Lexical Knowledge Structures

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Fields in ConceptNet5.1 { } "endLemmas": "fruit", "rel": "/r/IsA", "end": "/c/en/fruit", "features": [ "/c/en/apple /r/IsA -", "/c/en/apple - /c/en/fruit", "- /r/IsA /c/en/fruit" ], "license": "/l/CC/By", "sources": [ "/s/rule/sum_edges" ], "startLemmas": "apple", "text": [ "fruit", "apple" ], "uri": "/a/[/r/IsA/,/c/en/apple/,/c/en/fruit/]", "weight": 244.66679999999999, "dataset": "/d/conceptnet/5/combined-core", "start": "/c/en/apple", "score": 1049.3064999999999, "context": "/ctx/all", "timestamp": "2012-05-25T03:41:00.346Z", "nodes": [ "/c/en/fruit", "/c/en/apple", "/r/IsA" ], "id": "/e/3221407ec935683f2b7079b0495f164e1e321cd4" A K Nirala Lexical Knowledge Structures
ConceptNet5.1 WEB API Lookup : When URI is known. Example http://conceptnet5.media.mit.edu/data/5.1/c/en/apple Search : when URI is not known Performed with base URL + criteria (in GET) BASE URL : http://conceptnet5.media.mit.edu/data/5.1/search WITH criteria : http://conceptnet5.media.mit.edu/data/5.1/searchtext=apple Association : for finding similar concepts. 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 and 12: Relations in ConceptNet Agents, Thi
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: Concept URIs Each concept has minim
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
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 and 98:
Scoring the verb pair on the patter
Page 99 and 100:
Pruning If the pattern matching was
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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