HealthAid: Extracting domain targeted high precision procedural knowledge from on-line communities |
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| Institution: | 1. 710071 School of Computer Science and Technology, Xidian University, Xi''an, PR China;2. 19 Ambo University, Ambo, Ethiopia;1. The Hong Kong Polytechnic University, Hong Kong, China;2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China;3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;4. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China;5. Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China;1. Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin, China;2. Institute of AI, Shandong Computer Science Center(National Supercomputer Center in Jinan), QILU University of Technology, China;1. Xianyang Vocational Technical College, Xianyang, P. R. China;2. China Electric Power Research Institute, Beijing, P. R. China;3. GuiZhou University, Guizhou Provincial Key Laboratory of Public Big Data, Guiyang, P. R. China;4. State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an, P. R. China;5. Pedagogical University of Krakow, Podchorazych 2 St., 30-084 Kraków, Poland;1. School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China;2. School of Computer and Information Engineering, Hubei Normal University, Huangshi 435002, China;3. School of Computer, Central China Normal University Wuhan 430079, China;5. National Language Resources Monitor & Research Center for Network Media, Wuhan 430079, China |
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| Abstract: | Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that ‘how-to queries’ make up a significant amount of users’ queries. Unfortunately, these kinds of knowledge are missing from most knowledge graphs and commonsense knowledge bases in use. To empower semantic search, and other intelligent applications, computers need a much broader understanding of the world properties of everyday objects, human activities, and more. Luckily, such knowledge is abundantly available on-line and can be accessed from how-to communities. One domain of interest by on-line communities is the health domain, whereby users usually seek home remedies to common health-related issues. An example of such queries might be ‘how to stop nausea using acupressure’ or ‘how to aid digestion naturally’. To answer such questions, we need systems that understand natural language and knowledge bases with task frames of solutions in a holistic approach, including the tools required, the agents involved, and the temporal order of the actions. Our goal is to construct a machine-readable domain targeted high precision procedural knowledge base containing task frames. We developed a pipeline of methods leveraging open information extraction tool to extract procedural knowledge by tapping into on-line communities. Also, we devised a mechanism to canonicalize the task frames into clusters based on the similarity of the problems they intend to solve. The resulting know-how knowledge base, HealthAidKB, consists of more than 71 K task frames which are structured hierarchically and categorically; and can be used in many applications such as semantic search, digital personal assistants, human-computer dialog and computer vision. A comprehensive evaluation of our knowledge base shows high accuracy. |
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