Automatic Extraction and Incorporation of Purpose Data 目的将数据的自动提取.pptVIP

Automatic Extraction and Incorporation of Purpose Data into PurposeNet P. Kiran Mayee Rajeev Sangal Soma Paul INTRODUCTION Purpose Need for a knowledge base of objects and actions in which the knowledge is organized around purpose. PurposeNet PurposeNet is an intelligent knowledge-based system dealing with specialized attributes of artifacts – namely, their purpose, purpose of their types, components, accessories, as also data about their birth, processes, side-effects, maintenance and result on destruction. PurposeNet Building the PurposeNet Template Designing Revision Refinement of template Selection of Domain Information Retrieval from Web Ontology population Testing Need for Automation Acquisition bottleneck Massive availability of text Availability of purpose cues Purpose data required Artifact -- garage Purpose Action -- store Upon -- vehicle Purpose Cues Word(s)? Lexical entities in a particular order Classification Sentences beginning with artifact name Sentences ending with artifact name Sentence containing artifact name Hidden Cues Sentences commencing with artifact name Sentences ending with artifact name Sentences containing artifact name Methodology for purpose data extraction Algorithm for Purpose Data Extraction Data Wikipedia – 249 files Wordnet – 81,837 descriptions Princeton noun-artifact corpus – 82,115 sentences Observations – summary results Purpose Data Extraction Misses IE Metrics for Extraction Result BreakUp per Cue Class Comparison with manually built Ontology Exponential increase in speed High Error Rate Issues Redundancy Primary purpose not always obtained Pronouns and brand names Correctness and consistency not guaranteed One-to-one mapping assumed Other sentence manifestations Further Enhancements Parsed input Cues for hidden case Better artifact lookup list Multipage lookup for consistency Cloud computing Automating other attributes of PurposeNet Conclusions A methodology was proposed for automated ontology