"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.
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.
Is it a Fruit, an Apple or a Granny Smith? Abstract The "basic level", according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels. We argue that applications that use concept hierarchies - such as knowledge graphs, ontologies or taxonomies - could significantly improve their user interfaces if they'knew' which concepts are the basic level concepts. This paper examines to what extent the basic level can be learned from data. We test the utility of three types of concept features, that were inspired by the basic level theory: lexical features, structural features and frequency features. We evaluate our approach on WordNet, and create a training set of manually labelled examples that includes concepts from different domains. Our findings include that the basic level concepts can be accurately identified within one domain. Concepts that are difficult to label for humans are also harder to classify automatically. Our experiments provide insight into how classification performance across domains could be improved, which is necessary for identification of basic level concepts on a larger scale. 1 Introduction One of the ongoing challenges in Artificial Intelligence is to explicitly describe the world in ways that machines can process. This has resulted in taxonomies, thesauri, ontologies and more recently knowledge graphs. While these various knowledge organization systems (KOSs) may use different formal languages, they all share similar underlying data representations.
Distributed data mining (DDM) deals with the problem of finding patterns or models, called knowledge, in an environment with distributed data and computations. Today, a massive amounts of data which are often geographically distributed and owned by different organisation are being mined. As consequence, a large mount of knowledge are being produced. This causes problems of not only knowledge management but also visualization in data mining. Besides, the main aim of DDM is to exploit fully the benefit of distributed data analysis while minimising the communication. Existing DDM techniques perform partial analysis of local data at individual sites and then generate a global model by aggregating these local results. These two steps are not independent since naive approaches to local analysis may produce an incorrect and ambiguous global data model. The integrating and cooperating of these two steps need an effective knowledge management, concretely an efficient map of knowledge in order to take the advantage of mined knowledge to guide mining the data. In this paper, we present "knowledge map", a representation of knowledge about mined knowledge. This new approach aims to manage efficiently mined knowledge in large scale distributed platform such as Grid. This knowledge map is used to facilitate not only the visualization, evaluation of mining results but also the coordinating of local mining process and existing knowledge to increase the accuracy of final model.
How the new advances in semantics can help us be better at Machine Learning. Deep learning on graphs is taking more importance by the day. I've been talking about the data fabric in general, and giving some concepts of Machine Learning and Deep Learning in the data fabric. The Data Fabric is the platform that supports all the data in the company. How it's managed, described, combined and universally accessed.