ExperTwin: An Alter Ego in Cyberspace for Knowledge Workers
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The document introduces Expertwin, a knowledge advantage machine designed to enhance the workspace of knowledge workers by providing timely and contextual information retrieval capabilities. It utilizes machine learning and natural language processing to index and classify knowledge from various sources, streamlining the collection and organization of relevant information. Expertwin aims to reduce productivity friction and stress by integrating a visualization component for improved data analysis and accessibility.
ExperTwin: An Alter Ego in Cyberspace for Knowledge Workers
- 1. ExperTwin: An Alter Ego in Cyberspace for Knowledge Workers C. Toxtli, C. Flores-Saviaga, M. Maurier, A. Ribot, T. Bankole, A. Entrekin, M. Cantley, S. Singh, S. Reddy, R. Reddy
- 2. Problem statement Knowledge workers (i.e. news writers, researchers) are benefited from having the right information (i.e. in context), in time (i.e. auto suggestions) and in place (i.e. in their workspace). Querying and filtering multi-domain knowledge bases (i.e. Google) are time consuming tasks. The collected information is usually moved to the workspace and the friction of switching contexts cause interruptions and add up to reduced productivity and increased stress (Czerwinski 2000, Iqbal 2007, Mark 2008)
- 3. Example Imagine that you are writing an article about the relation of the United States government to the North Korea government. Maybe you need to know: ● What are the last actions from North Korea (query focused in North Korea) ● What is the United States government expecting (query focused in U.S.) ● How previous agreements had evolved (query ordered by time) Then you collect, organize and cite the found information.
- 4. Solution - ExperTwin In order to empower knowledge workers to be able to get opportune in-context information in their workspace, we present ExperTwin, a Knowledge Advantage Machine (KAM) capable to manage personal semantic networks.
- 5. Goal The purpose of this research is to envision how a knowledge worker workspace can be enhanced by applying Knowledge Advantage Machine frameworks such as Vijjana (Makineni 2015).
- 6. Terminology Knowledge Advantage (KA): Just as Mechanical Advantage played a key role in the industrial era, the concept of Knowledge Advantage could be applied to deal with the information explosion problem, and it is defined as the ratio of time it takes to accomplish a knowledge based task to amount of time it takes to search for the relevant knowledge. Knowledge Advantage Machine (KAM): Any machine (or an app) that increases the KA may be thought of as a KAM. Knowledge Unit (KU): referred in this paper as JANs. Knowledge Object that contains all the metadata of each content.
- 7. ExperTwin - Components ● Knowledge Discovery ● Learning Agent ● Visualization
- 8. Knowledge Discovery ExperTwin indexes the knowledge from web sources, local sources, web feeds and email. ExperTwin crawlers constantly updates the Knowledge Base from these sources.
- 9. Knowledge Discovery - Multiple sources
- 10. Learning Agent - Natural Language Processing Purpose: Keyword extraction will, with a degree of accuracy, tell what the purpose of many articles are. From aiding in determining relevance to user preferences. Keyword Extraction 1. Text to obtain keywords from 2. Number of keywords wanted 3. Title of text if obtainable Dictionary of Keywords with weights. Perform NLP with NLTK and RAKE_NLTK libraries
- 11. Learning Agent - Machine Learning According to the user preference of a content over different contexts, the classifier give an extra weight to each content. Preprocessing 1. Run through the database 2. Generate keywords for every JAN in database 3. Define user defined keywords 4. Label article as class 1/class 2 based on the results of step 3 5. Collect master document Tensorflo w
- 12. Learning Agent - Machine Learning 1. CPU based tensorflow® 2. Learn vocabulary and term document matrix with scikit learn 3. relU + sigmoid activation functions wt 50% dropout 4. Train with 70% of data 5. 87% test accuracy Training https://goo.gl/aRXEbp Tensorflo w
- 13. Learning Agent - Machine Learning 1. Load saved neural network architecture 2. Query the database for unclassified JANs 3. Retrieve content & transform to document term matrix 4. Make predictions 5. Update database Testing/Processing https://goo.gl/9q5azK 1. CPU based tensorflow® 2. Learn vocabulary and term document matrix with scikit learn 3. relU + sigmoid activation functions wt 50% dropout 4. Train with 70% of data 5. 87% test accuracy TrainingPreprocessing 1. Run through the database 2. Generate keywords for every JAN in database 3. Define user defined keywords 4. Label article as class 1/class 2 based on the results of step 3 5. Collect master document
- 14. Learning Agent - GraphDB The semantic network is stored in a graph database by linking the keywords to the JANs and assigning different weights. ● Each twin has a meta-knowledge base ● Stores its biases and reasoning for relating data ● Self-representing (see image) ● Allows us to rank articles by relevance in real time ● Searchable
- 15. Architecture
- 16. Visualization ● Work area ● Content suggestions ● Content explorer ○ 2D & 3D visualizations ○ Graph representation
- 17. Visualization - Work Area
- 18. Visualization - Work area ● Need login (through Google Sign-In with a gmail address) ● Many users can use the interface at the same time ● Users need to set up interest keywords (add/delete) ● Keywords associated with user listed ● Users can pick keywords in dropdown or search to start browsing
- 19. Visualization - Work area ● Context choice: Research / Professional / Study / Social / Others ● Will help in the choice/ranking of the articles ● Drag and Drop: to add files or folder to the database ● Help: ○ To send articles (url) to database through an email inbox@aiwvu.ml ○ To download Chrome Extension to add articles to database
- 20. Visualization - Content suggestions From user search, get ten best ranked articles ● Thumbnail (if any) ● Title of article ● Date of publication ● Article clickable for a preview
- 21. Visualization - Content suggestions Each article listed can be open in preview: ● Title ● Date of publication ● Source ● Full content ● User rating
- 22. Visualization - Content explorer 7 8 7.- Switch to Graph view 8.- Graph view
- 23. 2D & 3D visualizations A search -> list of articles 4 types of 3D representations available: ● Table ● Sphere ● Helix ● Grid
- 24. Graph visualizations Articles and their relationship available in Graph 3D representation Populated by a user search Each article = node Link = keyword shared by nodes
- 25. Virtual Reality visualization By using a VR Headset, ExperTwin let users get immerse into the content.
- 26. ● This work only focuses in how a Knowledge Advantage Machine frameworks can be applied to implement an enhanced workspace for knowledge workers. ● Evaluations should be performed to determine how much this tool can help information workers to improve their work by being assisted by ExperTwin. Limitations
- 27. Conclusions We propose ExperTwin a Knowledge Advantage Machine that enhances the knowledge worker workspace by adding in-context information retrieval capabilities and information analysis visualizations to improve knowledge based tasks.