Search

Atom interferometers covering macroscopic domains of space-time are a spectacular manifestation of the wave nature of matter. Because of their unique coherence properties, Bose-Einstein condensates are ideal sources for an atom interferometer in extended free fall. In this Letter we report on the realization of an asymmetric Mach-Zehnder interferometer operated with a Bose-Einstein condensate in microgravity. The resulting interference pattern is similar to the one in the far field of a double slit and shows a linear scaling with the time the wave packets expand. We employ delta-kick cooling in order to enhance the signal and extend our atom interferometer. Our experiments demonstrate the high potential of interferometers operated with quantum gases for probing the fundamental concepts of quantum mechanics and general relativity.

We adapt existing approaches for privacy-preserving publishing of linked data to a setting where the data are given as Description Logic (DL) ABoxes with possibly anonymised (formally: existentially quantified) individuals and the privacy policies are expressed using sets of concepts of the DL. We provide a chacterization of compliance of such ABoxes w.r.t. policies, and show how optimal compliant anonymisations of ABoxes that are non-compliant can be computed. This work extends previous work on privacy-preserving ontology publishing, in which a very restricted form of ABoxes, called instance stores, had been considered, but restricts the attention to compliance. The approach developed here can easily be adapted to the problem of computing optimal repairs of quantified ABoxes.

Knowledge Graphs (KGs) are typically large data-first knowl- edge bases with weak inference rules and weakly-constraining data schemes. The SHACL Shapes Constraint Language is a W3C recommendation for the expression of shapes as constraints on graph data. SHACL shapes serve to validate a KG and can give informative insight into the structure of data. Here, we introduce Inverse Open Path (IOP) rules, a logical for- malism which acts as a building block for a restricted fragment of SHACL that can be used for schema-driven structural knowledge graph validation and completion. We define quality measures for IOP rules and propose a novel method to learn them, SHACLearner. SHACLearner adapts a state-of-the-art embedding-based open path rule learner (oprl) by modifying the efficient matrix-based evaluation module. We demonstrate SHACLearner performance on real-world massive KGs, YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts), finding that it can efficiently learn hundreds of high-quality rules.

This manuscript introduces J2RM: a tool to process mappings from JSON data to RDF triples guided by an OWL2 ontology structure. The mappings are defined as annotation properties associated with each ontology entity of interest. They are embedded in an ontology file so that they can be readily deployed and shared to automate RDF-graph creation. In this paper, we motivate the need for such mappings, describe some of their definitions on a use case example, present the formal grammar of the mapping language, and explain how these mappings work. We conclude with a discussion of the key aspects, main contributions, and future improvements.

Gesture-controlled interfaces are becoming increasingly popular with the growing use of Internet of Things (IoT) systems. In particular, in automobiles, smart homes, computer games and Augmented Reality (AR)/Virtual Reality (VR) applications, gestures have become prevalent due to their accessibility to everyone. Designers, producers, and vendors integrating gesture interfaces into their products have also increased in numbers, giving rise to a greater variation of standards in utilizing them. This variety can confuse a user who is accustomed to a set of conventional controls and has their own preferences. The only option for a user is to adjust to the system even when the provided gestures are not intuitive and contrary to a user's expectations. This paper addresses the problem of the absence of a systematic analysis and description of gestures and develops an ontology which formally describes gestures used in Human Device Interactions (HDI). The presented ontology is based on Semantic Web standards (RDF, RDFS and OWL2). It is capable of describing a human gesture semantically, along with relevant mappings to affordances and user/device contexts, in an extensible way.

Knowledge graphs (KGs) proliferating on theWeb are known to be incomplete. Much research has been proposed for automatic com- pletion, sometimes by rule learning, that scales well. All existing methods learn closed rules. Here we introduce open path (OP) rules and present a novel algorithm, oprl, for learning them. While closed rules are used to complete a KG by answering given queries, OP rules identify the incom- pleteness of a KG by inducing such queries to ask. We use adaptations of Freebase, YAGO2, and a synthetic but complete Poker KG to evaluate oprl. We find that oprl mines hundreds of accurate rules from massive KGs with up to 1M facts. The learnt OP rules induce queries with preci- sion up to 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion.

Ontologies expressed in OWL and their associated SHACL [4] constraints contain detailed metadata and assumptions on Knowledge Graphs (KGs). With this information comes the possibility of developing front-end applications that interact with these KGs. These opportunities are often unrealised as Web developers are required to interpret OWL and SHACL statements in order to write front-end code that conforms with the back-end data model. This paper introduces on2ts, a developer tool that automatically converts OWL definitions and SHACL constraints into Typescript [1] classes and interfaces. This enables developers to import these definitions directly into their application. The authors have developed this tool with the goal of reducing development time of linked-data applications, improving type-safety of linked-data applications and providing an appropriate level of abstraction for front-end development environments.

Knowledge graph creation and maintenance is difficult for naive users. One barrier is the paucity of user friendly publishing tools that separate schema modeling from instance data creation. The Shapes Constraint Language (SHACL)[12], a W3C standard for validating RDF based knowledge graphs, can help. SHACL enables domain relevant structure, expressed as a set of shapes, to constrain knowledge graphs. This paper introduces Schimatos, a form based Web application with which users can create and edit RDF data constrained and validated by shapes. Forms themselves are generated from, and stored as, shapes. In addition, Schimatos, can be used to edit shapes, and hence forms. Thus, Schimatos enables end-users to create and edit complex graphs abstracted in an easy-to-use custom graphical user interface with validation procedures to mitigate the risk of errors. This paper presents the architecture of Schimatos, defines the algorithm that builds Web forms from shape graphs, and details the workflows for SHACL creation and data-entry. Schimatos is illustrated by application to Wikidata.

Scientific knowledge has been traditionally disseminated and preserved through research articles published in journals, conference proceedings, and online archives. However, this article-centric paradigm has been often criticized for not allowing to automatically process, categorize, and reason on this knowledge. An alternative vision is to generate a semantically rich and interlinked description of the content of research publications. In this paper, we present the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities. AI-KG includes about 14M RDF triples and 1.2M reified statements extracted from 333K research publications in the field of AI, and describes 5 types of entities (tasks, methods, metrics, materials, others) linked by 27 relations. AI-KG has been designed to support a variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and helping to inform decision-making in funding bodies and research policymakers. AI-KG has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier. It then integrates and filters the resulting triples using a combination of deep learning and semantic technologies in order to produce a high-quality knowledge graph. This pipeline was evaluated on a manually crafted gold standard, yielding competitive results. AI-KG is available under CC BY 4.0 and can be downloaded as a dump or queried via a SPARQL endpoint.

Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a ground truth. The SemTab dataset is useful to have a general understanding of how these algorithms work, and the organizers of the challenge included some artificial noise to the data to make the annotation trickier. However, it is hard to analyze specific aspects in an automatic way. For example, the ambiguity of names at the entity-level can largely affect the quality of the annotation. In this paper, we propose a novel dataset to complement the datasets proposed by SemTab. The dataset consists of a set of high-quality manually-curated tables with non-obviously linkable cells, i.e., where values are ambiguous names, typos, and misspelled entity names not appearing in the current version of the SemTab dataset. These challenges are particularly relevant for the ingestion of structured legacy sources into existing knowledge graphs. Evaluations run on this dataset show that ambiguity is a key problem for entity linking algorithms and encourage a promising direction for future work in the field.

We introduce DLR+, an extension of the n-ary propositionally closed description logic DLR to deal with attribute-labelled tuples (generalising the positional notation), projections of relations, and global and local objectification of relations, able to express inclusion, functional, key, and external uniqueness dependencies. The logic is equipped with both TBox and ABox axioms. We show how a simple syntactic restriction on the appearance of projections sharing common attributes in a DLR+ knowledge base makes reasoning in the language decidable with the same computational complexity as DLR. The obtained DLR± n-ary description logic is able to encode more thoroughly conceptual data models such as EER, UML, and ORM.