Knowledge Representation and Semantic Capturing

1
Knowledge Representation and Semantic Capturing

• Albena Strupchanska
• Linguistic Modelling Department,
• Institute for Parallel Processing,
• Bulgarian Academy of Sciences
• albena_at_lml.bas.bg

2
Few words about me

• Programmer at LMD, 2001 -2003
• Research Associate at LMD since 2003
• Research interests
• knowledge representation CGs, LFs in NLU
  ontologies, semantic web
• information extraction
• e-learning
• question-answering

3
Knowledge Representation Conceptual Graphs

• Realization of CG operations (generalization,
  specialization, projection and join)
• Integration of CG operations in CGWorld
• Usage of those operation in several system
  prototypes (simple question-answering, eLearning)

4
Knowledge Acquisition form Text

• General approach used in a few prototypes that
  process text in controlled English (restricted
  domains)
• Lexical analysis, Named entities recognition and
  Part-of-speech tagger - GATE
• Syntactic analysis - parser developed by Milena
  Yankova
• Result translation of text into Logical Forms
  (LFs) and other similar formalisms e.g.
  Conceptual Graphs

5
Knowledge-based approaches

• Resources used
• type hierarchy
• domain knowledge
• Attempts to
• treat negation (prototype developed)
• recognize scenarios (FRET system)

6
Naive Negation Processing

• Sentence/Query -gt LF -gt CG
• The question
• "Who does not buy bonds?
• will be translated to
• (all (X,bond(X)buy(Y)?(Y,agnt,Univ)
• ?(Y,obj,X)))
• set the negation scope to the whole sentence

7
Naive Negation Processing

• construct all possible LFs with localization of
  the negated phrases
• (2.1) exists(X,bond(X)buy(Y)?(Y,agnt,Univ)
  ?(Y,obj,X))
• (2.2) exists (X,bond(X) buy(Y) ?(Y,agnt,Univ)
  ?(Y,obj,X))
• (2.3) exists (X,bond(X)buy(Y) ?(Y,agnt,Univ)
  ?(Y,obj,X))
• (2.1) Who does buy financial instruments
  different from bonds ?
• (2.2) Who is doing other actions with bonds
  except buying them?
• (2.3) Who is doing other actions except buying
  with something different from bonds

8
Naive Negation Processing

• Every negated concept is replaced by its
  hierarchical environment
• every concept corresponding to a verb is replaced
  by its "antonym or complementary events"
• every object is replaced by the so-called
  restricted universally quantified concepts.
• S(nc)(Sib(nc) ? SonSib(nc)) \ Son(nc), where nc
  is the negated concept
• Projection of the query to the KB of CGs gt
  retrieval of answers

9
FRET - Football Reports Extraction of Templates

• Semantically driven approach for scenario
  recognition and templates filling
• deep understanding only in certain
  scenario-relevant points by elaborating
  inference mechanisms
• LF representation for effective inference
• Text football reports with specific paragraph
  structure (tickers for each minute)

10
FRETs Architecture
11
FRET - Resource Bank

• Lexicon
• Grammar rules
• Rules for translation in logical form
• Graphs of events
• description of the domain events (nodes) and
  relations (arcs) between them
• Templates description (uninstantiated LFs)

12
FRET - Graph of Events

• Three types of events (nodes in a directed
  graph)
• Main event - LF description of obligatory and
  optional fields of the template and relations
  between them
• Base events - LF of most important self-dependent
  events in the chosen domain
• Sub-events - kinds of base events that are
  immediately connected to the main event (i.e.
  there exists an arc between the nodes of the main
  and the sub-events)

13
FRET - Graph of Events

• Four types of relations (an arc with associated
  weight in the graph)
• Event E2 invalidates event E1, i.e. event E2
  happens after E1 and annuls it
• Event E1 entails event E2, i.e. when E1 happens
  E2 always happens at the same time.
• Event E1 enables event E2, i.e. event E1 happens
  before the beginning of event E2 and event E1 is
  a precondition for E2
• Event E2 is a part of event E1.

14
FRET - Graph of Events
15
FRET - Identification of Negation

• Explicit negation
• Short sentences containing No
• Complete sentence containing Not/Non/No
• Both cases marker NEG attached to the LF of the
  previous sentence or succeeding part of the
  sentence
• Implicit negation
• Sentences with but, however, although
• Markers BAHpos and BAHneg
• Markers are inserted during the parsing process

16
FRET - Negation

• Sentence
• 79 mins Henry fires at goal, but misses from a
  tight angle.
• Logical forms
• time(79) fire(A) ?(A,agnt,Henry)
  ?(A,at,B)
• goal(B) marker(BAHpos,7).
• time(79) miss(A) ?(A,agnt,Henry)
  ?(A,form,B) angle(B) ?(B,char,C) tight(C)
  marker(BAHneg,7).

17
FRET -Treatment of Negation

• Interpretation of marked LFs
• NEG
• the matching result is ignored
• BAHpos or BAHneg
• there are two possible interpretations
• negation
• conjunction of independent statements
• the algorithm checks whether the dual LFs marked
  with these markers can be matched to events
  connected with invalidate relation in the graph
• if this succeeds, the previous matching is
  ignored.

18
FRET - Templates Filling

• The templates filler performs two main steps
• Matching LF
• based on the modification of the unification
  algorithm
• Filling templates
• The templates filler processes those LF, which
  are produced from the so-called extended
  paragraph. Thus each paragraph is treated
  separately.

19
FRET - Matching Algorithm

• Direct matching
• each LF from the extended paragraph to the main
  event
• Inference Matching
• use inference rules and the knowledge base
• FRET inference-matching algorithm derives an
  inference from
• base events LFs gt sub-events LFs gt main event
  LF
• If necessary information about some sub- event
  gt consider type of relation between this
  sub-event and the main event gt either recognize
  or not the main event

20
Advantages and disadvantages

• Logical forms convenient formalism for making
  inference
• Knowledge representation as graph of events
• Partial parsing (better to understand less than
  nothing)
• - Creation of graph of events (nodes presented in
  LFs) and templates (presented in LFs)
• - Narrow and restricted domains (not scaleable)

21
Conclusion

• Knowledge-based approaches are successful when
  they are applied to specific domains
• Choice of domain representation formalism is
  crucial for semantic capturing
• Domain modelling is difficult and time-consuming
• Much efforts for semantic capturing of simple
  cases. Probably when these cases are the right
  ones the goal justifies the means

22
Thank you!Any questions?