Using Description Logics for Recognising Textual Entailment

The aim of this paper is to show how we can handle the Recognising Textual Entailment (RTE) task by using Description Logics (DLs). To do this, we propose a representation of natural language semantics in DLs inspired by existing representations in first-order logic. But our most significant contribution is the definition of two novel inference tasks: A-Box saturation and subgraph detection which are crucial for our approach to RTE

An Event-Ontology-Based Approach to Constructing Episodic Knowledge from Unstructured Text Documents

Document summarization is an important function for knowledge management when a digital library of text documents grows. It allows documents to be presented in a concise manner for easy reading and understanding. Traditionally, document summarization adopts sentence-based mechanisms that identify and extract key sentences from long documents and assemble them together. Although that approach is useful in providing an abstract of documents, it cannot extract the relationship or sequence of a set of related events (also called episodes). This paper proposes an event-oriented ontology approach to constructing episodic knowledge to facilitate the understanding of documents. We also empirically evaluated the proposed approach by using instruments developed based on Bloom’s Taxonomy. The result reveals that the approach based on proposed event-oriented ontology outperformed the traditional text summarization approach in capturing conceptual and procedural knowledge, but the latter was still better in delivering factual knowledge

POLIS: a probabilistic summarisation logic for structured documents

PhDAs the availability of structured documents, formatted in markup languages such as SGML, RDF, or XML, increases, retrieval systems increasingly focus on the retrieval of document-elements, rather than entire documents. Additionally, abstraction layers in the form of formalised retrieval logics have allowed developers to include search facilities into numerous applications, without the need of having detailed knowledge of retrieval models. Although automatic document summarisation has been recognised as a useful tool for reducing the workload of information system users, very few such abstraction layers have been developed for the task of automatic document summarisation. This thesis describes the development of an abstraction logic for summarisation, called POLIS, which provides users (such as developers or knowledge engineers) with a high-level access to summarisation facilities. Furthermore, POLIS allows users to exploit the hierarchical information provided by structured documents. The development of POLIS is carried out in a step-by-step way. We start by defining a series of probabilistic summarisation models, which provide weights to document-elements at a user selected level. These summarisation models are those accessible through POLIS. The formal definition of POLIS is performed in three steps. We start by providing a syntax for POLIS, through which users/knowledge engineers interact with the logic. This is followed by a definition of the logics semantics. Finally, we provide details of an implementation of POLIS. The final chapters of this dissertation are concerned with the evaluation of POLIS, which is conducted in two stages. Firstly, we evaluate the performance of the summarisation models by applying POLIS to two test collections, the DUC AQUAINT corpus, and the INEX IEEE corpus. This is followed by application scenarios for POLIS, in which we discuss how POLIS can be used in specific IR tasks

A Preliminary Ontology for Spermatozoa Analysis

Biomedical computation has used ontologies as a foundation for building knowledge-based systems and technologies for SemanticWeb applications. However, so far they had not been utilized in andrology related domains. This paper presents a preliminary effort to provide a comprehensive ontology for classifying and managing spermatozoa samples and their phenotypic traits, in order to analyze and diagnose them. Our study is centered around the development of a Computer Asisted Sperm Analysis (CASA) system.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A Preliminary Ontology for Spermatozoa Analysis

Biomedical computation has used ontologies as a foundation for building knowledge-based systems and technologies for SemanticWeb applications. However, so far they had not been utilized in andrology related domains. This paper presents a preliminary effort to provide a comprehensive ontology for classifying and managing spermatozoa samples and their phenotypic traits, in order to analyze and diagnose them. Our study is centered around the development of a Computer Asisted Sperm Analysis (CASA) system.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Eine Übersicht über Information Retrieval (IR) und NLP-Verfahren zur Klassifikation von Texten

Die vorliegende Arbeit soll einen kurzen Überblick über gängige Ansätze aus dem Information Retrieval (IR) und der Natürlichsprachlichen Verarbeitung (NLP) zur Informationsextraktion geben. Diese Untersuchung wurde primär mit dem Ziel durchgeführt, statistische und wissensbasierte Techniken auf ihre Einsetzbarkeit zur Klassifikation von Texten zu evaluieren. Wir unterscheiden zwischen statistischen, regelbasierten, konzeptbasierten, probabilistischen sowie konnektionistischen Verfahren und stellen exemplarisch hierfür bekannte Systeme vor. Sowohl Information Retrieval- als auch NLP-Systeme gehen von korrekten ASCII-Texten als Eingabe aus. Diese Voraussetzung gilt jedoch in der Dokumentanalyse nicht. Nach dem optischen Abtasten eines Dokuments, der Strukturanalyse und der nachfolgenden Texterkennung treten Wortalternativen mit Erkennungswahrscheinlichkeiten auf, die bei der partiellen inhaltlichen Analyse, d. h. der Informationsextraktion aus Texten, berücksichtigt werden müssen. Deshalb gehen wir am Schluß der Arbeit darauf ein, inwieweit die oben genannten Verfahren prinzipiell auf die Dokumentanalyse übertragbar sind. Vorab soll betont werden, daß die vorliegende Studie zwei im Rahmen des ALV-Projektes am DFKI entwickelte Prototypen zur inhaltsbasierten Klassifikation von Dokumenten motiviert: einer verwendet statistische Methoden zur automatischen Indexierung; der andere beruht auf einem Regelinterpreter, der die bewerteten Worthypothesen als Evidenzen für Konzepte durch ein hierarchisches Netzwerk propagiert