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A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases
The document discusses the application of distributional semantics for selective reasoning in commonsense knowledge bases, highlighting the development of a Distributional Navigational Algorithm (DNA) to enhance semantic search and reasoning capabilities. It outlines the motivation behind using semantic systems for intelligent behavior, addressing challenges like knowledge base incompleteness and scalability. The findings suggest that while distributional semantics improves path selection and knowledge base completeness, further research is needed to enhance precision in these models.
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A Distributional Semantics Approach for Selective Reasoning on Commonsense Graph Knowledge Bases
- 1. A Distributional SemanticsApproach for Selective Reasoning on Commonsense Graph Knowledge Bases André Freitas, João C. Pereira Da Silva, Edward Curry, Paul Buitelaar Insight Centre for Data Analytics NLDB 2014 Montpellier, France
- 2. Applying Distributional Semanticsto Commonsense Reasoning André Freitas, João C. Pereira Da Silva, Edward Curry, Paul Buitelaar Insight Centre for Data Analytics NLDB 2014 Montpellier, France
- 3. Outline Motivation DistributionalSemantics Distributional Navigational Algorithm (DNA) Evaluation Take-away message
- 4.
- 5. Semantic Systems &Commonsense Knowledge Bases Knowledge Representation Model Commonsense Data Expected Result: Intelligent behavior Semantic flexibility, predictive power, automation ... Acquisition Inference Model Scalability Consistency 5
- 6. Formal Representation ofMeaning 6
- 7. Most semanticmodels have dealt with particular types of constructions, and have been carried out under very simplifying assumptions, in true lab conditions. If these idealizations are removed it is not clear at all that modern semantics can give a full account of all but the simplest models/statements. Formal World Real World Baroni et al. 2013 Semantics for a Complex World 7
- 8. Commonsense Reasoning Copingwith KB incompleteness - Supporting semantic approximation Selective reasoning - Selecting the relevant facts in the context of the inference Acquisition Scalability Strategy: Using distributional semantics to solve both the acquisition and scalability problems 10
- 9. Example Does John Smithhave a degree? 11
- 10. Example Does John Smithhave a degree? 12
- 11. Example Does John Smithhave a degree? Selective reasoning Coping with KB Incompleteness 13
- 12. Applications Semantic search Question answering Approximate semantic inference Word sense disambiguation Paraphrase detection Text entailment Semantic anomaly detection ... 14
- 13.
- 14. Distributional Hypothesis “Words occurringin similar (linguistic) contexts tend to be semantically similar” He filled the wampimuk with the substance, passed it around and we all drunk some 16
- 15. Distributional Semantic Models(DSMs) car dog cat bark run leash 17 Context
- 16. Semantic Similarity &Relatedness θ car dog cat bark run leash 18
- 17. DSMs as CommonsenseReasoning Commonsense is here θ car dog cat bark run leash 19
- 18. DSMs as CommonsenseReasoning θ car dog cat bark run leash ... vs. Semantic best-effort
- 19.
- 20. Approach Overview Distributional Navigational Algorithm (DNA) Ƭ-Space Large-scale unstructureddata Unstructured Commonsense KB Structured Commonsense KB Distributional semantics Reasoning Context 22
- 21.
- 22. Distributional semantic relatednessas a Selectivity Heuristics Distributional heuristics 24 target source
- 23. Distributional heuristics 25 Distributional semantic relatednessas a Selectivity Heuristics target source
- 24. Distributional heuristics 26 Distributional semantic relatednessas a Selectivity Heuristics targettarget source
- 25. Distributional Navigational Algorithm (DNA) Input: Reasoningcontext: Source and target word pairs Structured Knowledge Base (KB) Distributional Semantic Model (DSM) Output: Meaningful paths in the KB connecting source and target 27
- 26.
- 27. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB John Smith 29 Step: Resoning context = <John Smith, degree>
- 28. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB 30 occupation Step: Get neighboring relations engineer John Smith John Smith catholic religion ...
- 29. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB 31 Step: Calculate the distributional semantic relatedness between the target term and the neighboring entities John Smith John Smith catholicoccupation engineer religion ... sem rel (catholic, degree) = 0.004 sem rel (engineer, degree) = 0.07
- 30. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB 32 John Smith John Smith catholicoccupation engineer religion ... sem rel (catholic, degree) = 0.004 sem rel (engineer, degree) = 0.01 Step: Filter the elements below the threshold
- 31. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB 33 John Smith John Smith occupation engineer Step: Navigate to the next nodes
- 32. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB 34 John Smith John Smith occupation engineer Step: redefine the reasoning context: <engineer, degree>
- 33. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Get neighboring relations John Smith engineer learn subjectof bridge a rivercapableof dam creates 35 occupation
- 34. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB sem rel (dam, degree) = 0.002 Step: Calculate distributional semantic relatedness between the target term and the neighboring entities sem rel (brdge a river, degree) = 0.004 sem rel (learn, degree) = 0.01 John Smith engineer learn subjectof bridge a rivercapableof dam creates 36 occupation
- 35. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB sem rel (dam, degree) = 0.002 Step: Filter the elements below the threshold sem rel (brdge a river, degree) = 0.004 sem rel (learn, degree) = 0.01 John Smith engineer learn subjectof bridge a rivercapableof dam creates 37 occupation
- 36. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Search highly related entities in the KB not connected (distributional semantics) John Smith engineer learn subjectof Reasoning context: ‘learn degree’ 38 occupation
- 37. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Navigate to the elements above the threshold John Smith engineer learn subjectof 39 occupation
- 38. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Repeat the steps John Smith engineer learn subjectof education have or involve 40 occupation
- 39. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Repeat the steps John Smith engineer learn subjectof education have or involve at location university 41 occupation
- 40. Distributional Navigational Algorithm (DNA) DoesJohn Smith have a degree? Structured Commonsense KB Distributional Commonsense KB Step: Search highly related entities in the KB not connected (distributional semantics) John Smith engineer learn subjectof education have or involve at location university Reasoning context: ‘university degree’ 42 occupation
- 41. Distributional Navigational Algorithm (DNA) Structured CommonsenseKB Distributional Commonsense KB John Smith engineer learn subjectof education have or involve at location universitycollege Does John Smith have a degree? Step: Search highly related entities in the KB not connected (distributional semantics) Reasoning context: ‘university degree’ 43 occupation
- 42. Distributional Navigational Algorithm (DNA) Structured CommonsenseKB Distributional Commonsense KB John Smith engineer learn subjectof education have or involve at location universitycollege Does John Smith have a degree? Step: Repeat the steps degree gives 44 occupation
- 43. Examples of SelectedPaths Reasoning context: < battle, war > 45
- 44. Examples of SelectedPaths 46
- 45. Improving the DNAAlgorithm: Semantic Differential Δ 47 Closer to the target
- 46.
- 47. Evaluating Semantic Selectivity Howdoes the semantic selectivity scale with the increase in the number of candidate paths? How does the accuracy of the semantic selectivity scale with the increase in the number of candidate paths? 49
- 48. Experimental Setup Queryset: 102 word pairs (derived from Question Answering over Linked Data queries 2011/2012) E.g. - What is the highest mountain? - Mount Everest elevation 8848.0 Distributional Semantic Model: ESA Threshold: η = 0.05 Dataset: ConceptNet Gold standard: Manual validation with two independent annotators 50
- 49. ConceptNet Number ofclauses x per relation type: x = 1 (45,311) 1 < x < 10 (11,804) 10 <= x < 20 (906) 20<= x < 500 (790) x >= 500 (50) 51
- 50. Semantic Selectivity total numberof paths (path length n) number of paths selected selectivity = The semantic selectivity for the DNA approach scales with the increasing in the number of candidate paths How does the semantic selectivity scale with the increase in the number of candidate paths? 52
- 51. Semantic Relevance number ofreturned paths number of relevant paths accuracy = What is the semantic relevance of the returned paths? How does the accuracy of the semantic selectivity scale with the increase in the number of candidate paths? There is a significant reduction in the accuracy with the increase in the number of paths. However the accuracy value remains high. 53
- 52. Evaluating Semantic Incompleteness Howdoes distributional semantics support increasing the KB completeness? 54
- 53. Incompleteness 39 <source,target> query pairs Over all ConceptNet entities Example: - Query: < mayor, city > - Returned entities: council municipality downtown ward incumbent borough reelected metropolitan elect candidate politician democratic 55
- 54. Incompleteness Avg. KBcompletion precision = 0.568 Avg. # of strongly related entities returned per query = 19.21 number of retrieved entities number of strongly related entities KB completion precision = How does distributional semantics support increasing the KB completeness? Distributional semantics supports improving the completeness of the KB However, further investigation is necessary to improve the precision of distributional models 56
- 55. Take-away message DistributionalSemantics provides in the selection of meaningful paths: - high selectivity - high selectivity scalability - medium-high accuracy Distributional semantics supports improving the completeness of the KB - However, further investigation is necessary to improve the precision of distributional models in this context 57
- 56.