moreover, logical representations naturally allow us to combine information coming from a variety of sources, including structured information (e.g. ontologies and knowledge graphs), information provided by experts, or even information expressed in natural language. However, logical inference is also very brittle. Two particularly problematic limitations in the context of web data are (i) the fact that there are no mechanisms for handling inconsistency (in most logics) and (ii) there are no mechanisms for deriving plausible conclusions in cases where ""hard evidence"" is missing. Vector space models form a popular alternative to logic based representations. The main idea is to represent objects, categories, and the relations between them, as geometric objects (e.g. points, vectors, regions) in a high-dimensional Euclidean space. Such models have proven surprisingly effective for many tasks in fields such as information retrieval, natural language processing, and machine learning. However, the underlying inference processes lack transparency, and conclusions that are derived come without guarantees. This is problematic in many applications, as it is often important that we can provide an intuitive justification to the end user about why a given statement is believed. Such justifications are moreover invaluable for debugging or assessing the performance of a system. Moreover, the black box nature of vector space representations makes it difficult to integrate them with other sources of information.
The aim of this project was to combine the best of both worlds. Specifically, methods have been developed to learn expressive vector space models, to derive interpretable semantic structures from these models, and to use such structures to implement robust forms of logic based inference. Among others, our methods make it possible to make more accurate predictions in relational domains (e.g. predicting properties of molecules, links between users of social networks, or missing facts in a knowledge base), to implement flexible information retrieval systems (e.g. finding entities that satisfy some high-level properties, even if such properties are not mentioned in available text descriptions), and to achieve deeper levels of natural language understanding."