Lexical Knowledge Structures

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Relations in ConceptNet, K-Lines There are 20 different relations (as in ConceptNet2.1) K lines 2 (1.25 million assertion) ConceptuallyRelatedTo ‘bad breath’‘mint’‘f=4;i=0;’ ThematicKLine ‘wedding dress’‘veil’‘f=9;i=0;’ SuperThematicKLine ‘western civilisation’‘civilisation’ ‘f=0;i=12;’ 2 [5] : Marvin Minsky : A Theory of Memory A K Nirala Lexical Knowledge Structures
Relations in ConceptNet Agents, Things AGENTS (104 000 assertions) CapableOf ‘dentist’‘pull tooth’‘f=4;i=0;‘ THINGS (52 000 assertions) IsA (Hyponym) ‘horse’‘mammal’‘f=17;i=3;‘ PartOf (Meronym) ‘butterfly’‘wing’‘f=5;i=1;‘ DefinedAs (Gloss) ‘meat’‘flesh of animal’‘f=2;i=1;‘ MadeOf ‘bacon’‘pig’‘f=3;i=0;‘ PropertyOf ‘fire’‘dangerous’‘f=17;i=1;‘ 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: Typical relations in concept net A
Page 13 and 14: Relations in ConceptNet Functional,
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
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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
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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
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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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