"An ontology defines the terms used to describe and represent an area of knowledge. … Ontologies include computer-usable definitions of basic concepts in the domain and the relationships among them."
– from OWL Web Ontology Language Use Cases and Requirements. W3C Recommendation (10 February 2004). Jeff Heflin, editor.
How Real World Ontology can help us in the #DataScience World of #AITechnology? Ontology encompasses problems about the most general properties and relations of the entities which do exist. Ontology is the way we can connect entities and understand their relationships, their types and tokens. With ontology one can enable such a description, but first we need to formally specify components such as individuals (tokens, instances of objects), classes (types), attributes (properties) and relations as well as limitations and restrictions, rules and axioms. Formal ontology gives precise mathematical formulations of the properties and relations of certain entities.
We are seeking talented problem solvers and innovative thinkers to deliver cutting-edge research in the exploitation of information and intelligence to enhance future UK defence and security capability. In the role, you will encounter a variety of challenges, sometimes requiring a high-level view of capability and sometimes in the detail of an application of a technology. You will work closely with our teams and our clients: • defining research tasks to achieve research goals, • delivering research and developing prototypes, and • coordinating and overseeing research delivery by subcontractors. You will be an experienced self-starter, with a technical background in a relevant area, such as knowledge graphs, ontology development, machine learning, natural language processing, data science, intelligence fusion and/or command and control. You will pride yourself on working closely with our own teams, our clients and our suppliers to deliver the best outcomes.
As we move closer to the year 2023, the concept of Web 3.0 is gaining more and more attention. Also known as the "Semantic Web," Web 3.0 represents the next generation of the Internet, and has the potential to revolutionize how we interact with and use the web. At its core, the Semantic Web is about adding meaning and context to the vast amount of data that is available online. It does this through the use of semantic technologies, which enable computers to understand the meaning and context of data, rather than just its raw form. One way that the Semantic Web achieves this is through the use of semantic markup languages, such as RDF (Resource Description Framework) and OWL (Web Ontology Language).
Aiming at ontology-based data access to temporal data, we design two-dimensional temporal ontology and query languages by combining logics from the (extended) DL-Lite family with linear temporal logic LTL over discrete time (Z,<). Our main concern is first-order rewritability of ontology-mediated queries (OMQs) that consist of a 2D ontology and a positive temporal instance query. Our target languages for FO-rewritings are two-sorted FO(<)—first-order logic with sorts for time instants ordered by the built-in precedence relation < and for the domain of individuals—its extension FO(<,≡) with the standard congruence predicates t ≡ 0 (mod n), for any fixed n > 1, and FO(RPR) that admits relational primitive recursion. In terms of circuit complexity, FO(<,≡)- and FO(RPR)-rewritability guarantee answering OMQs in uniform AC0 and NC1, respectively. We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL ontology languages and investigate the FO-rewritability of OMQs with atomic queries by constructing projections onto 1D LTL OMQs and employing recent results on the FO-rewritability of propositional LTL OMQs. As the projections involve deciding consistency of ontologies and data, we also consider the consistency problem for our languages. While the undecidability of consistency for 2D ontology languages with expressive Boolean role inclusions might be expected, we also show that, rather surprisingly, the restriction to Krom and Horn role inclusions leads to decidability (and ExpSpace-completeness), even if one admits full Booleans on concepts. As a final step, we lift some of the rewritability results for atomic OMQs to OMQs with expressive positive temporal instance queries. The lifting results are based on an in-depth study of the canonical models and only concern Horn ontologies.
Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database team. Earlier, he was a Managing Director at State Street, heading their efforts to adopt ontologies and graph databases. Before that, he worked as a technology architect at Pegasystems, as an architect and technology strategist at Nokia Location & Commerce (aka HERE), and prior to that he was a Research Fellow at the Nokia Research Center Cambridge. He was an elected member of the Advisory Board of the World Wide Web Consortium (W3C) in 1998-2013, and represented Nokia in the W3C Advisory Committee in 1998-2002. In 1996-1997 he was a Visiting Scientist at MIT Laboratory for Computer Science, working with W3C and launching the Resource Description Framework (RDF) standard; he served as a co-editor of the RDF Model and Syntax specification.
Python is an interpreted, object-oriented programming language. Despite it's popularity, it's often accused of being slow. In this course you will learn how to optimize the performance of your Python code. You will learn various tricks to reduce execution time. A lot of people have different definitions of performance.
As with many fields, knowledge graphs boast a wide array of specialized terms. This guide provides a handy reference to these concepts. The Resource Description Framework (or RDF) is a conceptual framework established in the early 2000s by the World Wide Web Consortium for describing sets of interrelated assertions. RDF breaks down such assertions into underlying graph structures in which a subject node is connected to an object node via a predicate edge. The graph then is constructed by connecting the object nodes of one assertion to the subject nodes of another assertion, in a manner analogous to Tinker Toys (or molecular diagrams).
AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Unicode is an information #technology standard for the consistent encoding, representation, and handling of text expressed in most of the world's writing systems. The standard is maintained by the Unicode Consortium, and as of March 2020, there is a total of 143,859 characters, with Unicode 13.0 (these characters consist of 143,696 graphic characters and 163 format characters) covering 154 modern and historic scripts, as well as multiple symbol sets and emoji. The character repertoire of the Unicode Standard is synchronized with ISO/IEC 10646, and both are code-for-code identical. The Universal Coded Character Set (UCS) is a standard set of characters defined by the International Standard ISO/IEC 10646, Universal Coded Character Set (UCS), which is the basis of many character encodings, improving as characters from previously unrepresented writing systems are added. To integrate AI into computers and system software means to create a Unicode abstraction level, the Universal Coded Data Set (UCDS), as AI Unidatacode or EIS UCDS.