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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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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 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.
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 ending with artifact name We cut trees with an axe. action upon artifact
Sentences containing artifact name Use the air+pump to fill the tyre. Use the <artifact> to <action> the <upon>
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
Data Wikipedia – 249 files Wordnet – 81,837 descriptions Princeton noun-artifact corpus – 82,115 sentences
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 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