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KEIPD: Knowledge Extraction and Inference System for Personal Documents

  • Zhaoyang Lv
  • Yuanyuan Liu
  • Xiaohui Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)

Abstract

Public personal documents on the Internet, such as resumes and personal homepages, may imply social relationships among people, which is of great value in various applications. This paper presents KEIPD, a system to extract and infer knowledge from personal documents. KEIPD employs a tree-similarity based approach to extract information from personal documents to obtain a relational network of entities. Then the inference of social relationships can be transformed into a link prediction problem. KEIPD implements some popular unsupervised predictors for link prediction and prune the candidate entity pairs based on the domain-dependent constraint.

Keywords

Link Prediction Parse Tree Name Entity Recognition Relational Network Path Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.School of Information TechnologyYork UniversityTorontoCanada

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