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Our aim is to construct a perfect (i.e. minimal and optimal) ILP refinement operator for hypotheses spaces bounded below by a most specific clause and subject to syntactical restrictions in the form of input/output variable declarations (like in Progol). Since unfortunately no such optimal refinement operators exist, we settle for a weaker form of optimality and introduce an associated weaker form...
Divide-and-Conquer (DAC) and Separate-and-Conquer (SAC) are two strategies for rule induction that have been used extensively. When searching for rules DAC is maximally conservative w.r.t. decisions made during search for previous rules. This results in a very efficient strategy, which however suffers from difficulties in effectively inducing disjunctive concepts due to the replication problem. SAC...
Most ILP systems employ the covering algorithm whereby hypotheses are constructed iteratively clause by clause. Typically the covering algorithm is greedy in the sense that each iteration adds the best clause according to some local evaluation criterion. Some typical problems of the covering algorithm are: unnecessarily long hypotheses, difficulties in handling recursion, difficulties in learning...
In this paper, we present a new method based on nonmonotonic learning where the Inductive Logic Programming (ILP) algorithm is used twice and apply our method to acquire graphic design knowledge. Acquiring design knowledge is a challenging task because such knowledge is complex and vast. We thus focus on principles of layout and constraints that layouts must satisfy to realize automatic layout generation...
We consider the task of tagging Slovene words with morphosyntactic descriptions (MSDs). MSDs contain not only part-of-speech information but also attributes such as gender and case. In the case of Slovene there are 2,083 possible MSDs. P-Progol was used to learn morphosyntactic disambiguation rules from annotated data (consisting of 161,314 examples) produced by the MULTEXT-East project. P-Progol...
We present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life in water for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived...
In this paper we present 1BC, a first-order Bayesian Classifier. Our approach is to view individuals as structured terms, and to distinguish between structural predicates referring to subterms (e.g. atoms from molecules), and properties applying to one or several of these subterms (e.g. a bond between two atoms). We describe an individual in terms of elementary features consisting of zero or more...
Since its inception, the field of inductive logic programming has been centrally concerned with the use of background knowledge in induction. Yet, surprisingly, no serious attempts have been made to account for background knowledge in refinement operators for clauses, even though such operators are one of the most important, prominent and widely-used devices in the field. This paper shows how a sort...
A new IFLP schema is presented as a general framework for the induction of functional logic programs (FLP). Since narrowing (which is the most usual operational semantics of (FLP) performs a infication (mgu) followed by a replacement, we introduce two main operators in our IFLP schema: a generalisation and an inverse replacement or property of equality. We prove that this schema is strong complete...
From the point of view of computational linguistics, Hungarian is a difficult language due to its complex grammar and rich morphology. This means that even a common task such as part-of-speech tagging presents a new challenge for learning when looked at for the Hungarian language, especially given the fact that this language has fairly free word order. In this paper we therefore present a case study...
There is a history of research focussed on learning of shiftreduce parsers from syntactically annotated corpora by the means of machine learning techniques based on logic. The presence of lexical semantic tags in the treebank has proved useful for learning semantic constraints which limit the amount of nondeterminism in the parsers. The level of generality of the semantic tags used is of direct importance...
In our previous work we introduced a hybrid, GA&ILP-based approach for learning of stem-suffix segmentation rules from an unmarked list of words. Evaluation of the method was made difficult by the lack of word corpora annotated with their morphological segmentation. Here the hybrid approach is evaluated indirectly, on the task of tag prediction. A pair of stem-tag and suffix-tag lexicons is obtained...
This paper presents an application of Inductive Logic Programming (ILP) and Backpropagation Neural Network (BNN) to the problem of Thai character recognition. In such a learning problem, there exist several different classes of examples; there are 77 different Thai characters. Using examples constructed from character images, ILP learns 77 rules each of which defines each character. However, some...
Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and subgroup...
This paper reports the ongoing work of producing a state of the art part of speech tagger for unedited Swedish text. Rules eliminating faulty tags have been induced using Progol. In previously reported experiments, almost no linguistically motivated background knowledge was used [5,8]. Still, the result was rather promising (recall 97.7%, with a pending average ambiguity of 1.13 tags/word). Compared...
Shinohara, Arimura, and Krishna Rao have shown learnability in the limit of minimal models of classes of logic programs from positive only data. In most cases, these results involve logic programs in which the “size” of the head yields a bound on the size of the body literals. However, when local variables are present, such a bound on body literal size cannot directly be ensured. The above authors...
In this paper the problem of induction of clausal theories through a search space consisting of theories is studied. We order the search space by an extension of θ-subsumption for theories, and find a least generalization and a greatest specialization of theories. A most specific theory is introduced, and we develop a refinement operator bounded by this theory.
We present a method for discovering new knowledge from structural data which are represented by graphs in the framework of inductive logic programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. Formal Graph System (FGS) is a kind of logic programming system which directly deals with graphs just like...
Inductive Logic Programming (ILP) involves constructing an hypothesis H on the basis of background knowledge B and training examples E. An independent test set is used to evaluate the accuracy of H. This paper concerns an alternative approach called Analogical Prediction (AP). AP takes B, E and then for each test example ???x, y??? forms an hypothesis Hx from B, E, x. Evaluation of AP is...
Inductive Logic Programming considers almost exclusively universally quantified theories. To add expressiveness we should consider general prenex conjunctive normal forms (PCNF) with existential variables. ILP mostly uses learning with refinement operators. To extend refinement operators to PCNF, we should first extend substitutions to PCNF. If one substitutes an existential variable in a formula,...
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