1,111 research outputs found

    Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment

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    A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We propose a neural-symbolic system to address this limitation in this paper. It is designed as a differentiable end-to-end deep memory network that learns over abstract, generic symbols to discover entailment patterns common to any reasoning task. A key component of the system is a simple but highly effective normalization process for continuous representation learning of KG entities within memory networks. Our results show how the model, trained over a set of KGs, can effectively entail facts from KGs excluded from the training, even when the vocabulary or the domain of test KGs is completely different from the training KGs

    End-to-End Differentiable Proving

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    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

    Empowering Knowledge Bases: a Machine Learning Perspective

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    The construction of Knowledge Bases requires quite often the intervention of knowledge engineering and domain experts, resulting in a time consuming task. Alternative approaches have been developed for building knowledge bases from existing sources of information such as web pages and crowdsourcing; seminal examples are NELL, DBPedia, YAGO and several others. With the goal of building very large sources of knowledge, as recently for the case of Knowledge Graphs, even more complex integration processes have been set up, involving multiple sources of information, human expert intervention, crowdsourcing. Despite signi - cant e orts for making Knowledge Graphs as comprehensive and reliable as possible, they tend to su er of incompleteness and noise, due to the complex building process. Nevertheless, even for highly human curated knowledge bases, cases of incompleteness can be found, for instance with disjointness axioms missing quite often. Machine learning methods have been proposed with the purpose of re ning, enriching, completing and possibly raising potential issues in existing knowledge bases while showing the ability to cope with noise. The talk will concentrate on classes of mostly symbol-based machine learning methods, speci cally focusing on concept learning, rule learning and disjointness axioms learning problems, showing how the developed methods can be exploited for enriching existing knowledge bases. During the talk it will be highlighted as, a key element of the illustrated solutions, is represented by the integration of: background knowledge, deductive reasoning and the evidence coming from the mass of the data. The last part of the talk will be devoted to the presentation of an approach for injecting background knowledge into numeric-based embedding models to be used for predictive tasks on Knowledge Graphs

    Towards generalizable neuro-symbolic reasoners

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    Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedSymbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of trainable artificial neural networks. Over the course of this dissertation, we provide a brief summary of our recent efforts to bridge the neural and symbolic divide in the context of deep deductive reasoners. More specifically, We designed a novel way of conducting neuro-symbolic through pointing to the input elements. More importantly we showed that the proposed approach is generalizable across new domain and vocabulary demonstrating symbol-invariant zero-shot reasoning capability. Furthermore, We have demonstrated that a deep learning architecture based on memory networks and pre-embedding normalization is capable of learning how to perform deductive reason over previously unseen RDF KGs with high accuracy. We are applying these models on Resource Description Framework (RDF), first-order logic, and the description logic EL+ respectively. Throughout this dissertation we will discuss strengths and limitations of these models particularly in term of accuracy, scalability, transferability, and generalizabiliy. Based on our experimental results, pointer networks perform remarkably well across multiple reasoning tasks while outperforming the previously reported state of the art by a significant margin. We observe that the Pointer Networks preserve their performance even when challenged with knowledge graphs of the domain/vocabulary it has never encountered before. To our knowledge, this work is the first attempt to reveal the impressive power of pointer networks for conducting deductive reasoning. Similarly, we show that memory networks can be trained to perform deductive RDFS reasoning with high precision and recall. The trained memory network's capabilities in fact transfer to previously unseen knowledge bases. Finally will talk about possible modifications to enhance desirable capabilities. Altogether, these research topics, resulted in a methodology for symbol-invariant neuro-symbolic reasoning

    Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

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    Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal

    Neuro-symbolic Computation for XAI: Towards a Unified Model

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    The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies
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