End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

Visual Concept-Metaconcept Learning

Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associated question-answer pairs. The key is to exploit the bidirectional connection between visual concepts and metaconcepts. Visual representations provide grounding cues for predicting relations between unseen pairs of concepts. Knowing that red and green describe the same property of objects, we generalize to the fact that cube and sphere also describe the same property of objects, since they both categorize the shape of objects. Meanwhile, knowledge about metaconcepts empowers visual concept learning from limited, noisy, and even biased data. From just a few examples of purple cubes we can understand a new color purple, which resembles the hue of the cubes instead of the shape of them. Evaluation on both synthetic and real-world datasets validates our claims.Comment: NeurIPS 2019. First two authors contributed equally. Project page: http://vcml.csail.mit.edu

Sistema baseado em conhecimento (SBC) de apoio à capacitação organizacional

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2017Evitar a perda da memória organizacional e a dependência de uma ou poucas pessoas é um desafio da Era do Conhecimento. Conhecimentos chave são aqueles vitais para o cumprimento da missão, permitem alcançar os objetivos estratégicos e estão alinhados com a construção da visão organizacional. Conhecimentos chave, independentes do nível (estratégico, tático ou operacional), criam vantagens competitivas de longo, médio e curto prazo. O objetivo desta pesquisa é propor um Sistema Baseado em Conhecimento (SBC) de apoio à capacitação organizacional. A aplicação foi realizada em uma instituição bancária. É utilizado um método que combina a metodologia de engenharia de ontologias e método incremental de desenvolvimento. Engenharia de ontologias é uma metodologia, da Engenharia do Conhecimento (EC), para o desenvolvimento ordenado e por etapas de SBC. O método incremental permite chegar de forma ágil no primeiro protótipo, para posteriormente, ir incorporando novas funcionalidades em ciclos curtos sucessivos. Como resultado deste trabalho, tem-se a proposta de um SBC, suportado por ontologia, para apoio ao aprendizado e ferramenta de consulta no domínio do curso Autorregulação Bancária - Conhecimentos Gerais. Adicionalmente, foram propostas métricas de avaliação do desempenho da Gestão do Conhecimento (GC) para este e outros cursos de capacitação semelhantes na organização. A aplicação do método permitiu concluir que a metodologia híbrida, aqui proposta, auxilia efetivamente o desenvolvimento de SBC de apoio à capacitação organizacional, pudendo ser replicável em outros cursos, e tendo como critérios fundamentais a agregação de valor, a escalabilidade e a interoperabilidade.Abstract: A challenge of the Knowledge Age is avoiding loss of organizational memory and reducing dependence on few people's knowledge. Key knowledge is vital to mission fulfillment, drives the achievement of strategic objectives and is aligned with pursuing the organization's vision. Key knowledge, independent of the level (strategic, tactical or operational), creates long, medium or short term competitive advantages. The objective of this research is to propose a Knowledge Based System (KBS) to support organizational training. The research was applied at a financial institution. A method that combines ontology engineering methodology and incremental development method was used. Engineering of ontologies is a methodology, of Knowledge Engineering (KE), for the orderly and stepwise development of KBS. The incremental method allows arriving in an agile way in the first prototype, and then, incorporate new functionalities in successive short cycles. As a result of this work, there is a proposal of a KBS, based on ontology, to support learning and serving as query tool in the field of the course Banking Self-Regulation - General Knowledge. Additionally, metrics were proposed to measure the performance of Knowledge Management (KM) for this, and other similar training courses in the organization. Applying the method shows that the hybrid methodology proposed here effectively assists the development of SBC in support of organizational training, being able to be replicable in other courses, and having as fundamental criteria adding value, scalability and interoperability

Combining Representation Learning with Logic for Language Processing

The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.Comment: PhD Thesis, University College London, Submitted and accepted in 201