Organizing Knowledge in a Complex Financial Domain

Mays, Eric; Apté, Chidanand; Griesmer, James H.; Kastner, John K.

doi:10.1109/mex.1987.4307092

Cited by 35 publications

(10 citation statements)

References 6 publications

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“…This DL was chosen because, although it is relatively simple, it nonetheless illustrates several of the more subtle issues involved in pac-learning more complex DLs. It is furthermore a subset of CLASSIC, a DL that has been implemented and used in several real-world applications (Beck et al, 1989;Devanbu et al, 1991;Mays et al, 1987;Schewe, 1989). Section 3 also contains several examples of CORECLASSIC descriptions, and a brief discussion of how CORECLASSIC differs from first-order representations based on definite clauses.…”

Section: Contributions Of the Papermentioning

confidence: 99%

“…CORECLASSIC is a subset of CLASSIC, a DL that has been implemented and used in several real-world applications (Beck et al, 1989;Devanbu et al, 1991;Mays et al, 1987;Schewe, 1989). We chose this subset because it accurately illustrates the complexities that arise in learning DLs: in particular, both the learning algorithm of Section 5 and the hardness results of Section 4 extend to full CLASSIC .…”

Section: The Coreclassic Description Logicmentioning

confidence: 99%

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The learnability of description logics with equality constraints

Cohen

Hirsh

1994

Mach Learn

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Abstract.Although there is an increasing amount of experimental research on learning concepts expressed in first-order logic, there are still relatively few formal results on the polynomial learnability of first-order representations from examples. Most previous analyses in the pac-model have focused on subsets of Prolog, and only a few highly restricted subsets have been shown to be learnable. In this paper, we will study instead the learnability of the restricted first-order logics known as "description logics", also sometimes called "terminological logics" or "KL-ONE-type languages". Description logics are also subsets of predicate calculus, but are expressed using a different syntax, allowing a different set of syntactic restrictions to be explored. We first define a simple description logic, summarize some results on its expressive power, and then analyze its learnability. It is shown that the full logic cannot be tractably learned. However, syntactic restrictions exist that enable tractable learning from positive examples alone, independent of the size of the vocabulary used to describe examples. The learnable sublanguage appears to be incomparable in expressive power to any subset of first-order logic previously known to be learnable.

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Section: Contributions Of the Papermentioning

confidence: 99%

Section: The Coreclassic Description Logicmentioning

confidence: 99%

The learnability of description logics with equality constraints

Cohen

Hirsh

1994

Mach Learn

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“…The former box functions like noun-phrase terms that can be referenced while interacting with the system, and the latter functions like propositional sentences that relate the terminological objects in T-box. This separation enormously influences subsequent research on system integration and knowledge representation (Arens et al, 1993;Knoblock, Yigal, and Hsu, 1994;Levy, Srivastava, and Kirt, 1995;Levy, Rajarman, and Ordille, 1996a;Mays, Tyler, McCuire, and Schlossberg, 1987). In our approach, terms appearing in T-box, including individuals, concepts, and roles, correspond respectively to the instance names, target concepts, and concept characteristics.…”

Section: Description Logicmentioning

confidence: 99%

Incorporating Generalized Quantifiers into Description Logic to Improve Source Selection

Madnick

Tu²

2001

SSRN Journal

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“…The former box functions like noun-phrase terms that can be referenced while interacting with the system, and the latter functions like propositional sentences that relate the terminological objects in T -box. This separation enormously influences subsequent research on system integration and knowledge representation (Arens, Chee, Hsu, and Knoblock 1993, Knoblock, Yigal, and Hsu, 1994, Levy, Srivastava, and Kirt, 1995, Mays, Tyler, McCuire, and Schlossberg, 1987. In our approach, terms appearing in T-box, including individuals, concepts, and roles, correspond respectively to the instance names, target concepts, and concept characteristics.…”

Section: Description Logicmentioning

confidence: 99%

Incorporating generalized quantifiers into description logic for representing data source contents

Madnick

1998

Data Mining and Reverse Engineering

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Systems for helping users to select data sources in an environment, such as the Internet, must be expressive enough to allow a variety of data sources to be formally represented. We build upon and extend the concept language, description logic (DL), to propose a novel representation system to achieve that goal. We point out that there are technical barriers within description logic limiting the types of data sources that can be represented. Specifically, we show that (1) DL is awkward in representing sufficient conditions, and (2) DL can describe properties of a concept itself only in the case of existential quantification. To be concrete, whereas it is easy in DL to say 'There are some objects in a concept C such that each of them holds the property P', it is awkward, if not impossible, to say 'All (Most, More-than-n, More-than-ll2) of those that hold the property P are in the concept C' or 'Most (More-than-n, More-than-II2) of those that are in the concept C hold the property P'. These barriers cause us to extend DL with the notion of generalized quantifiers. We improve the previous results of generalized quantifiers to make them inter-operable with traditional logic. The proposed formalism integrates the nice features of generalized quantifiers into description logic, and hence achieves more expressive power than various representation systems based purely on description logic. It is also shown that our proposed language preserves those mathematical properties that traditional logic-based formalisms are known to hold.

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Organizing Knowledge in a Complex Financial Domain

Cited by 35 publications

References 6 publications

The learnability of description logics with equality constraints

The learnability of description logics with equality constraints

Incorporating Generalized Quantifiers into Description Logic to Improve Source Selection

Incorporating generalized quantifiers into description logic for representing data source contents

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