Domain knowledge

About: Domain knowledge is a research topic. Over the lifetime, 18369 publications have been published within this topic receiving 416605 citations.

Deep learning for healthcare: review, opportunities and challenges.

Abstract: Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

Enabling Knowledge Creation: How to Unlock the Mystery of Tacit Knowledge and Release the Power of Innovation

Abstract: When The Knowledge-Creating Company (OUP; nearly 40,000 copies sold) appeared, it was hailed as a landmark work in the field of knowledge management. Now, Enabling Knowledge Creation ventures even further into this all-important territory, showing how firms can generate and nurture ideas by using the concepts introduced in the first book. Weaving together lessons from such international leaders as Siemens, Unilever, Skandia, and Sony, along with their own first-hand consulting experiences, the authors introduce knowledge enabling-the overall set of organizational activities that promote knowledge creation-and demonstrate its power to transform an organization's knowledge into value-creating actions. They describe the five key "knowledge enablers" and outline what it takes to instill a knowledge vision, manage conversations, mobilize knowledge activists, create the right context for knowledge creation, and globalize local knowledge. The authors stress that knowledge creation must be more than the exclusive purview of one individual-or designated "knowledge" officer. Indeed, it demands new roles and responsibilities for everyone in the organization-from the elite in the executive suite to the frontline workers on the shop floor. Whether an activist, a caring expert, or a corporate epistemologist who focuses on the theory of knowledge itself, everyone in an organization has a vital role to play in making "care" an integral part of the everyday experience; in supporting, nurturing, and encouraging microcommunities of innovation and fun; and in creating a shared space where knowledge is created, exchanged, and used for sustained, competitive advantage. This much-anticipated sequel puts practical tools into the hands of managers and executives who are struggling to unleash the power of knowledge in their organization.

Knowledge discovery in databases: an overview

Abstract: After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book Knowledge Discovery in Databases were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to "AI Magazine readers of this article.

Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons

Abstract: Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).