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Automatic Extraction and Incorporation of Purpose Data into PurposeNet

P. Kiran Mayee Rajeev Sangal Soma Paul. SCONLI3 JNU NEW DELHI. Automatic Extraction and Incorporation of Purpose Data into PurposeNet. INTRODUCTION . Purpose Need for a knowledge base of objects and actions in which the knowledge is organized around purpose. . PurposeNet.

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Automatic Extraction and Incorporation of Purpose Data into PurposeNet

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  1. P. Kiran Mayee Rajeev Sangal Soma Paul SCONLI3 JNU NEW DELHI Automatic Extraction and Incorporation of Purpose Data into PurposeNet

  2. INTRODUCTION Purpose Need for a knowledge base of objects and actions in which the knowledge is organized around purpose.

  3. 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.

  4. PurposeNet

  5. Building the PurposeNet Template Designing Revision & Refinement of template Selection of Domain Information Retrieval from Web Ontology population Testing

  6. Need for Automation Acquisition bottleneck Massive availability of text Availability of purpose cues

  7. Purpose data required Artifact -- garage Purpose Action -- store Upon -- vehicle

  8. 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

  9. Sentences commencing with artifact name

  10. Sentences ending with artifact name We cut trees with an axe. action upon artifact

  11. Sentences containing artifact name Use the air+pump to fill the tyre. Use the <artifact> to <action> the <upon>

  12. Methodology for purpose data extraction

  13. Algorithm for Purpose Data Extraction Algorithm PurpDataExtract(corpus)‏ Step1 : Read first sentence in Corpus. Step2 : Loop until end-of-corpus – 2a. if contains(sentence, artifact) and match( sentence, cuetable)‏ then extract(sentence, artifact)‏ extract(sentence, to_action)‏ extract(sentence, to_upon)‏ add_to_ontology(artifact, to_action, to_upon) else 2b. goto step 3. Step3 : Read next sentence

  14. Data Wikipedia – 249 files Wordnet – 81,837 descriptions Princeton noun-artifact corpus – 82,115 sentences

  15. Observations – summary results

  16. Purpose Data Extraction Misses

  17. IE Metrics for Extraction

  18. Result BreakUp per Cue Class

  19. Comparison with manually built Ontology Exponential increase in speed High Error Rate

  20. Issues Redundancy Primary purpose not always obtained Pronouns and brand names Correctness and consistency not guaranteed One-to-one mapping assumed Other sentence manifestations

  21. Further Enhancements Parsed input Cues for hidden case Better artifact lookup list Multipage lookup for consistency Cloud computing Automating other attributes of PurposeNet

  22. Conclusions A methodology was proposed for automated ontology population of purposenet The methodology was implemented on three corpora The time-taken for purposenet 'purpose' ontology population was a fraction of that by manual methods The Error rate was found to be high

  23. Thank You

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