Automatic Semantic Network Generation from Unstructured Documents – The Options

摘要: There is plenty of information that exists in freeform or web-based text and whose use from a computing perspective limited because its unstructured nature. This data first needs to be structured as knowledge base facilitate tasks such query, search, re-use, sharing, keeping current, question answering (Q&A), prediction, disambiguation summarization. Structuring through topic models, database systems taxonomies have been tried but these tend domain specific scalability the level diverse available. Knowledge representation create bases can done linking used applications LinkedIn, Facebook, BabelNet, Google Graph, among others. essentially forms semantic structure also called network (SN). The automatic generation structures problem largely unresolved. paper concentrates on development model uses machine learning automatically generate networks web data. SNs are an important component support Q&A, retrieval Automatic generation, known induction, reduces complete reliance manual time-consuming authoring.