A Knowledge Extraction and Management Component to Support Spontaneous Participation

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• 关键词：e ; Participation ; Citizen ; led e ; Participation ; Information extraction (IE) ; Natural Language Processing (NLP) ; Public services ; e ; Government
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9249
• 期：1
• 页码：68-80
• 全文大小：1,543 KB
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• 作者单位：Lukasz Porwol (20)
  Islam Hassan (20)
  Adegbojega Ojo (20)
  John Breslin (20)
  
  20. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
  
• 丛书名：Electronic Participation
• ISBN：978-3-319-22500-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Harnessing spontaneous contributions of citizens on Social Media and networking sites is a major feature of the next generation citizen-led e-Participation paradigm. However, extracting information of interest from Social Media streams is a challenging task and requires support from domain specific language resources such as lexica. This work describes our efforts at developing a Knowledge Extraction and Management component which employs a lexicon for extracting information related to public services in Social Media contents or streams as part of a holistic technology infrastructure for citizen-led e-Participation. Our approach consists of three basic steps -(1) acquisition and refinement of public service catalogues, (2) organization of the public service names into a lexicon based on different semantic similarity measures and (3) development of a dictionary-based Named Entity Recognizer (NER) or “spotter-based on the lexicon. We evaluate the performance of the NER solution supported by contextual information generated by two well-known general-purpose information NER tools (DBpedia Spotlight and Alchemy) on a dataset of tweets. Results show that our strategy to domain specific information extraction from Social Media is effective. We conclude with a scenario on how our approach could be scaled-up to extract other types of information from citizen discussions on Social Media.