In the field of machine learning, data understanding is the practice of getting initial insights in unknown datasets. Such knowledge-intensive tasks require a lot of documentation, which is necessary for data scientists to grasp the meaning of the data. Usually, documentation is separate from the data in various external documents, diagrams, spreadsheets and tools which causes considerable look up overhead. Moreover, other supporting applications are not able to consume and utilize such unstructured data. That is why we propose a methodology that uses a single semantic model that interlinks data with its documentation. Hence, data scientists are able to directly look up the connected information about the data by simply following links. Equally, they can browse the documentation which always refers to the data. Furthermore, the model can be used by other approaches providing additional support, like searching, comparing, integrating or visualizing data. To showcase our approach we also demonstrate an early prototype.

The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the "intelligent enterprise", in other words, one which is to some extent "self-describing" and able to adapt to organisational change. However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools.

GraphDB supports inference out of the box and provides updates to inferred facts automatically. Facts change all the time and the amount of resources it would take to manually manage updates or rerun the inferencing process would be overwhelming without this capability. This results in improved query speed, data availability and accurate analysis. GraphDB will use the data and the rules to infer more facts and thus produce a richer data set than the one you started with. GraphDB can be configured via "rule-sets" – sets of axiomatic triples and entailment rules – that determine the applied semantics.

We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space and find that learning embeddings in the Lorentz model is substantially more efficient than in the Poincar\'e-ball model. We show that the proposed approach allows us to learn high-quality embeddings of large taxonomies which yield improvements over Poincar\'e embeddings, especially in low dimensions. Lastly, we apply our model to discover hierarchies in two real-world datasets: we show that an embedding in hyperbolic space can reveal important aspects of a company's organizational structure as well as reveal historical relationships between language families.

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time. The usage of open standards for data and knowledge models eliminates proprietary vendor lock-in.

Our state of mind is based on experiences and what other people tell us. This may result in conflicting information, uncertainty, and alternative facts. We present a robot that models relativity of knowledge and perception within social interaction following principles of the theory of mind. We utilized vision and speech capabilities on a Pepper robot to build an interaction model that stores the interpretations of perceptions and conversations in combination with provenance on its sources. The robot learns directly from what people tell it, possibly in relation to its perception. We demonstrate how the robot's communication is driven by hunger to acquire more knowledge from and on people and objects, to resolve uncertainties and conflicts, and to share awareness of the per- ceived environment. Likewise, the robot can make reference to the world and its knowledge about the world and the encounters with people that yielded this knowledge.

While the use of knowledge graphs has exploded in recent years, there exist few tools and mechanisms for users to explore, query and manage semantic data in knowledge graphs. A lack of user-friendly tools to construct SPARQL queries has been a barrier preventing the wide adoption of Semantic Web technologies by domain experts and application developers alike. There are a variety of tools and techniques to convert CSV and relational data to RDF, but to the best of our knowledge there is no integrated, user-friendly toolkit for data triplification and ingestion into a triple store. In this paper, we present the Semantics Toolkit (SemTK), an open source project that allows user-friendly querying and semantic data management. Through its user interface, SemTK allows users to convert CSV data into RDF triples and ingest them into a triple store. It also allows users to visually explore the ontology and construct SPARQL queries via a drag-and-drop interface. SemTK also provides novel SQL stored procedurelike support for saving and executing semantic queries with run-time constraints. Additionally, SemTK provides REST APIs for all of its functionality including allowing data ingestion and queries to be executed programmatically, dramatically simplifying the deployment of knowledge-driven applications. The Semantics Toolkit is open-sourced under the Apache License, Version 2.0 and is available at https://github.com/ge-semtk/semtk.

This paper presents Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet, which is inspired by the theory of Conceptual Metaphor. Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology design framework to deal with both semiotic and referential aspects of frames, roles, mappings, and eventually blending. The description of the resource is supplied by a discussion of its applications, with examples taken from metaphor generation, and the referential problems of metaphoric mappings. Both schema and data are available from the Framester SPARQL endpoint.

The Resource Description Framework, more commonly known as RDF, is a graph data model that formally describes the semantics, or meaning of information. It also represents metadata, that is, data about data. These triples are based on an Entity Attribute Value (EAV) model, in which the subject is the entity, the predicate is the attribute, and the object is the value. Each triple has a unique identifier known as the Uniform Resource Identifier, or URI. The parts of a triple, the subject, predicate, and object, represent links in a graph.

SPARQL is a SQL-like query language for RDF data. SPARQL queries can produce result sets that are tabular or RDF graphs depending on the kind of query used. Let's use SPARQL, the query language for RDF graphs, to create a graph. First, define prefixes to URIs with the PREFIX keyword. In the example below, we set bedrock as the default namespace for the query.