1. ASKNet: Automated Semantic Knowledge Network Brian Harrington Stephen Clark Oxford University Computing Laboratory From Proceeding of AAAI 2007 Another paper: “ASKNet: Automatically Generating Semantic Knowledge Networks”, Brian Harrington, AAAI2007, Poster Advisor: Hsin-His Chen Reporter: Chi-Hsin Yu Date: 2008.01.30

2. Outlines • Introduction • Motivation • Parsing & Semantic Analysis • The Network • Information Integration • Evaluation • Conclusions & Future Work

3. Introduction • ASKNet system • Automated Semantic Knowledge Network • Based on Discourse representation theory, presented by Kamp, 1981 • 1.5 million nodes, 3.5 million edges • the construction of semantic networks on a large scale • to reduce the knowledge acquisition bottleneck for AI

4. Introduction –What Is Semantics? Implicate semantics (Peter D. Turney, 2006) Lexical semantics Semantic-related resources Ontology (The Suggested Upper Merged Ontology, SUMO) Formal semantics (logic representation)

5. Discourse representation theory, DRT Existing discourse New sentence

6. Introduction - Procedure • Creation • joining sentence level network fragments, and then joining document level networks • NE Recognition, and parsing (Combinatory Categorial Grammar, CCG) • Information Integration • Each new sentence encountered is seen as an update to the world knowledge base represented by the entire knowledge network. • Spreading activation (Collins & Loftus 1975) is used to determine the semantic connections between entities and concepts.

7. Motivation A founder of AI, Marvin Minsky, once estimated that ‘...commonsense is knowing maybe 30 or 60 million things about the world.

8. Motivation (cont.) • The existing system • Limited size, limited type of relation • No scalability for the construct-by-manual approach • An ideal relation harvesting algorithm (from Expresso system, Patrick Pantel, 2006) • Performance: • it must generate both high precision and high recall relation instances • Minimal supervision: • it must require little or no human annotation; • Breadth • it must be applicable to varying corpus sizes and domains • Generality: • it must be applicable to a wide variety of relations

9. Parsing • The parser • extremely efficient and robust, and has wide coverage • uses the Clark and Curran (2004b) parser • based on the linguistical formalism Combinatory Categorial Grammar (CCG) (Steedman 2000) • A named entity (NER) recognition tool is also built into the parser. • person, organization, date, time, location and monetary amount

10. Semantic Analysis • the semantic analysis tool Boxer (Bos et al. 2004) • converting the parsed output into a series of first order logic predicates • The logical theory used for the representation is Discourse representation Theory (DRT) (Kamp & Reyle 1993) -- formal semantics • The output of Boxer • a Prolog style discourse representation structure • variables assigned to objects • first order predicates representing relations between those objects • recursively defined sub-structures

11. Semantic Analysis –Example Susan Bob likes Fred

12. The Network • Consisting of object nodes linked by directed labeled relations • allows nodes and relations to be combined to form complex nodes which can represent larger and more abstract concepts • The nested structure of the network • allows for the expression of complex concepts • without having to resort to a rigidly defined structure such as the hierarchical structure used by WordNet (Fellbaum 1998) • Each relation in the network has a weight • Represents the confidence of the network in the “real world” existence of the relation and also its salience

13. The Network – Example

14. Information Integration – The Update Algorithm • Merging • sentence level networks  document level networks • document level networks  the overall knowledge network • Basic premise behind the algorithm • some of the nodes in the update network may refer to the same real world entities as existing nodes in the knowledge network • Potential node pair matches are initially scored based on lexical information (edit distance), then • spreading activation is used to gradually refine the scores

15. gore=血 Edit distance Gore=高爾

16. Information Integration – Spreading Activation • Firing a node sends activation out to all of the nodes semantically connected to that node • Determining the semantic distance between various entities and concepts • Localization of spreading activation • only accesses a small number of nodes in a localized area

17. Information Integration –Semantic Distance Evaluating nodes

18. Evaluation processing approximately 2 million sentences Localization

19. Conclusions & Future Work • Conclusions • very large semantic networks can be created quickly • Future work • varying the strength of links based on the confidence of the parse or the source from which the information came; • distinguishing between type and token instances of objects in the network; • implementing a more rigorous pruning algorithm to remove extraneous network fragments • Evaluation method

20. Thanks!!