Lexical Knowledge Structures

More documents

Recommendations

Info

n-ary relations Expressing multiple relations 7 Every edge is given an edge identifier. #1 (Sam, is a, scientist) #2 (#1, since, 1998) #3 (#1, source, Wikipedia) 7 picture taken from presentation by Fabian M. Suchanek A K Nirala Lexical Knowledge Structures
YAGO Model: Formal view common entities : which are neither facts nor relations. E.g.# : singer, person, Elvis Presley individuals : common entities which are not classes. E.g.# : Elvis Presley Its a reification graph. defined over set of common entities nodes C, set of edge identifiers I set of relation names R reification graph is an injective total function G C,I ,R : I → (C ∪ I ) × R × (C ∪ I ) 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 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: YAGO data model, few examples Elvis
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
show all

Lexical Knowledge Structures

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?