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Even though the advent of the Web coupled with powerful search engines has empowered the knowledge workers to quickly find the needed information, it still is a time-consuming operation. Presently there are no readily available tools that can create and maintain an up-to-date personal knowledge base that can be readily consulted when needed. While organizing the entire Web as a semantic network is a long-term goal, creation of a semantic network of personal knowledge sources that are continuously updated by crawlers and other devices is an attainable task. We created an app titled ExperTwin, that collects personally relevant knowledge units (known as JANs) from the Web, Email correspondence, and locally stored files, organize them as a semantic network that can be easily queried and visualized in many formats - just in time - when performing a knowledge-based task. The architecture of ExperTwin is based on the model of a “Society of Intelligent Agents”, where each agent is responsible for a specific task. Collection of JANs from multiple sources, establishing the relevancy, and creation of the personal semantic network are some of the many tasks performed by the individual agents. Tensorflow and Natural Language Processing (NLP) tools have been implemented to let ExperTwin learn from users. Document the design and deployment of ExperTwin as a “Knowledge Advantage Machine” able to search for relevant information while performing a knowledge-based task, is the main goal of the research presented in this paper. |
