Ontology driven information retrieval.

Ontology-driven information retrieval deals with the use of entities specified in domain ontologies to enhance search and browse. The entities or concepts of lightweight ontological resources are traditionally used to index resources in specialised domains. Indexing with concepts is often achieved manually and reusing them to enhance search remains a challenge. Other challenges range from the difficulty in merging multiple ontologies for use in retrieval to the problem of integrating concept-based search into existing search systems. We mainly encounter these challenges in enterprise search environments, which have not kept pace with Web search engines and mostly rely on full-text search systems. Full-text search systems are keyword-based and suffer from well-known vocabulary mismatch problems. Ontologies model domain knowledge and have the potential for use in understanding the unstructured content of documents. In this thesis, we investigate the challenges of using domain ontologies for enhancing search in enterprise systems. Firstly, we investigate methods for annotating documents by identifying the best concepts that represent their contents. We explore ways to overcome the challenges of insufficient textual features in lightweight ontologies and introduce an unsupervised method for annotating documents based on generating concept descriptors from external resources. Specifically, we augment concepts with descriptive textual content by exploiting the taxonomic structure of an ontology to ensure that we generate useful descriptors. Secondly, the need often arises for cross-ontology reasoning when using multiple ontologies in ontology-driven search. Once again, we attempt to overcome the absence of rich features in lightweight ontologies by exploring the use of background knowledge for the alignment process. We propose novel ontology alignment techniques which integrate string metrics, semantic features, and term weights for discovering diverse correspondence types in supervised and unsupervised ontology alignment. Thirdly, we investigate different representational schemes for queries and documents and explore semantic ranking models using conceptual representations. Accordingly, we propose a semantic ranking model that incorporates the knowledge of concept relatedness and a predictive model to apply semantic ranking only when it is deemed beneficial for retrieval. Finally, we conduct comprehensive evaluations of the proposed methods and discuss our findings

Information search and similarity based on Web 2.0 and semantic technologies

The World Wide Web provides a huge amount of information described in natural language at the current society’s disposal. Web search engines were born from the necessity of finding a particular piece of that information. Their ease of use and their utility have turned these engines into one of the most used web tools at a daily basis. To make a query, users just have to introduce a set of words - keywords - in natural language and the engine answers with a list of ordered resources which contain those words. The order is given by ranking algorithms. These algorithms use basically two types of features: dynamic and static factors. The dynamic factor has into account the query; that is, those documents which contain the keywords used to describe the query are more relevant for that query. The hyperlinks structure among documents is an example of a static factor of most current algorithms. For example, if most documents link to a particular document, this document may have more relevance than others because it is more popular. Even though currently there is a wide consensus on the good results that the majority of web search engines provides, these tools still suffer from some limitations, basically 1) the loneliness of the searching activity itself; and 2) the simple recovery process, based mainly on offering the documents that contains the exact terms used to describe the query. Considering the first problem, there is no doubt in the lonely and time-consuming process of searching relevant information in the World Wide Web. There are thousands of users out there that repeat previously executed queries, spending time in taking decisions of which documents are relevant or not; decisions that may have been taken previously and that may be do the job for similar or identical queries for other users. Considering the second problem, the textual nature of the current Web makes the reasoning capability of web search engines quite restricted; queries and web resources are described in natural language that, in some cases, can lead to ambiguity or other semantic-related difficulties. Computers do not know text; however, if semantics is incorporated to the text, meaning and sense is incorporated too. This way, queries and web resources will not be mere sets of terms, but lists of well-defined concepts. This thesis proposes a semantic layer, known as Itaca, which joins simplicity and effectiveness in order to endow with semantics both the resources stored in the World Wide Web and the queries used by users to find those resources. This is achieved through collaborative annotations and relevance feedback made by the users themselves, which describe both the queries and the web resources by means of Wikipedia concepts. Itaca extends the functional capabilities of current web search engines, providing a new ranking algorithm without dispensing traditional ranking models. Experiments show that this new architecture offers more precision in the final results obtained, keeping the simplicity and usability of the web search engines existing so far. Its particular design as a layer makes feasible its inclusion to current engines in a simple way.Internet pone a disposición de la sociedad una enorme cantidad de información descrita en lenguaje natural. Los buscadores web nacieron de la necesidad de encontrar un fragmento de información entre tanto volumen de datos. Su facilidad de manejo y su utilidad los han convertido en herramientas de uso diario entre la población. Para realizar una consulta, el usuario sólo tiene que introducir varias palabras clave en lenguaje natural y el buscador responde con una lista de recursos que contienen dichas palabras, ordenados en base a algoritmos de ranking. Estos algoritmos usan dos tipos de factores básicos: factores dinámicos y estáticos. El factor dinámico tiene en cuenta la consulta en sí; es decir, aquellos documentos donde estén las palabras utilizadas para describir la consulta serán más relevantes para dicha consulta. La estructura de hiperenlaces en los documentos electrónicos es un ejemplo de factor estático. Por ejemplo, si muchos documentos enlazan a otro documento, éste último documento podrá ser más relevante que otros. Si bien es cierto que actualmente hay consenso entre los buenos resultados de estos buscadores, todavía adolecen de ciertos problemas, destacando 1) la soledad en la que un usuario realiza una consulta; y 2) el modelo simple de recuperación, basado en ver si un documento contiene o no las palabras exactas usadas para describir la consulta. Con respecto al primer problema, no hay duda de que navegar en busca de cierta información relevante es una práctica solitaria y que consume mucho tiempo. Hay miles de usuarios ahí fuera que repiten sin saberlo una misma consulta, y las decisiones que toman muchos de ellos, descartando la información irrelevante y quedándose con la que realmente es útil, podrían servir de guía para otros muchos. Con respecto al segundo, el carácter textual de la Web actual hace que la capacidad de razonamiento en los buscadores se vea limitada, pues las consultas y los recursos están descritos en lenguaje natural que en ocasiones da origen a la ambigüedad. Los equipos informáticos no comprenden el texto que se incluye. Si se incorpora semántica al lenguaje, se incorpora significado, de forma que las consultas y los recursos electrónicos no son meros conjuntos de términos, sino una lista de conceptos claramente diferenciados. La presente tesis desarrolla una capa semántica, Itaca, que dota de significado tanto a los recursos almacenados en la Web como a las consultas que pueden formular los usuarios para encontrar dichos recursos. Todo ello se consigue a través de anotaciones colaborativas y de relevancia realizadas por los propios usuarios, que describen tanto consultas como recursos electrónicos mediante conceptos extraídos de Wikipedia. Itaca extiende las características funcionales de los buscadores web actuales, aportando un nuevo modelo de ranking sin tener que prescindir de los modelos actualmente en uso. Los experimentos demuestran que aporta una mayor precisión en los resultados finales, manteniendo la simplicidad y usabilidad de los buscadores que se conocen hasta ahora. Su particular diseño, a modo de capa, hace que su incorporación a buscadores ya existentes sea posible y sencilla.Programa Oficial de Posgrado en Ingeniería TelemáticaPresidente: Asunción Gómez Pérez.- Secretario: Mario Muñoz Organero.- Vocal: Anselmo Peñas Padill

Improving Product-related Patent Information Access with Automated Technology Ontology Extraction

Ph.DDOCTOR OF PHILOSOPH

Towards the extraction of cross-sentence relations through event extraction and entity coreference

Cross-sentence relation extraction deals with the extraction of relations beyond the sentence boundary. This thesis focuses on two of the NLP tasks which are of importance to the successful extraction of cross-sentence relation mentions: event extraction and coreference resolution. The first part of the thesis focuses on addressing data sparsity issues in event extraction. We propose a self-training approach for obtaining additional labeled examples for the task. The process starts off with a Bi-LSTM event tagger trained on a small labeled data set which is used to discover new event instances in a large collection of unstructured text. The high confidence model predictions are selected to construct a data set of automatically-labeled training examples. We present several ways in which the resulting data set can be used for re-training the event tagger in conjunction with the initial labeled data. The best configuration achieves statistically significant improvement over the baseline on the ACE 2005 test set (macro-F1), as well as in a 10-fold cross validation (micro- and macro-F1) evaluation. Our error analysis reveals that the augmentation approach is especially beneficial for the classification of the most under-represented event types in the original data set. The second part of the thesis focuses on the problem of coreference resolution. While a certain level of precision can be reached by modeling surface information about entity mentions, their successful resolution often depends on semantic or world knowledge. This thesis investigates an unsupervised source of such knowledge, namely distributed word representations. We present several ways in which word embeddings can be utilized to extract features for a supervised coreference resolver. Our evaluation results and error analysis show that each of these features helps improve over the baseline coreference system’s performance, with a statistically significant improvement (CoNLL F1) achieved when the proposed features are used jointly. Moreover, all features lead to a reduction in the amount of precision errors in resolving references between common nouns, demonstrating that they successfully incorporate semantic information into the process

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen