Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and an entity, and a TBox, i.e., terminology axioms between two concepts. Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers. While previous NLR methods can give specific entity-level answers, i.e., ABox answers, they are not able to provide descriptive concept-level answers, i.e., TBox answers, where each concept is a description of a set of entities. In other words, previous NLR methods only reason over the ABox of an ontology while ignoring the TBox. In particular, providing TBox answers enables inferring the explanations of each query with descriptive concepts, which make answers comprehensible to users and are of great usefulness in the field of applied ontology. In this work, we formulate the problem of neural logical reasoning across TBox and ABox (TA-NLR), solving which needs to address challenges in incorporating, representing, and operating on concepts. We propose an original solution named TAR for TA-NLR. Firstly, we incorporate description logic based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities. Moreover, we design operators involving concepts on top of fuzzy set representation of concepts and queries for optimization and inference. Extensive experimental results on two real-world datasets demonstrate the effectiveness of TAR for TA-NLR.

The US Army recently announced that it is developing the first drones that can spot and target vehicles and people using artificial intelligence (AI). This is a big step forward. Whereas current military drones are still controlled by people, this new technology will decide who to kill with almost no human involvement. Once complete, these drones will represent the ultimate militarisation of AI and trigger vast legal and ethical implications for wider society. There is a chance that warfare will move from fighting to extermination, losing any semblance of humanity in the process.

Weapons of war have evolved over time, but the decision to kill has always been left with humans. But with developing AI and autonomous technology, it is now possible to build killing machines that require no human input at all. Taking the final decision away from a human raises serious ethical concerns over the use of fully-autonomous weapons. It could mean wars will be less about fighting, and more extermination. In an article for The Conversation, Dr Peter Lee, Director for Security and Risk Research and Innovation at the University of Portsmouth explains the potential devastation these machines could cause.

The US Army recently announced that it is developing the first drones that can spot and target vehicles and people using artificial intelligence (AI). This is a big step forward. Whereas current military drones are still controlled by people, this new technology will decide who to kill with almost no human involvement. Once complete, these drones will represent the ultimate militarisation of AI and trigger vast legal and ethical implications for wider society. There is a chance that warfare will move from fighting to extermination, losing any semblance of humanity in the process.