A Review of Common Problems in Linguistic Resources and a New Way to Represent Ontological Relations

Existing lexical resources have taxonomic structure related problems that negatively impact the results of domain-specific applications. This is the result of an approach that focuses on implementation and content issues rather than on questions of design, semantic cleanness and application usefulness. Although taxonomy structuring methodologies have been developed to correct some of these problems, they remain too general and far from the problem-solving and domain-specific approach that ontology-based linguistic resources need in order to be problem-solving. Based on a short analysis of some common problems in lexical resources and of the available taxonomy structuring methodologies, we propose the use of a set of ideas that may be useful in the design and development of an application-oriented ontology-based linguistic resource, and help developers in refining the meaning of concepts and relations to avoid the common problems present in linguistic resources. In addition, we present an E-R model where these ideas have been integrated to better illustrate our proposal and enhance our methodology for the construction of ontology-based linguistic resources.Sociedad Argentina de Informática e Investigación Operativ

XML Schema Clustering with Semantic and Hierarchical Similarity Measures

With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis

Scalable and Declarative Information Extraction in a Parallel Data Analytics System

Informationsextraktions (IE) auf sehr großen Datenmengen erfordert hochkomplexe, skalierbare und anpassungsfähige Systeme. Obwohl zahlreiche IE-Algorithmen existieren, ist die nahtlose und erweiterbare Kombination dieser Werkzeuge in einem skalierbaren System immer noch eine große Herausforderung. In dieser Arbeit wird ein anfragebasiertes IE-System für eine parallelen Datenanalyseplattform vorgestellt, das für konkrete Anwendungsdomänen konfigurierbar ist und für Textsammlungen im Terabyte-Bereich skaliert. Zunächst werden konfigurierbare Operatoren für grundlegende IE- und Web-Analytics-Aufgaben definiert, mit denen komplexe IE-Aufgaben in Form von deklarativen Anfragen ausgedrückt werden können. Alle Operatoren werden hinsichtlich ihrer Eigenschaften charakterisiert um das Potenzial und die Bedeutung der Optimierung nicht-relationaler, benutzerdefinierter Operatoren (UDFs) für Data Flows hervorzuheben. Anschließend wird der Stand der Technik in der Optimierung nicht-relationaler Data Flows untersucht und herausgearbeitet, dass eine umfassende Optimierung von UDFs immer noch eine Herausforderung ist. Darauf aufbauend wird ein erweiterbarer, logischer Optimierer (SOFA) vorgestellt, der die Semantik von UDFs mit in die Optimierung mit einbezieht. SOFA analysiert eine kompakte Menge von Operator-Eigenschaften und kombiniert eine automatisierte Analyse mit manuellen UDF-Annotationen, um die umfassende Optimierung von Data Flows zu ermöglichen. SOFA ist in der Lage, beliebige Data Flows aus unterschiedlichen Anwendungsbereichen logisch zu optimieren, was zu erheblichen Laufzeitverbesserungen im Vergleich mit anderen Techniken führt. Als Viertes wird die Anwendbarkeit des vorgestellten Systems auf Korpora im Terabyte-Bereich untersucht und systematisch die Skalierbarkeit und Robustheit der eingesetzten Methoden und Werkzeuge beurteilt um schließlich die kritischsten Herausforderungen beim Aufbau eines IE-Systems für sehr große Datenmenge zu charakterisieren.Information extraction (IE) on very large data sets requires highly complex, scalable, and adaptive systems. Although numerous IE algorithms exist, their seamless and extensible combination in a scalable system still is a major challenge. This work presents a query-based IE system for a parallel data analysis platform, which is configurable for specific application domains and scales for terabyte-sized text collections. First, configurable operators are defined for basic IE and Web Analytics tasks, which can be used to express complex IE tasks in the form of declarative queries. All operators are characterized in terms of their properties to highlight the potential and importance of optimizing non-relational, user-defined operators (UDFs) for dataflows. Subsequently, we survey the state of the art in optimizing non-relational dataflows and highlight that a comprehensive optimization of UDFs is still a challenge. Based on this observation, an extensible, logical optimizer (SOFA) is introduced, which incorporates the semantics of UDFs into the optimization process. SOFA analyzes a compact set of operator properties and combines automated analysis with manual UDF annotations to enable a comprehensive optimization of data flows. SOFA is able to logically optimize arbitrary data flows from different application areas, resulting in significant runtime improvements compared to other techniques. Finally, the applicability of the presented system to terabyte-sized corpora is investigated. Hereby, we systematically evaluate scalability and robustness of the employed methods and tools in order to pinpoint the most critical challenges in building an IE system for very large data sets

Automatic reconstruction of itineraries from descriptive texts

Esta tesis se inscribe dentro del marco del proyecto PERDIDO donde los objetivos son la extracción y reconstrucción de itinerarios a partir de documentos textuales. Este trabajo se ha realizado en colaboración entre el laboratorio LIUPPA de l' Université de Pau et des Pays de l' Adour (France), el grupo de Sistemas de Información Avanzados (IAAA) de la Universidad de Zaragoza y el laboratorio COGIT de l' IGN (France). El objetivo de esta tesis es concebir un sistema automático que permita extraer, a partir de guías de viaje o descripciones de itinerarios, los desplazamientos, además de representarlos sobre un mapa. Se propone una aproximación para la representación automática de itinerarios descritos en lenguaje natural. Nuestra propuesta se divide en dos tareas principales. La primera pretende identificar y extraer de los textos describiendo itinerarios información como entidades espaciales y expresiones de desplazamiento o percepción. El objetivo de la segunda tarea es la reconstrucción del itinerario. Nuestra propuesta combina información local extraída gracias al procesamiento del lenguaje natural con datos extraídos de fuentes geográficas externas (por ejemplo, gazetteers). La etapa de anotación de informaciones espaciales se realiza mediante una aproximación que combina el etiquetado morfo-sintáctico y los patrones léxico-sintácticos (cascada de transductores) con el fin de anotar entidades nombradas espaciales y expresiones de desplazamiento y percepción. Una primera contribución a la primera tarea es la desambiguación de topónimos, que es un problema todavía mal resuelto dentro del reconocimiento de entidades nombradas (Named Entity Recognition - NER) y esencial en la recuperación de información geográfica. Se plantea un algoritmo no supervisado de georreferenciación basado en una técnica de clustering capaz de proponer una solución para desambiguar los topónimos los topónimos encontrados en recursos geográficos externos, y al mismo tiempo, la localización de topónimos no referenciados. Se propone un modelo de grafo genérico para la reconstrucción automática de itinerarios, donde cada nodo representa un lugar y cada arista representa un camino enlazando dos lugares. La originalidad de nuestro modelo es que además de tener en cuenta los elementos habituales (caminos y puntos del recorrido), permite representar otros elementos involucrados en la descripción de un itinerario, como por ejemplo los puntos de referencia visual. Se calcula de un árbol de recubrimiento mínimo a partir de un grafo ponderado para obtener automáticamente un itinerario bajo la forma de un grafo. Cada arista del grafo inicial se pondera mediante un método de análisis multicriterio que combina criterios cualitativos y cuantitativos. El valor de estos criterios se determina a partir de informaciones extraídas del texto e informaciones provenientes de recursos geográficos externos. Por ejemplo, se combinan las informaciones generadas por el procesamiento del lenguaje natural como las relaciones espaciales describiendo una orientación (ej: dirigirse hacia el sur) con las coordenadas geográficas de lugares encontrados dentro de los recursos para determinar el valor del criterio ``relación espacial''. Además, a partir de la definición del concepto de itinerario y de las informaciones utilizadas en la lengua para describir un itinerario, se ha modelado un lenguaje de anotación de información espacial adaptado a la descripción de desplazamientos, apoyándonos en las recomendaciones del consorcio TEI (Text Encoding and Interchange). Finalmente, se ha implementado y evaluado las diferentes etapas de nuestra aproximación sobre un corpus multilingüe de descripciones de senderos y excursiones (francés, español, italiano)

Information-theoretic causal inference of lexical flow

This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. A flow-based separation criterion and domain-specific directionality detection criteria are developed to make existing causal inference algorithms more robust against imperfect cognacy data, giving rise to two new algorithms. The Phylogenetic Lexical Flow Inference (PLFI) algorithm requires lexical features of proto-languages to be reconstructed in advance, but yields fully general phylogenetic networks, whereas the more complex Contact Lexical Flow Inference (CLFI) algorithm treats proto-languages as hidden common causes, and only returns hypotheses of historical contact situations between attested languages. The algorithms are evaluated both against a large lexical database of Northern Eurasia spanning many language families, and against simulated data generated by a new model of language contact that builds on the opening and closing of directional contact channels as primary evolutionary events. The algorithms are found to infer the existence of contacts very reliably, whereas the inference of directionality remains difficult. This currently limits the new algorithms to a role as exploratory tools for quickly detecting salient patterns in large lexical datasets, but it should soon be possible for the framework to be enhanced e.g. by confidence values for each directionality decision

Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-Ranking and Adaptive Learning and Extension

The goal of this research project is the realization of an artificial intelligence-driven lightweight domain knowledge search framework that returns a domain knowledge structure upon request with highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology learning models. The re-ranking algorithm, a necessary mechanism to counter-play the heterogeneity and unstructured nature of web data, uses augmented queries and a hierarchical taxonomic structure to get further insight into the initial search results obtained from credited generic search engines. A semantic weight scale is applied to each node in the ontology graph and in turn generates a matrix of aggregated link relation scores that is used to compute the likely semantic correspondence between nodes and documents. Bootstrapped with a light-weight seed domain ontology, the theoretical platform focuses on the core back-end building blocks, employing two supervised automated learning models as well as semi-automated verification processes to progressively enhance, prune, and inspect the domain ontology to formulate a growing, up-to-date, and veritable system.\\ The framework provides an in-depth knowledge search platform and enhances user knowledge acquisition experience. With minimum footprint, the system stores only necessary metadata of possible domain knowledge searches, in order to provide fast fetching and caching. In addition, the re-ranking and ontology learning processes can be operated offline or in a preprocessing stage, the system therefore carries no significant overhead at runtime

Graph-based methods for large-scale multilingual knowledge integration

Given that much of our knowledge is expressed in textual form, information systems are increasingly dependent on knowledge about words and the entities they represent. This thesis investigates novel methods for automatically building large repositories of knowledge that capture semantic relationships between words, names, and entities, in many different languages. Three major contributions are made, each involving graph algorithms and statistical techniques that combine evidence from multiple sources of information. The lexical integration method involves learning models that disambiguate word meanings based on contextual information in a graph, thereby providing a means to connect words to the entities that they denote. The entity integration method combines semantic items from different sources into a single unified registry of entities by reconciling equivalence and distinctness information and solving a combinatorial optimization problem. Finally, the taxonomic integration method adds a comprehensive and coherent taxonomic hierarchy on top of this registry, capturing how different entities relate to each other. Together, these methods can be used to produce a large-scale multilingual knowledge base semantically describing over 5 million entities and over 16 million natural language words and names in more than 200 different languages.Da ein großer Teil unseres Wissens in textueller Form vorliegt, sind Informationssysteme in zunehmendem Maße auf Wissen über Wörter und den von ihnen repräsentierten Entitäten angewiesen. Gegenstand dieser Arbeit sind neue Methoden zur automatischen Erstellung großer multilingualer Wissensbanken, welche semantische Beziehungen zwischen Wörtern bzw. Namen und Konzepten bzw. Entitäten formal erfassen. In drei Hauptbeiträgen werden jeweils graphtheoretische bzw. statistische Verfahren zur Verknüpfung von Indizien aus mehreren Wissensquellen vorgestellt. Bei der lexikalischen Integration werden statistische Modelle zur Disambiguierung gebildet. Die Entitäten-Integration fasst semantische Einheiten unter Auflösung von Konflikten zwischen Äquivalenz- und Verschiedenheitsinformationen zusammen. Diese werden schließlich bei der taxonomischen Integration durch eine umfassende taxonomische Hierarchie ergänzt. Zusammen können diese Methoden zur Induzierung einer großen multilingualen Wissensbank eingesetzt werden, welche über 5 Millionen Entitäten und über 16 Millionen Wörter und Namen in mehr als 200 Sprachen semantisch beschreibt

Information-theoretic causal inference of lexical flow

This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages

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

Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process

Hans Hjelm. Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process. NEALT Monograph Series, Vol. 1 (2009), 159 pages. © 2009 Hans Hjelm. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/10126