This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. Keywords: Mapping languages · Legal Knowledge Graphs · Legal semantic model 1 Introduction The body of law to which citizens and businesses have to adhere is constantly increasing in volume and complexity [2]. The information contained in such a body of law is usually provided by unstructured text within legal documents, for which a number of systems have been developed. The information made available by such legal information systems, however, is often accessed with simple, keyword-based search interfaces and presented as a simple list of hits [7].

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo development.

An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments. In order to deal with increasingly large and dynamic environments, and to quickly react to emergencies, tools are needed to simplify the process of translating high-level policies into executable queries and rules. We present a framework to produce such tools, which uses rules to aggregate low-level sensor data, described using the Semantic Sensor Network Ontology, into more useful and actionable abstractions. Using the schema of the underlying data sources as an input, we automatically generate abstractions which are relevant to the use case at hand. In this demonstration we present a policy editor tool and a simulation on which policies can be tested.

Leveraging standard HLS (High Level Synthesis) tools from FPGA vendors, SLX FPGA tackles the challenges associated with the HLS design flow. In this paper, the results of an SLX FPGA-optimized implementation of a Secure Hash Algorithm (SHA-3; also known as Keccak) are compared to a competition-winning hand-optimized HLS implementation of the same algorithm. SLX provides a nearly 400x speed-up over the unoptimized implementation and even outperforms the hand-optimized implementation by 14%. Moreover, it is also more resource efficient, consuming nearly 3.6 times less look-up tables and 1.76 times less flip-flops. Click here to read more.

Please join the Ontology team as they tour the United States presenting their solution for a public blockchain & distributed collaboration platform. They will discuss their unique viewpoint on developing blockchain technology in China & its impact throughout the space. He is one of the first Semantic Web experts in China & has many years of experience in enterprise resource planning, digitization of government affairs, gaming platforms, & media streaming. In 2008, he joined Project Halo, a project initiated by Paul Allen, Co-Founder of Microsoft, where he worked on big data & artificial intelligence. In 2013, Hu helped set up leading fintech company Green Dot's subsidiary in China, where he developed a thorough understanding of the financial system & credit card business.

ARGO is short for the Amazon Rainforest Genome Ontology project. The mission is to tap into the scientific, biotechnological, and medical potential of the plant biodiversity in the Amazon rainforest in order to discover, utilise and preserve their biological value … before it is too late. If you want to follow our progress, please signup below.

This paper focuses on the problem of unsupervised alignment of hierarchical data such as ontologies or lexical databases. This is a problem that appears across areas, from natural language processing to bioinformatics, and is typically solved by appeal to outside knowledge bases and label-textual similarity. In contrast, we approach the problem from a purely geometric perspective: given only a vector-space representation of the items in the two hierarchies, we seek to infer correspondences across them. Our work derives from and interweaves hyperbolic-space representations for hierarchical data, on one hand, and unsupervised word-alignment methods, on the other. We first provide a set of negative results showing how and why Euclidean methods fail in this hyperbolic setting. We then propose a novel approach based on optimal transport over hyperbolic spaces, and show that it outperforms standard embedding alignment techniques in various experiments on cross-lingual WordNet alignment and ontology matching tasks.

OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way, and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational Logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed. Under consideration in Theory and Practice of Logic Programming (TPLP) KEYWORDS: Prolog; Ontologies; Multiagent Systems; Visual Languages; Scene Interpretation1 Introduction Human perception of complex visual scenes has been studied for a long time in psychology and neuroscience (Kondo et al. 2017): according to the seminal work on "high-level scene perception" (Henderson and Hollingworth 1999), besides low-level or early vision, concerned with extraction of physical properties such as depth, color, and texture from an image (Marr 1982), and intermediate-level vision, concerned with extraction of shape and spatial relations that can be determined without regard to meaning (Ullman 1996), a further level of vision is required to perceive and understand a scene: high-level vision concerns the mapping from visual representations to meaning and includes [...] the identification of objects and scenes. In their recent studies, Kveraga, Bar, and Baldassano (Kveraga and Bar 2014; Baldassano 2015) demonstrate that the brain has regions related to higher-order properties like overall geometry, arXiv:1911.04863v1

Since this article is about libraries, we'll not make any introduction to AI itself. Additionally, theoretical background of AI is necessary in order to use libraries presented in this article. AI is a very wide field, so we will be focusing on the most popular fields today like Natural Language Processing, Machine Learning, Neural Networks and more. In the end, we'll mention few interesting AI challenges where you can practice your understanding of AI. Apache Jena is an open source Java framework for building semantic web and linked data applications from RDF data. The official website provides a detailed tutorial on how to use this framework with a quick introduction to RDF specification.

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.