N. Di Mauro

Machine learning methods for automatically processing historical documents: from paper acquisition to XML transformation

One of the aims of the EU project COLLATE is to design and implement a Web-based collaboratory for archives, scientists and end-users working with digitized cultural material. Since the originals of such a material are often unique and scattered in various archives, severe problems arise for their wide fruition. A solution would be to develop intelligent document processing tools that automatically transform printed documents into a Web-accessible form such as XML. Here, we propose the use of a document processing system, WISDOM++, which uses heavily machine learning techniques in order to perform such a task, and report promising results obtained in preliminary experiments.

A General Similarity Framework for Horn Clause Logic

First-Order Logic formulae are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.

Plugging Numeric Similarity in First-Order Logic Horn Clauses Comparison

Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient. This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions. The viability of the solution is demonstrated on sample problems.

Automatic topics identification for reviewer assignment

Scientific conference management involves many complex and multi-faceted activities, which would make highly desirable for the organizing people to have a Web-based management system that makes some of them a little easier to carry out. One of such activities is the assignment of submitted papers to suitable reviewers, involving the authors, the reviewers and the conference chair. Authors that submit the papers usually must fill a form with paper title, abstract and a set of conference topics that fit their submission subject. Reviewers are required to register and declare their expertise on the conference topics (among other things). Finally, the conference chair has to carry out the review assignment taking into account the information provided by both the authors (about their paper) and the reviewers (about their competencies). Thus, all this subtasks needed for the assignment are currently carried out manually by the actors. While this can be just boring in the case of authors and reviewers, in case of conference chair the task is also very complex and time-consuming. In this paper we propose the exploitation of intelligent techniques to automatically extract paper topics from their title and abstract, and the expertise of the reviewers from the titles of their publications available on the Internet. Successively, such a knowledge is exploited by an expert system able to automatically perform the assignments. The proposed methods were evaluated on a real conference dataset obtaining good results when compared to handmade ones, both in terms of quality and user-satisfaction of the assignments, and for reduction in execution time with respect to the case of humans performing the same process.

Incremental Learning of First Order Logic Theories for the Automatic Annotations of Web Documents

Organizing large repositories spread throughout the most diverse Web sites rises the problem of effective storage and efficient retrieval of documents. This can be obtained by selectively extracting from them the significant textual information, contained in peculiar layout components, that in turn depend on the identification of the correct document class. The continuous flow of new and different documents in a weakly structured environment like the Web calls for in- crementality, as the ability to continuously update or revise a faulty knowledge previously acquired, while the need to express structural relations among layout components suggest the exploitation of a powerful and symbolic representation language. This paper proposes the application of incremental first-order logic learning techniques in the document layout preprocessing steps, supported by good results obtained in experiments on a real dataset.

k-Nearest Neighbor Classification on First-Order Logic Descriptions

Classical attribute-value descriptions induce a multi-dimensional geometric space. One way for computing the distance between descriptions in such a space consists in evaluating an Euclidean distance between tuples of coordinates. This is the ground on which a large part of the Machine Learning literature has built its methods and techniques. However, the complexity of some domains require the use of First-Order Logic as a representation language. Unfortunately, when First-Order Logic is considered, descriptions can have different length and multiple instance of predicates, and the problem of indeterminacy arises. This makes computation of the distance between descriptions much less straight forward, and hence prevents the use of traditional distance-based techniques. This paper proposes the exploitation of a novel framework for computing the similarity between relational descriptions in a classical instance-based learning technique, k-Nearest Neighbor classification. Experimental results on real-world datasets show good performance, comparable to that of state-of-the-art conceptual learning systems, which supports the viability of the proposal.

θ-Subsumption and Resolution: A New Algorithm

Efficiency of the first-order logic proof procedure is a major issue when deduction systems are to be used in real environments, both on their own and as a component of larger systems (e.g., learning systems). This paper proposes a new θ-subsumption algorithm that is able to return the set of all substitutions by which such a relation holds between two clauses without performing backtracking. Differently from others proposed in the literature, it can be extended to perform resolution, also in theories containing recursive clauses.

Generalization-based similarity for conceptual clustering

Knowledge extraction represents an important issue that concerns the ability to identify valid, potentially useful and understandable patterns from large data collections. Such a task becomes more difficult if the domain of application cannot be represented by means of an attribute-value representation. Thus, a more powerful representation language, such as First-Order Logic, is necessary. Due to the complexity of handling First-Order Logic formulae, where the presence of relations causes various portions of one description to be possibly mapped in different ways onto another description, few works presenting techniques for comparing descriptions are available in the literature for this kind of representations. Nevertheless, the ability to assess similarity between first-order descriptions has many applications, ranging from description selection to flexible matching, from instance-based learning to clustering. This paper tackles the case of Conceptual Clustering, where a new approach to similarity evaluation, based on both syntactic and semantic features, is exploited to support the task of grouping together similar items according to their relational description. After presenting a framework for Horn Clauses (including criteria, a function and composition techniques for similarity assessment), classical clustering algorithms are exploited to carry out the grouping task. Experimental results on real-world datasets prove the effectiveness of the proposal.

Inference of abduction theories for handling incompleteness in first-order learning

In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms. Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive representations which require more complex inference mechanisms. However, the applicability of such new and complex inference mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain. This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming information in order to handle cases of missing knowledge.

Evaluation and Validation of Two Approaches to User Profiling

In the Internet era, huge amounts of data are available to everybody, in every place and at any moment. Searching for relevant information can be overwhelming, thus contributing to the user’s sense of information overload. Building systems for assisting users in this task is often complicated by the difficulty in articulating user interests in a structured form – a profile – to be used for searching. Machine learning methods offer a promising approach to solve this problem. Our research focuses on supervised methods for learning user profiles which are predictively accurate and comprehensible.