Lexical Knowledge Structures

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Bibliography II Hugo Liu and Push Singh. Conceptnet: A practical commonsense reasoning toolkit. BT Technology Journal, 22:211–226, 2004. Marvin Minsky. K-lines: A theory of memory. Massachusetis Institute of Technology, (AIM-516), 1979. Pim Nauts. A kidsâ open mind common sense : Solving problems in commonsense computing with a little help from children. 2009. A K Nirala Lexical Knowledge Structures
Bibliography III Mueller E T Lim G Perkins T Singh P, Lin T and Zhu W L. Open mind commonsense: knowledge acquisition from the general public. Proceedings of the First International Conference on Ontologies, Databases, and Applications of Semantics for Large Scale Information Systems, Lecture Notes in Computer Science No 2519 Heidelberg, Springer, 2002. Fabian M. Suchanek. Leila: Learning to extract information by linguistic analysis. In In Workshop on Ontology Population at ACL/COLING, pages 18–25, 2006. 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
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Evaluation of accumulated data 8 ju
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Knowledge acquisition from the gene
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OMCS web interface A sample web int
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Feedback and Inference (contd.) Met
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More user inputs Clarification by s
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ConceptNet5 ConceptNet5 released on
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URI hierarchy Uniform Resource Iden
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Concept URIs Each concept has minim
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ConceptNet5.1 WEB API Lookup : When
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API for Association BASE URL : http
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GOOSE 2004 Goal-Oriented Search Eng
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GOOSE : a scenario [3] Goal : I wan
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Other applications [4] Commonsense
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Information Extraction Google searc
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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: Bibliography I Timothy Chklovski an
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