Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies.

The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools. In 2018, we have started the Knowledge Graph track, whose goal is to evaluate the simultaneous matching of entities and schemas of large-scale knowledge graphs. In this paper, we discuss the design of the track and two different strategies of gold standard creation. We analyze results and experiences obtained in first editions of the track, and, by revealing a hidden task, we show that all tools submitted to the track (and probably also to other tracks) suffer from a bias which we name the golden hammer bias.

Knowledge graphs have proven to be extremely useful in powering diverse applications in semantic search, natural language understanding, and even image classification. Graph4Code attempts to build well structured knowledge graphs about program code to similarly revolutionize diverse applications such as code search, code understanding, refactoring, bug detection, and code automation. We build such a graph by applying a set of generic code analysis techniques to Python code on the web. Since use of popular Python modules is ubiquitous in code, calls to functions in Python modules serve as key nodes of the knowledge graph. The edges in the graph are based on 1) function usage in the wild (e.g., which other function tends to call this one, or which function tends to precede this one, as gleaned from program analysis), 2) documentation about the function (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow), and 3) program specific features such as class hierarchies. We use the Whyis knowledge graph management framework to make the graph easily extensible. We apply these techniques to 1.3M Python files drawn from GitHub, and associated documentation on the web for over 400 popular libraries, as well as StackOverflow posts about the same set of libraries. This knowledge graph will be made available soon to the larger community for use.

In the expanding Semantic Web, an increasing number of sources of data and knowledge are accessible by human and software agents. Sources may differ in granularity or completeness, and thus be complementary. Consequently, unlocking the full potential of the available knowledge requires combining them. To this aim, we define the task of knowledge reconciliation, which consists in identifying, within and across sources, equivalent, more specific, or similar units. This task can be challenging since knowledge units are heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we propose a rule-based methodology for the reconciliation of $n$-ary relations. To alleviate the heterogeneity in representation, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by reconciling 50,435 $n$-ary relations from four different real-world sources, which highlighted noteworthy agreements and discrepancies within and across sources.

The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations. While distance functions for propositional representations have been thoroughly studied, work on distance functions for structured representations, such as graphs, frames or logical clauses, has been carried out in different communities and is much less understood. Specifically, a significant amount of work that requires the use of a distance or similarity function for structured representations of data usually employs ad-hoc functions for specific applications. Therefore, the goal of this paper is to provide an overview of this work to identify connections between the work carried out in different areas and point out directions for future work.

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%.