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Computational Complexity of Terminological Reasoning in BACK

This article explores the computational complexity of terminological reasoning in the BACK system, a hybrid knowledge representation system. It discusses the complexity of subsumption and the formal treatment of subsumption in the system. The article also presents the definition of FLN (frame-based description language) and the consequences of the results.

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Computational Complexity of Terminological Reasoning in BACK

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  1. Computational Complexity of Terminological Reasoning in BACK Authors: Bernhard Nebel, TechnischeUniversitätBerlin, CIS/KIT Sekr. FR 5-8, Franklinstraße28/29 D-1000 Berlin 10, West-Germany e-mail: nebel@db0tuill.bitnet published in Artificial Intelligence 34: 371-383, 1988 Presented by Jordan Bradshaw and Corey White

  2. Introduction • Complexity of Subsumption • A Formal Treatment of Subsumption • Definition of FLN • Proof of NP-hardness • Formulation of the Problem • Consequences of Results Overview

  3. The BACK system is part of the KL-ONE hybrid knowledge representation system. • Which is a FDL (frame-based description language) • It's used to represent terminological knowledge Introduction

  4. Concept Relationship Example

  5. Important characteristics of FDL • take definition notions seriously • Allows concepts/roles to specify relationships to other concepts • Grandparent is a specialization of parent, although its not explicitly mentioned. • Since there is more represented than what's explicitly written, a reasoner is needed to uncover the hidden relationships. Introduction cont…

  6. Introduction cont…

  7. Some queries can be reduced to other query types • All queries in this case can be reduced to subsumptions provided the concepts/roles. • Classification -> Subsumption (provided O(n2)) • Disjointness ->Incoherency • Incoherency -> Subsumption • Property possession -> Classification Introduction cont…

  8. Subsumption is a crucial part of terminological reasoning. • Subsumption basic idea: • All detected relationships in KL-ONE are sound, but the detection procedure is incomplete. • FDL used in KL-ONE the subsumption problem can be intractable. Complexity of Subsumption

  9. BNF notation of introduction example: A Formal Treatment of Subsumption

  10. Partially defined concepts can be modeled by assuming additional anonymous atomic concepts: A Formal Treatment of Subsumption

  11. Here is the extension, for the objects described by their particular concept: A Formal Treatment of Subsumption

  12. C1 subsumes C2 if and only if set D and any extension function ε over D, the following will hold: • The language above, FLby Brachman & Levesque is intractable with respect to subsumption. • FL-, without the restr operator was shown to be more acceptable from a computational complexity perspective. A Formal Treatment of Subsumption

  13. Definition of FLN

  14. It can be seen that FLN allows the introduction of incoherent concepts: • More can be inferred from this example: Definition of FLN

  15. It is therefore necessary to consider the disjointness of role filler concepts. • This can still be handled in polynomial time, as there are n * (n -1) / 2 pairs of sub roles to consider. • There are other more complex situations to consider though... Definition of FLN

  16. These sub roles aren't pairwise disjoint but lead to incoherency when considered together. • This is likely an intractable problem • Even if it wasn't intractable, there are still no sound, complete, polynomial algorithms for subsumption Definition of FLN

  17. Subsumption in FLN can be compared to the complement of the SET-SPLITTING problem • SET-SPLITTING is proven NP-complete • SET-SPLITTING is defined as: • Given a collection C of subsets of a finite set S, is there a partition of S into two subsets S1 and S2 such that no subset in C is entirely contained in either S1 or S2. Proof of NP-hardness

  18. Given a special case subsumption problem: • SUBSUMES ((atleast 3 R, X) • X is a description containing atleasts and alls on sub roles of R • Consider a SET-SPLITTING problem with: • S = {s1, s2, … sn} • C = {c1, c2, … cm} • Each ci has the form: • Ci = {sf(i, 1), sf(i, 2), … sf(i, ||Ci||)} Formulation of the Problem

  19. This gives rise to an X of the form: • (and (atleast 1 (androle R Rprim1)) (all (androle RRprim1) π(s1))(atleast 1 (androle R Rprim2)) (all (androle RRprim2) π(s2))....(atleast 1 (androle R Rprimn)) (all (androle RRprimn) π(sn)) • Where π is a transformation function such that for each set Ci the conjuction of π(sf(i,k)) for 1 < k < ||Ci|| forms an incoherent concept Formulation of the Problem

  20. Under this formulation, this means that the corresponding sub roles can't be filled with the same instance. • However, if the subset of S doesn't contain a set Ci, then the sub roles can be filled with the same instance. • We then assume m roles in Ri corresponding to sets of Ci. Formulation of the Problem

  21. Where CPi,j is defined as: Formulation of the Problem

  22. This means that a conjunction of π(Sj) is incoherent iff for some role Ri we have more than ||Ci|| -1 different atleast restrictions • This results in the following analogy to the SET-SPLITTING problem: • If a role R of concept X can be filled with 2 or less role fillers, then there is a set splitting. • Else, a there is no set splitting. • Since this solves an instance of the SET-SPLITTING problem, subsumption in FLN is co-NP-hard. Formulation of the Problem

  23. This has some unfortunate consequences: • There are no complete, sound algorithms for subsumption on FDLs with this much expressive power that run in polynomial time. • We can improve this by reducing the expressiveness of the FDL: removing atleast, atmost and androle can help • We can settle on algorithms that are not complete instead, but tractable. • This is a common approach for AI algorithms Consequences

  24. To provide completeness, the expressiveness of the FDL must be limited: • Remove all operators relating roles • Alternatively, restrict these operators to some, none and unique • Weakening the semantics has the effect of reducing what inferences can be made • Even somewhat obvious relationships might be missed Consequences

  25. This gives us three ultimate choices: • A complete, sound algorithm: extremely slow • Weak semantics: might miss a lot of inferences • Strong semantics and incomplete algorithm: might miss some less obvious inferences • The approach depends on what's needed, but most practical systems would opt for the last option Consequences

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