101.
Incorporating Linguistics Constraints into Inductive Logic Programming
- James Cussens,Stephen Pulman
We report work on effectively incorporating linguistic
knowledge into grammar induction. We
use a highly interactive bottom-up inductive
logic programming (ILP) algorithm to learn
`missing' grammar rules from an incomplete
grammar. Using linguistic constraints on, for
example, head features and gap threading, reduces
the search space to such an extent that,
in the small-scale experiments reported here,
we can generate and store all candidate grammar
rules together with information about their
coverage and linguistic properties. This allows
an appealingly simple and controlled method
for generating linguistically plausible grammar
rules. Starting from a base of highly specific
rules, we apply least general generalisation
and inverse resolution to generate more general
rules. Induced rules are ordered, for example by
coverage,...
102.
Incorporating Linguistics Constraints into Inductive Logic Programming
- James Cussens,Stephen Pulman
We report work on effectively incorporating lin-
guistic knowledge into grammar induction. We
use a highly interactive bottom-up inductive
logic programming (ILP) algorithm to learn
'missing' grammar rules from an :incomplete
grammar. Using linguistic constraints on, for
example, head features and gap threading, reduces
the search space to such an extent that,
in the small-scale experiments reported here,
we can generate and store all candidate grammar
rules together with information about their
coverage and linguistic properties. This allows
an appealingly simple and controlled method
for generating linguistically plausible grammar
rules. Starting from a base of highly specific
rules, we apply least general generalisation
and inverse resolution to generate more general
rules. Induced rules are ordered, for example...
103.
Extracting context-sensitive models in Inductive Logic Programming
- Ashwin Srinivasan
Given domain-specific background knowledge and data in the form of examples, an Inductive Logic Programming (ILP) system extracts models in the data-analytic sense. We view the model-selection step facing an ILP system as a decision problem, the solution of which requires knowledge of the context in which the model is to be deployed. In this paper, "context" will be defined by the current specification of the prior class distribution and the client's preferences concerning errors of classification. Within this restricted setting, we consider the use of an ILP system in situations where: (a) contexts can change regularly. This can arise...
104.
Biochemical knowledge discovery using Inductive Logic Programming
- Stephen Muggleton,Ashwin Srinivasan,R. D. King
Machine Learning algorithms are being increasingly used for
knowledge discovery tasks. Approaches can be broadly divided by distinguishing
discovery of procedural from that of declarative knowledge.
Client requirements determine which of these is appropriate. This paper
discusses an experimental application of machine learning in an area
related to drug design. The bottleneck here is in finding appropriate
constraints to reduce the large number of candidate molecules to be
synthesised and tested. Such constraints can be viewed as declarative
specifications of the structural elements necessary for high medicinal activity
and low toxicity. The first-order representation used within Inductive
Logic Programming (ILP) provides an appropriate description
language for such constraints. Within this application...
105.
Inductive Logic Programming for Discovering Financial Regularities
- Boris Kovalerchuk,Evgenii Vityaev
The purpose of this work is discovering regularities in financial time series using Inductive Logic Programming (ILP) and related "Discovery" software system [Vityaev et al., 1992,1993] in data mining. Discovered regularities were used for forecasting the target variable, representing the relative difference in percent between today's closing price and the price five days ahead. We describe the method, types of regularities found and analyzed, statistical characteristics of these regularities on the training and test data and the percentage of true and false predictions on the test data. There are more than 130 discovered regularities on 10 year (1985-1994) data. The...
106.
Using Inductive Logic Programming for Natural Language Processing
- James Cussens,Stephen Muggleton,Ashwin Srinivasan
We summarise recent work on using Inductive Logic Programming (ILP)
for Natural Language Processing (NLP). ILP performs learning in a first-order
logical setting, and is thus well-suited to induce over the various structured
representations used in NLP. We present Stochastic Logic Programs (SLPs)
and demonstrate their use in ILP when learning from positive examples only.
We also give accounts of work on learning grammars from children's books
and part-of-speech tagging.
1 Inductive Logic Programming and Progol
By using computational logic as the representational mechanism for hypotheses and
observations, Inductive Logic Programming (ILP) can overcome the two main limitations
of classical machine learning techniques, such as the Top-Down-Inductionof
-Decision-Tree (TDIDT) family [9]):
1....
107.
Using Inductive Logic Programming for Natural Language Processing
- James Cussens,Stephen Muggleton,Ashwin Srinivasan
We summarise recent work on using Inductive Logic Programming
(ILP) for Natural Language Processing (NLP). ILP performs learning in
a first-order logical setting, and is thus well-suited to induce over the
various structured representations used in NLP. We present Stochastic
Logic Programs (SLPs) and demonstrate their use in ILP when learning
from positive examples only. We also give accounts of work on learning
grammars from children's books and part-of-speech tagging.
1 Inductive Logic Programming and Progol
By using computational logic as the representational mechanism for hypotheses
and observations, Inductive Logic Programming (ILP) can overcome the two
main limitations of classical machine learning techniques, such as the Top-DownInduction
-of-Decision-Tree (TDIDT) family [9]):
1....
108.
Biases and their Effects in Inductive Logic Programming
- Birgit Tausend,Fakultat Informatik
In inductive learning, the shift of the representation language of the
hypotheses from attribute-value languages to Horn clause logic used by
Inductive Logic Programming systems accounts for a very complex hypothesis
space. In order to reduce this complexity, most of these systems
use biases. In this paper, we study the influence of these biases on the size
of the hypothesis space. For this comparison, we first identify the basic
constituents the biases are combined of. Then, we discuss the restrictions
set on the distribution of terms in the clause by the constituents of the bias.
The effects of several constituents and of some combinations are shown by
seven experiments.
1...
109.
Biochemical knowledge discovery using Inductive Logic Programming
- Stephen Muggleton,Ashwin Srinivasan,R. D. King,M. J. E. Sternberg
Machine Learning algorithms are being increasingly used for knowledge discovery tasks.
Approaches can be broadly divided by distinguishing discovery of procedural from that of
declarative knowledge. Client requirements determine which of these is appropriate. This
paper discusses an experimental application of machine learning in an area related to drug
design. The bottleneck here is in finding appropriate constraints to reduce the large number of
candidate molecules to be synthesisedand tested. Such constraints canbe viewed as declarative
specifications of the structural elements necessary for high medicinal activity and low toxicity.
The first-order representation used within Inductive Logic Programming (ILP) provides an
appropriate description language for such constraints. Within this...
110.
An intelligent search method using Inductive Logic Programming
- Nobuhiro Inuzuka,Hirohisa Seki,Hidenori Itoh
We propose a method to use Inductive Logic
Programming to give heuristic functions for
searching goals to solve problems. The method
takes solutions of a problem or a history of
search and a set of background knowledge on
the problem. In a large class of problems, a
problem is described as a set of states and a set
of operators, and is solved by finding a series of
operators. A solution, a series of operators that
brings an initial state to a final state, is transformed
into positive and negative examples of
a relation "better-choice", which describes that
an operator is better than others in a state. We
also give a way to...
111.
An intelligent search method using Inductive Logic Programming
- Nobuhiro Inuzuka,Hirohisa Seki,Hidenori Itoh
We propose a method to use Inductive Logic
Programming to give heuristic functions for
searching goals to solve problems. The method
takes solutions of a problem or a history of
search and a set of background knowledge on
the problem. In a large class of problems, a
problem is described as a set of states and a set
of operators, and is solved by finding a series of
operators. A solution, a series of operators that
brings an initial state to a final state, is transformed
into positive and negative examples of
a relation "better-choice", which describes that
an operator is better than others in a state. We
also give a way to...
112.
Computational Logic and Machine Learning: A roadmap for Inductive Logic Programming
- Nada Lavrac
Computational logic has already significantly influenced (symbolic) machine
learning through the field of inductive logic programming (ILP) which is
concerned with the induction of logic programs from examples and background
knowledge. In ILP, the shift of attention from program synthesis to
knowledge discovery resulted in advanced techniques that are practically applicable
for discovering knowledge in relational databases. Machine learning,
and ILP in particular, has the potential to influence computational logic by
providing an application area full of industrially significant problems, thus
providing a challenge for other techniques in computational logic. This paper
gives a brief introduction to ILP, presents state-of-the-art ILP techniques for
relational knowledge discovery as well as some...
113.
Inductive Logic Programming for Corpus-Based Acquisition of Semantic Lexicons
- Pascale S Ebillot,Pierrette Bouillon
In this paper, we propose an Inductive Logic
Programming learning method which aims at
automatically extracting special Noun-Verb (NV)
pairs from a corpus in order to build up
semantic lexicons based on Pustejovsky's Generative
Lexicon (GL) principles (Pustejovsky,
1995). In one of the components of this lexical
model, called the qualia structure, words
are described in terms of semantic roles. For
example, the telic role indicates the purpose or
function of an item (cut for knife), the agentive
role its creation mode (build for house),
etc. The qualia structure of a noun is mainly
made up of verbal associations, encoding relational
information. The Inductive Logic Programming
learning method that we have developed
enables us to automatically...
114.
Inductive Logic Programming for Corpus-Based Acquisition of Semantic Lexicons
- Pierrette Bouillon
In this paper, we propose an Inductive Logic
Programming learning method which aims at
automatically extracting special Noun-Verb (NV)
pairs from a corpus in order to build up
semantic lexicons based on Pustejovsky's Generative
Lexicon (GL) principles (Pustejovsky,
1995). In one of the components of this lexical
model, called the qualia structure, words
are described in terms of semantic roles. For
example, the telic role indicates the purpose or
function of an item (cut for knife), the agentive
role its creation mode (build for house),
etc. The qualia structure of a noun is mainly
made up of verbal associations, encoding relational
information. The Inductive Logic Programming
learning method that we have developed
enables us to automatically...
115.
Polynomial Learnability and Inductive Logic Programming: Methods and Results
- William W. Cohen,C. David
Over the last few years, the efficient learnability of logic programs has been studied
extensively. Positive and negative learnability results now exist for a number of restricted
classes of logic programs that are closely related to the classes used in practice
within inductive logic programming. This paper surveys these results, and also introduces
some of the more useful techniques for deriving such results. The paper does not
assume any prior background in computational learning theory.
0
1 Introduction
As noted by Stephen Muggleton in a recent invited talk on inductive logic programming
(ILP) [Muggleton, 1994a, Muggleton, 1994b], "ILP is based on the lock-step development
of Theory, Implementations, and Applications." The...
116.
Inductive Logic Programming for Nuclear Power Plants Monitoring
- Claire Nicolini,Francois Jacquenet,Marc Bernard
Nuclear power plants monitoring is a very tricky task
because there is no formal model of the plants when a
malfunction occurs. The experts of a plant have some
knowledge on the management of the plant but it is
very difficult for us to explain what may happen in such
or such configuration of the plant. In this paper, we
use a new learning technique to model the malfunction
of a nuclear plant. We use Inductive Logic Programming
to induce some logic programs that model the
usual malfunctions of the plant and may be used later
to diagnose and correct a malfunction on the plant.
Keywords : Inductive Logic Programming, Machine
Learning,...
117.
Computational Logic and Machine Learning: A roadmap for Inductive Logic Programming
- Nada Lavrac
Computational logic has already significantly influenced (symbolic) machine
learning through the field of inductive logic programming (ILP) which is
concerned with the induction of logic programs from examples and background
knowledge. In ILP, the shift of attention from program synthesis to
knowledge discovery resulted in advanced techniques that are practically applicable
for discovering knowledge in relational databases. Machine learning,
and ILP in particular, has the potential to influence computational logic by
providing an application area full of industrially significant problems, thus
providing a challenge for other techniques in computational logic. This paper
gives a brief introduction to ILP, presents state-of-the-art ILP techniques for
relational knowledge discovery as well as some...
118.
Computational Logic and Machine Learning: A roadmap for Inductive Logic Programming
- Nada Lavrac
Computational logic has already significantly influenced (symbolic) machine
learning through the field of inductive logic programming (ILP) which is
concerned with the induction of logic programs from examples and background
knowledge. In ILP, the shift of attention from program synthesis to
knowledge discovery resulted in advanced techniques that are practically applicable
for discovering knowledge in relational databases. Machine learning,
and ILP in particular, has the potential to influence computational logic by
providing an application area full of industrially significant problems, thus
providing a challenge for other techniques in computational logic. This paper
gives a brief introduction to ILP, presents state-of-the-art ILP techniques for
relational knowledge discovery as well as some...
119.
Polynomial Learnability and Inductive Logic Programming: Methods and Results
- William W. Cohen,C. David
Over the last few years, the ecient learnability of logic programs has been studied
extensively. Positive and negative learnability results now exist for a number of restricted
classes of logic programs that are closely related to the classes used in practice
within inductive logic programming. This paper surveys these results, and also introduces
some of the more useful techniques for deriving such results. The paper does not
assume any prior background in computational learning theory.
120.
Inductive Logic From Data Analysis to Experimental Design
- Kevin H. Knuth
In celebration of the work of Richard Threlkeld Cox, we explore inductive logic and its role in science touching on both experimental design and analysis of experimental results. In this exploration we demonstrate that the duality between the logic of assertions and the logic of questions has important consequences. We discuss the conjecture that the relevance or bearing, b, of a question on an issue can be expressed in terms of the probabilities, p, of the assertions that answer the question via the entropy. In its application to the scientific method, the logic of questions, inductive inquiry, can be applied...
