Lexical Knowledge Structures

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YAGO 2 : Extensible Extraction Architecture Implication rules stored as “$1 $2 $3; $2 subpropertyOf $4;”implies “$1 $4 $3” Replacement rules for cleaning HTML tags, normalizing units etc “\{\{USA\}\}” replace “[[United States]]” Extraction rules stores regular expression rules 12 . for deriving fact. 12 the regex is as defined for : regular expression syntax of java.util.regex A K Nirala Lexical Knowledge Structures
Information Extraction from different dimension Temporal Dimension: Assign begin and/or end of time spans to all entries, facts, events, etc. Geo-Spatial Dimension: assign location in space to all entities having a permanent location. GeoNames 13 is taped. Textual Dimension: relation like hasWikipediaAnchorText, hasCitationTitle, etc, are extracted from Wikipedia multi-lingual data from Universal Wordnet is added. 13 from http://www.geonames.org/ 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
Page 21 and 22:
Evaluation of accumulated data 8 ju
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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
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: YAGO 2 : Extensible Extraction Arch
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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