Lexical Knowledge Structures

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Relations in ConceptNet Events, Spatial, Causal EVENTS (38 000 assertions) PrerequisiteEventOf ‘eat breakfast’‘wake up in morning’ ‘f=2;i=0;’ FirstSubeventOf ‘start fire’‘light match’‘f=2;i=3;’ SubeventOf ‘eat breakfast’‘chew food’‘f=2;i=0;’ LastSubeventOf ‘attend classical concert’‘applaud’‘f=2;i=1;’ LocationOf EffectOf DesirousEffectOf SPATIAL (36 000 assertions) ‘army’‘in war’‘f=3;i=0;’ CAUSAL (17 000 assertions) ‘view video’‘entertainment’‘f=2;i=0;’ ‘sweat’‘take shower’‘f=3;i=1;’ A K Nirala Lexical Knowledge Structures
Relations in ConceptNet Functional, Affective FUNCTIONAL (115 000 assertions) UsedFor ‘alarm clock’‘wake up’‘f=1;i =2;’ CapableOfReceivingAction ‘drink’‘serve’‘f =0;i =14;’ MotivationOf DesireOf AFFECTIVE (34 000 assertions) ‘go to bed early’‘wake up in morning’ ‘f =3;i=0;’ ‘person’‘not be depressed’ ‘f=2;i=0;’ A K Nirala Lexical Knowledge Structures
Page 1 and 2: Lexical Knowledge Structures By Ash
Page 3 and 4: SHRDLU (1971) A K Nirala Lexical Kn
Page 5 and 6: SHRDLU : a success story. Considere
Page 7 and 8: ConceptNet ConceptNet A K Nirala Le
Page 9 and 10: Typical relations in concept net A
Page 11: Relations in ConceptNet Agents, Thi
Page 15 and 16: Development Process of ConceptNet v
Page 17 and 18: Extraction phase Relations are extr
Page 19 and 20: Relaxation phase (contd.) SuperThem
Page 21 and 22: Evaluation of accumulated data 8 ju
Page 23 and 24: Knowledge acquisition from the gene
Page 25 and 26: OMCS web interface A sample web int
Page 27 and 28: Feedback and Inference (contd.) Met
Page 29 and 30: More user inputs Clarification by s
Page 31 and 32: ConceptNet5 ConceptNet5 released on
Page 33 and 34: URI hierarchy Uniform Resource Iden
Page 35 and 36: Concept URIs Each concept has minim
Page 37 and 38: ConceptNet5.1 WEB API Lookup : When
Page 39 and 40: API for Association BASE URL : http
Page 41 and 42: GOOSE 2004 Goal-Oriented Search Eng
Page 43 and 44: GOOSE : a scenario [3] Goal : I wan
Page 45 and 46: 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
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Types of facts Category system of W
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Defining hierarchy of classes using
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Word heuristics A means relation is
Page 73 and 74:
Quality Control & Type Checking Can
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Evaluating YAGO Randomly selected f
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YAGO 2 : Extensible Extraction Arch
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Information Extraction from differe
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YAGO in development of ontologies Y
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Downloading YAGO Freely available a
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VerbOcean Developed at University o
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Relations captured by VerbOcean Sim
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Relations captured by VerbOcean Ena
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Extracting Associated verb pairs 1.
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Lexico-syntactic patterns (contd.)
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Lexico-syntactic patterns (contd.)
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Scoring the verb pair on the patter
Page 99 and 100:
Pruning If the pattern matching was
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Bibliography I Timothy Chklovski an
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Bibliography III Mueller E T Lim G
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Lexical Knowledge Structures

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