Lexical Knowledge Structures

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Extracting semantic relation if S p (V 1 , V 2 ) > C 1 (= 8.5) then semantic relation, S p , as indicated by the pattern p is inferred between (V 1 , V 2 ) Also for asymmetric relations S p (V 1 , V 2 )/S p (V 2 , V 1 ) > C 2 (taken as 5) A K Nirala Lexical Knowledge Structures
Pruning If the pattern matching was low (< 10) mark unrelated. happens-before If not-detected Un-mark enablement, if it is detected. strength if detected Un-mark similarity, if it is detected. Out of strength, similarity, opposition and enablement Output the one with highest score. and still marked. If no relation detected so far. mark unrelated. A K Nirala Lexical Knowledge Structures
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Lexical Knowledge Structures By Ash
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SHRDLU (1971) A K Nirala Lexical Kn
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SHRDLU : a success story. Considere
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ConceptNet ConceptNet A K Nirala Le
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Typical relations in concept net A
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Relations in ConceptNet Agents, Thi
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Relations in ConceptNet Functional,
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Development Process of ConceptNet v
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Extraction phase Relations are extr
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Relaxation phase (contd.) SuperThem
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Evaluation of accumulated data 8 ju
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Knowledge acquisition from the gene
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OMCS web interface A sample web int
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Feedback and Inference (contd.) Met
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More user inputs Clarification by s
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ConceptNet5 ConceptNet5 released on
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URI hierarchy Uniform Resource Iden
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Concept URIs Each concept has minim
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ConceptNet5.1 WEB API Lookup : When
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API for Association BASE URL : http
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GOOSE 2004 Goal-Oriented Search Eng
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GOOSE : a scenario [3] Goal : I wan
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Other applications [4] Commonsense
Page 47 and 48: Information Extraction Google searc
Page 49 and 50: YAGO data model, few examples Elvis
Page 51 and 52: YAGO Model: Formal view common enti
Page 53 and 54: Classes for all literals Classes fo
Page 55 and 56: Semantics : Rewrite rule (contd.)
Page 57 and 58: Semantics : Rewrite rule (contd) Gi
Page 59 and 60: Theorems & Corollary Given F = (I
Page 61 and 62: Sources for YAGO Sources and Inform
Page 63 and 64: Information Extraction Two steps (Y
Page 65 and 66: Infoboxes Infobox type establishes
Page 67 and 68: Types of facts Category system of W
Page 69 and 70: Defining hierarchy of classes using
Page 71 and 72: Word heuristics A means relation is
Page 73 and 74: Quality Control & Type Checking Can
Page 75 and 76: Evaluating YAGO Randomly selected f
Page 77 and 78: YAGO 2 : Extensible Extraction Arch
Page 79 and 80: Information Extraction from differe
Page 81 and 82: YAGO in development of ontologies Y
Page 83 and 84: Downloading YAGO Freely available a
Page 85 and 86: VerbOcean Developed at University o
Page 87 and 88: Relations captured by VerbOcean Sim
Page 89 and 90: Relations captured by VerbOcean Ena
Page 91 and 92: Extracting Associated verb pairs 1.
Page 93 and 94: Lexico-syntactic patterns (contd.)
Page 95 and 96: Lexico-syntactic patterns (contd.)
Page 97: Scoring the verb pair on the patter
Page 101 and 102: Bibliography I Timothy Chklovski an
Page 103 and 104: Bibliography III Mueller E T Lim G
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Lexical Knowledge Structures

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