Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms. Current methods typically fail to find the appropriate level of generalization in a concept hierarchy for a given set of visual examples. Recent work in cognitive science on Bayesian models of generalization addresses this challenge, but prior results assumed that objects were perfectly recognized. We present an algorithm for learning visual concepts directly from images, using probabilistic predictions generated by visual classifiers as the input to a Bayesian generalization model. As no existing challenge data tests this paradigm, we collect and make available a new, large-scale dataset for visual concept learning using the ImageNet hierarchy as the source of possible concepts, with human annotators to provide ground truth labels as to whether a new image is an instance of each concept using a paradigm similar to that used in experiments studying word learning in children.

Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on the function implied by the activation of brain regions, it is necessary to combine a wide exploration of the various brain functions and some inversion of the statistical inference. Here we introduce a methodology for accumulating knowledge towards a bidirectional link between observed brain activity and the corresponding function. We rely on a large corpus of imaging studies and a predictive engine. Technically, the challenges are to find commonality between the studies without denaturing the richness of the corpus.

While topic models can discover patterns of word usage in large corpora, it is difficult to meld this unsupervised structure with noisy, human-provided labels, especially when the label space is large. In this paper, we present a model-Label to Hierarchy (L2H)-that can induce a hierarchy of user-generated labels and the topics associated with those labels from a set of multi-labeled documents. The model is robust enough to account for missing labels from untrained, disparate annotators and provide an interpretable summary of an otherwise unwieldy label set. We show empirically the effectiveness of L2H in predicting held-out words and labels for unseen documents. Papers published at the Neural Information Processing Systems Conference.

Thomas Kuhn proposed his paradigmatic view of scientific discovery five decades ago. The concept of paradigm has not only explained the progress of science, but has also become the central epistemic concept among STM scientists. Here, we adopt the principles of Kuhnian philosophy to construct a novel ontology aims at classifying and evaluating the impact of STM scholarly articles. First, we explain how the Kuhnian cycle of science describes research at different epistemic stages. Second, we show how the Kuhnian cycle could be reconstructed into modular ontologies which classify scholarly articles according to their contribution to paradigm-centred knowledge. The proposed ontology and its scenarios are discussed. To the best of the authors knowledge, this is the first attempt for creating an ontology for describing scholarly articles based on the Kuhnian paradigmatic view of science.

Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules. However, one of their main deficiencies compared to rule-based reasoners is the lack of derivations for the inferred triples (i.e. explainability in AI terms). In this paper, we build on these approaches to provide not only the inferred graph but also explain how these triples were inferred. In the graph words approach, RDF graphs are represented as a sequence of graph words where inference can be achieved through neural machine translation. To achieve explainability in RDFS reasoning, we revisit this approach and introduce a new neural network model that gets the input graph--as a sequence of graph words-- as well as the encoding of the inferred triple and outputs the derivation for the inferred triple. We evaluated our justification model on two datasets: a synthetic dataset-- LUBM benchmark-- and a real-world dataset --ScholarlyData about conferences-- where the lowest validation accuracy approached 96%.

Ontologies order and interconnect knowledge of a certain field in a formal and semantic way so that they are machine-parsable. They try to define allwhere acceptable definition of concepts and objects, classify them, provide properties as well as interconnect them with relations (e.g. "A is a special case of B"). More precisely, Tom Gruber defines Ontologies as a "specification of a conceptualization; [...] a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents." [1] An Ontology is made of Individuals which are organized in Classes. Both can have Attributes and Relations among themselves. Some complex Ontologies define Restrictions, Rules and Events which change attributes or relations. To be computer accessible they are written in certain ontology languages, like the OBO language or the more used Common Algebraic Specification Language. With the rising of a digitalized, interconnected and globalized world, where common standards have to be found, ontologies are of great interest. [2] Yet, the development of chemical ontologies is in the beginning. Indeed, some interesting basic approaches towards chemical ontologies can be found, but nevertheless they suffer from two main flaws. Firstly, we found that they are mostly only fragmentary completed or are still in an architecture state. Secondly, apparently no chemical ontology is widespread accepted. Therefore, we herein try to describe the major ontology-developments in the chemical related fields Ontologies about chemical analytical methods, Ontologies about name reactions and Ontologies about scientific units.

The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable and speed up the deployment of automated driving technologies, due to the complex operational domain of AVs. As traditional methods for assessing vehicles are not applicable for AVs, other approaches have been proposed. Among these, real-world scenario-based assessment is widely supported by many players in the automotive field. In this approach, test cases are derived from real-world scenarios that are obtained from driving data. To minimize any ambiguity regarding these test cases and scenarios, a clear definition of the notion of scenario is required. In this paper, we propose a more concrete definition of scenario, compared to what is known to the authors from the literature. This is achieved by proposing an ontology in which the quantitative building blocks of a scenario are defined. An example illustrates that the presented ontology is applicable for scenario-based assessment of AVs.

We analyse perception and memory using mathematical models for knowledge graphs and tensors to gain insights in the corresponding functionalities of the human mind. Our discussion is based on the concept of propositional sentences consisting of \textit{subject-predicate-object} (SPO) triples for expressing elementary facts. SPO sentences are the basis for most natural languages but might also be important for explicit perception and declarative memories, as well as intra-brain communication and the ability to argue and reason. A set of SPO sentences can be described as a knowledge graph, which can be transformed into an adjacency tensor. We introduce tensor models, where concepts have dual representations as indices and associated embeddings, two constructs we believe are essential for the understanding of implicit and explicit perception and memory in the brain. We argue that a biological realization of perception and memory imposes constraints on information processing. In particular, we propose that explicit perception and declarative memories require a semantic decoder, which, in a simple realization, is based on four layers: First, a sensory memory layer, as a buffer for sensory input, second, an index layer representing concepts, third, a memoryless representation layer for the broadcasting of information and fourth, a working memory layer as a processing center and data buffer. In a Bayesian brain interpretation, semantic memory defines the prior for triple statements. We propose that, in evolution and during development, semantic memory, episodic memory and natural language evolved as emergent properties in the agents' process to gain deeper understanding of sensory information. We present a concrete model realization and validate some aspects of our proposed model on benchmark data where we demonstrate state-of-the-art performance.

Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information. This structure can be defined by ontology and augmented in the classifier as a form of domain knowledge. To capture such dependencies between the labels, we propose an ontology-aware neural network containing two components: feed-forward ontology layers and graph convolutional networks (GCN). The feed-forward ontology layers capture the intra-dependencies of labels between different levels of ontology. On the other hand, GCN mainly models inter-dependency structure of labels within an ontology level. The framework is evaluated on two benchmark datasets for single-label and multi-label audio event classification tasks. The results demonstrate the proposed solutions efficacy to capture and explore the ontology relations and improve the classification performance.

Primary obligations include obtaining explicit consent from the data subject for the processing of personal data and providing full transparency with respect to processing and sharing. With the coming into effect of the GDPR in May 2018, several tools [11, 16, 19] have recently been developed that can be used to assist companies to assess the compliance of their systems and processes with respect to obligations set forth in the GDPR. However, such tools are targeted at self assessment (i.e. companies complete standard questionnaires in the form of a privacy impact assessment) and cannot be used to automatically check compliance with usage constraints. Such, automated transparency and compliance mechanisms would require not only machine-readable representations of the users consent, but also machine-readable representations of data processing and sharing. SPECIAL 1 is an EU H2020 research and innovation action, which addresses these challenges by demonstrating how Semantic Web technologies can be used for both consent and personal data processing representation and compliance checking. In particular we devise a suite of ontologies and vocabularies that can be used to: (i) model data usage policies, conforming the SPECIAL's Usage Policy Language, (ii) represent data processing and sharing events in a semantic log. Both of which have been developed in close collaboration with legal experts, thus ensuring that our automated compliance checking is tightly coupled with the legal assessment process.1 https://www.specialprivacy.eu/ 1 arXiv:2001.09461v1