Combining Logic and Probabilities for Discovering Mappings between Taxonomies

Abstract. In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Alignment of multi-cultural knowledge repositories

The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.Die Fähigkeit mehrere Wissensquellen in einer Anwendung miteinander zu verbinden ist ein wichtiger Bestandteil für verschiedene Anwendungsszenarien wie z.B. dem Auffinden von Dokumenten und der Beantwortung von Fragen. Unabhängig erstellte Datenquellen sind allerdings von Natur aus heterogen, was ihre unterschiedlichen Herkünfte widerspiegelt. Somit besteht ein Bedarf darin, die Konzepte und Entitäten zwischen den Wissensquellen anzugleichen. Frühere Arbeiten sind jedoch auf Datenquellen limitiert, die eine hohe Ähnlichkeit im Sinne von Struktur und Terminologie aufweisen. Das Ziel dieser Dissertation ist, Methoden für Aufbau und Pflege zum Angleich zwischen multikulturellen Wissensquellen zu entwickeln. Der erste Beitrag ist ein System names ACROSS, das auf die Reduzierung der terminologischen Kluft zwischen den Datenquellen abzielt. Der zweite Beitrag sind die Systeme LILIANA und SESAME, welche zum Angleich eben dieser Datenquellen unter Berücksichtigung deren struktureller Unterschiede dienen. Der dritte Beitrag ist ein Verfahren names LAIKA, das den Angleich dynamischer Quellen unterstützt. Unsere Experimente mit einer Reihe von Wissensquellen in Größenordnung des Web zeigen eine hohe Qualität unserer Verfahren. Zudem werden die Vorteile in der Verwendung von LILIANA und SESAME in Anwendungsszenarien für Suche und Exploration dargelegt

omap: An implemented framework for automatically aligning owl ontologies

Abstract. This paper introduces oMAP, a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Terminological, machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by the ontology alignment evaluation initiative (OAEI). We provide the results of this evaluation for the various contests with respect to the other competitors

Personalizing Interactions with Information Systems

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains

A multi-matching technique for combining similarity measures in ontology integration

Ontology matching is a challenging problem in many applications, and is a major issue for interoperability in information systems. It aims to find semantic correspondences between a pair of input ontologies, which remains a labor intensive and expensive task. This thesis investigates the problem of ontology matching in both theoretical and practical aspects and proposes a solution methodology, called multi-matching . The methodology is validated using standard benchmark data and its performance is compared with available matching tools. The proposed methodology provides a framework for users to apply different individual matching techniques. It then proceeds with searching and combining the match results to provide a desired match result in reasonable time. In addition to existing applications for ontology matching such as ontology engineering, ontology integration, and exploiting the semantic web, the thesis proposes a new approach for ontology integration as a backbone application for the proposed matching techniques. In terms of theoretical contributions, we introduce new search strategies and propose a structure similarity measure to match structures of ontologies. In terms of practical contribution, we developed a research prototype, called MLMAR - Multi-Level Matching Algorithm with Recommendation analysis technique, which implements the proposed multi-level matching technique, and applies heuristics as optimization techniques. Experimental results show practical merits and usefulness of MLMA

A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem

Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot