4,208 research outputs found
Inductive Logic Programming in Databases: from Datalog to DL+log
In this paper we address an issue that has been brought to the attention of
the database community with the advent of the Semantic Web, i.e. the issue of
how ontologies (and semantics conveyed by them) can help solving typical
database problems, through a better understanding of KR aspects related to
databases. In particular, we investigate this issue from the ILP perspective by
considering two database problems, (i) the definition of views and (ii) the
definition of constraints, for a database whose schema is represented also by
means of an ontology. Both can be reformulated as ILP problems and can benefit
from the expressive and deductive power of the KR framework DL+log. We
illustrate the application scenarios by means of examples. Keywords: Inductive
Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid
Knowledge Representation and Reasoning Systems. Note: To appear in Theory and
Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables
Pac-Learning Recursive Logic Programs: Efficient Algorithms
We present algorithms that learn certain classes of function-free recursive
logic programs in polynomial time from equivalence queries. In particular, we
show that a single k-ary recursive constant-depth determinate clause is
learnable. Two-clause programs consisting of one learnable recursive clause and
one constant-depth determinate non-recursive clause are also learnable, if an
additional ``basecase'' oracle is assumed. These results immediately imply the
pac-learnability of these classes. Although these classes of learnable
recursive programs are very constrained, it is shown in a companion paper that
they are maximally general, in that generalizing either class in any natural
way leads to a computationally difficult learning problem. Thus, taken together
with its companion paper, this paper establishes a boundary of efficient
learnability for recursive logic programs.Comment: See http://www.jair.org/ for any accompanying file
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Information Acquisition and Refunds for Returns
A product exhibits personal fit uncertainty when its consumers have idiosyncratic and uncertain values for it. Often a consumer can learn her long-run value quickly by obtaining the good for a trial period. Money back guarantees of satisfaction are commonly used to lower the cost to consumers of learning their values this way. Increasingly, however, consumers can instead learn about their values before they purchase by, e.g., reading product reviews or consulting experts. We study the effect on a firm’s optimal price and refund of this competing source of information. An efficient outcome would be achieved by setting the refund for a return equal to its salvage value. But a monopoly will, for some parameters, induce consumers to stay uninformed by promising a refund that is greater than the salvage value. This generates an inefficiently large number of returns, which the firm finds worthwhile in order to eliminate the information rents that consumers would obtain by becoming informed. This finding is consistent with the observation that for many products, money back guarantees are generous, as they commonly refund the entire, or almost the entire, purchase price of a product.information acquisition, refunds, money back guarantees, personal fit uncertainty
Approaching the Problem of Time with a Combined Semiclassical-Records-Histories Scheme
I approach the Problem of Time and other foundations of Quantum Cosmology
using a combined histories, timeless and semiclassical approach. This approach
is along the lines pursued by Halliwell. It involves the timeless probabilities
for dynamical trajectories entering regions of configuration space, which are
computed within the semiclassical regime. Moreover, the objects that Halliwell
uses in this approach commute with the Hamiltonian constraint, H. This approach
has not hitherto been considered for models that also possess nontrivial linear
constraints, Lin. This paper carries this out for some concrete relational
particle models (RPM's). If there is also commutation with Lin - the Kuchar
observables condition - the constructed objects are Dirac observables.
Moreover, this paper shows that the problem of Kuchar observables is explicitly
resolved for 1- and 2-d RPM's. Then as a first route to Halliwell's approach
for nontrivial linear constraints that is also a construction of Dirac
observables, I consider theories for which Kuchar observables are formally
known, giving the relational triangle as an example. As a second route, I apply
an indirect method that generalizes both group-averaging and Barbour's best
matching. For conceptual clarity, my study involves the simpler case of
Halliwell 2003 sharp-edged window function. I leave the elsewise-improved
softened case of Halliwell 2009 for a subsequent Paper II. Finally, I provide
comments on Halliwell's approach and how well it fares as regards the various
facets of the Problem of Time and as an implementation of QM propositions.Comment: An improved version of the text, and with various further references.
25 pages, 4 figure
E-Generalization Using Grammars
We extend the notion of anti-unification to cover equational theories and
present a method based on regular tree grammars to compute a finite
representation of E-generalization sets. We present a framework to combine
Inductive Logic Programming and E-generalization that includes an extension of
Plotkin's lgg theorem to the equational case. We demonstrate the potential
power of E-generalization by three example applications: computation of
suggestions for auxiliary lemmas in equational inductive proofs, computation of
construction laws for given term sequences, and learning of screen editor
command sequences.Comment: 49 pages, 16 figures, author address given in header is meanwhile
outdated, full version of an article in the "Artificial Intelligence
Journal", appeared as technical report in 2003. An open-source C
implementation and some examples are found at the Ancillary file
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A survey of induction algorithms for machine learning
Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
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