Knowledge Engineering


Practical Approach of Knowledge Management in Medical Science

arXiv.org Artificial Intelligence

Knowledge organization, infrastructure, and knowledge-based activities are all subjects that help in the creation of business strategies for the new enterprise. In this paper, the first basics of knowledge-based systems are studied. Practical issues and challenges of Knowledge Management (KM) implementations are then illustrated. Finally, a comparison of different knowledge-based projects is presented along with abstracted information on their implementation, techniques, and results. Most of these projects are in the field of medical science. Based on our study and evaluation of different KM projects, we conclude that KM is being used in every science, industry, and business. But its importance in medical science and assisted living projects are highlighted nowadays with the most of research institutes. Most medical centers are interested in using knowledge-based services like portals and learning techniques of knowledge for their future innovations and supports.


Embedding Symbolic Knowledge into Deep Networks

Neural Information Processing Systems

In this work, we aim to leverage prior symbolic knowledge to improve the performance of deep models. We propose a graph embedding network that projects propositional formulae (and assignments) onto a manifold via an augmented Graph Convolutional Network (GCN). To generate semantically-faithful embeddings, we develop techniques to recognize node heterogeneity, and semantic regularization that incorporate structural constraints into the embedding. Experiments show that our approach improves the performance of models trained to perform entailment checking and visual relation prediction. Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.


Media Hub/Materials on "AI Governance" - Internet Governance Knowledge Repository

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"The problem is not AI per se – but that this technology is developed in a biased context around gender, race and class. We need to build systems around the values we want our present and future societies to have." "A critical analysis of AI implies a close investigation of network structures and multiple layers of computational systems. It is our responsibility as researchers, activists and experts on digital rights to provoke awareness by reflecting on possible countermeasures that come from the technological, political, and artistic framework." Did you report on this topic?


AI assisted content classification for corporate learning & knowledge base - Software Technology Blog

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There is no shortage of training content for employees. However, quick access to the right information is the challenge. Traditionally, the L&D departments spend significant time on instructor-led training and aggregating and buying third-party training content. Other learning avenues, like on-the-job training, personalized training, micro-learning, and data or event-driven training programs are equally important. Employees today learn from content spread across internal and external systems including intranets, MooC platforms, LMS, social media platforms, external training content providers, document management systems, collaboration platforms, and even forums, Q&A portals, email and messenger/ chat platforms.


Can Pretrained Language Models Replace Knowledge Bases?

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The recent rapid development of pretrained language models has produced significant performance improvements on downstream NLP tasks. These pretrained language models compile and store relational knowledge they encounter in training data, which prompted Facebook AI Research and University College London to introduce their LAMA (LAnguage Model Analysis) probe to explore the feasibility of using language models as knowledge bases. The term "knowledge base" was introduced in the 1970s. Unlike databases which store figures, tables, and other straightforward data in computer memory, a knowledge base is able to store more complex structured and unstructured information. A knowledge base system can be likened to a library that stores facts in a specific field.


Can Pretrained Language Models Replace Knowledge Bases?

#artificialintelligence

The recent rapid development of pretrained language models has produced significant performance improvements on downstream NLP tasks. These pretrained language models compile and store relational knowledge they encounter in training data, which prompted Facebook AI Research and University College London to introduce their LAMA (LAnguage Model Analysis) probe to explore the feasibility of using language models as knowledge bases. The term "knowledge base" was introduced in the 1970s. Unlike databases which store figures, tables, and other straightforward data in computer memory, a knowledge base is able to store more complex structured and unstructured information. A knowledge base system can be likened to a library that stores facts in a specific field.


Managing Support Knowledge With AI: Talla Helps Toast

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One of the great challenges in knowledge management has always been getting the right knowledge to front line workers in real time. Old-style knowledge repositories are simply too difficult to search through when a customer is waiting for an answer. I've been poking around the area of managing customer support knowledge for over two decades, and it's always been challenging--not only to get the knowledge out to the front lines, but also to get it into a system in a relatively straightforward fashion. If AI could solve this problem, it could help a lot of companies. So I was excited when I started hearing about Talla a couple of years ago--first from Rudina Seseri at Glasswing Ventures, who has funded Talla and where I'm an advisor--and then from Rob May himself, the CEO of Talla.


IIT-G develops Artificial Intelligence powered 'Smart-Engineer'

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Guwahati: To advance greater penetration of Electric Vehicles (EVs) on the nation's roads, the Indian Institute of Technology-Guwahati (IIT-G) on Saturday announced the development of an Artificial Intelligence (AI) assisted engineering system design tool called the'Smart-Engineer'. "The current version of the Smart-Engineer is able to address the fundamentals involved in the design of induction motors. The early results are very promising, and we now intend to expand the capability of Smart-Engineer to include the finer aspects of motor design," Professor Praveen Kumar, Department of Electronics and Electrical Engineering (EEE), IIT-G, said in a statement. "We are compiling the know-why of motor design that we have gathered over the years in the e-mobility lab (EML) and will use this knowledge repository, combined with IBM's AI and cloud capabilities, to make Smart-Engineer even smarter," he added. The tool is built by a team comprising PhD and Masters students, Rajendra Kumar, Bikash Sah, Ankit Vishway and Rajendra Kumar, by leveraging the IBM Watson AI Platform and IBM Cloud.


AI Knowledge Map: How To Classify AI Technologies

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I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fixed boxes is often not worth the benefits of having such a "clear" framework (this is a generalization of course as sometimes they are extremely useful). I also believe this landscape is useful for people new to the space to grasp at-a-glance the complexity and depth of this topic, as well as for those more experienced to have a reference point and to create new conversations around specific technologies. What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM).


Knowledge Engineering

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Knowledge engineering is the process of creating rules that apply to data in order to imitate the way a human thinks and approaches problems. A task and its solution are broken down to their structure, and based on that information, AI determines how the solution was reached. Often, a library of problem-solving methods and knowledge to solve a particular set of problems is fed into a system as raw data. Then, the system can diagnose the problem and find the solution without further human input. The result can be used as a self-help troubleshooting software, or as a support module to a human agent.