Chapter 6. Inference beyond the index 2007 년 1 월 30 일 부산대학교 인공지능연구실 김민호 Text : FINDING OUT ABOUT Page. 182 ~ 251

Introduce(1/4) - Knowledge representation  AI is primary contribution to computer science!  Related to:  Programming language’s “abstract data types”  Database (logical!) modeling -eg, ‘ontology’ building

Introduce(2/4) - Traditionally (GOFAI) serving deductive goals  Valid inference Man (x) -> Mortal(x) Man(Socrates) Mortal(Socrates)  Expressiveness  Even first-order logic offers tradeoffs wrt/propositional

Introduce(3/4) - Machine learning: inductive sources of knowledge  Data-mining  Statistical analysis of large datasets  Searching for patterns  Inferring semantics (meaning) from syntactic cues  from word statistics  from bibliographic citations  Even from capitalization -Proper names → Global reference!

Introduce(4/4) - Exploiting other (non-index) information

Subsection  6.1 Citation: Interdocument Links  6.2 Hypertext, Intradocument Links  6.3 Keyword Structures  6.4 Social Relations among Authors  6.5 Modes of Inference  6.6 Deep Interfaces  6.7 FOA(The Law)  6.8 FOA(Evolution)  6.9 Text-Based Inteligence

6.1 Citation: Interdocument Links  Citation is a pointer, from a document to a document.  how accurately do we know the location of the citation in the citing paper?  how precisely is its pointer into the cited paper?

6.1 Citation: Interdocument Links  Document similarity based on shared bibliographies  Coupling  Overlap between two document’s bibliographis  Co-citation  Degree to which two documents are both referenced by other document’s bibliographies

6.1 Citation: Interdocument Links

 Common law depends on rule of precedence  Stare decisis  Prior decisions applied to new factual situations  Hierarchical local jurisdictions limit interpretation  Dialectic debate (rationale, justice, change, etc.)  NB: Same corpus used by both adversaries  References to history of O(10 year)

6.1 Citation: Interdocument Links  Unambiguous

6.1 Citation: Interdocument Links  Eigen-structure of citation graphs  Authority: analogous to bibliometric ‘impact’  Hubs: Pull together authorities  Citation-expanded hitlist

Summary  Writings do not exist in isolation  Author explicit references to other’s documents provides excellent evidence concerning the ARGUMENTS they each make

Google’s Page rank  Simulate stationary distribution of Markov process with incremental update of page weight

Hierarchic structure  Visualizing references

Pedagogical structure  Prerequisite lattice  Reading-level analysis – against well – tested vocabularies  Level of coverage

Argument relationships

thesaurus  BT/NT/RT relations  aot / AI “ontologies”

WordNet

Classification taxonomies  Institutionalized  Myopic discipline focus

Neural networks - basics Query Retrieval Relevance Feedback

Construction of initial NNet

Query: “Case-based approach to the law”  Morphological processing of tokens  High-frequency “noise” words elided

SAS in IR  Initial query may refer to many “features”  Descriptive keywords are only one type  Retrieval becomes a process of completion

Type of relation less important than fact of association

Initial retrieval

Most highly ranked document

Goal document

3rd-order transitive associations

4th-order transitive associations

Swanson's Arrowsmith