User driven information extraction with LODIE

Information Extraction (IE) is the technique for transforming unstructured or semi-structured data into structured representation that can be understood by machines. In this paper we use a user-driven Information Extraction technique to wrap entity-centric Web pages. The user can select concepts and properties of interest from available Linked Data. Given a number of websites containing pages about the concepts of interest, the method will exploit (i) recurrent structures in the Web pages and (ii) available knowledge in Linked data to extract the information of interest from the Web pages

Exploiting Linked Open Data to Uncover Entity Types

Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes

Caractérisation de l'environnement musical dans les documents audiovisuels

Currently, the amount of music available, notably via the Internet, is growing daily. The collections are too huge for a user to navigate into without help from a computer. Our work takes place in the general context of music indexation. In order to detail the context of our work, we present a brief overview of the work currently made in music indexation for indexation : instrument recognition, tonality and tempo estimation, genre and mood classification, singer identification, melody, score, chord and lyrics transcription. For each of these subjects, we insist on the definition of the problem and of technical terms, and on the more imporants problems encountered. In a second part, we present au method we developped to automatically distinguish between monophonic and polyphonic sounds. For this task, we developped two new parameters, based on the analysis of a confidence indicator. The modeling of these parameters is made with Weibull bivariate distributions. We studied the problem of the estimation of the parameters of this distribution, and suggested an original method derived from the moment method. A full set of experiment allow us to compare our system with classical method, and to validate each step of our approach. In the third part, we present a singing voice detector, in monophonic and polyphonic context. This method is base on the detection of vibrato. This parameter is derived from the analysis of the fundamental frequency, so it is a priori defined for monophonic sounds. Using two segmentations, we extend this concept to polyphonic sound, and present a new parameter : the extended vibrato. Our system's performances are comparable with those of state-of-the-art methods. Using the monophonic / polyphonic distinction as a pre-processing allow us to adapt our singing voice detector to each context. This leads to an improvment of the results. After giving some reflexions on the use of music for automatic description, annotating and indexing of audiovisual documents, we present the contribution of each tool we presented to music indexation, and to audiovisual documents indexation using music, and finally give some perspectives.Actuellement, la quantité de musique disponible, notamment via Internet, va tous les jours croissant. Les collections sont trop gigantesques pour qu'il soit possible d'y naviguer ou d'y rechercher un extrait sans l'aide d'outils informatiques. Notre travail se place dans le cadre général de l'indexation automatique de la musique. Afin de situer le contexte de travail, nous proposons tout d'abord une brève revue des travaux réalisés actuellement pour la description automatique de la musique à des fins d'indexation : reconnaissance d'instruments, détermination de la tonalité, du tempo, classification en genre et en émotion, identification du chanteur, transcriptions de la mélodie, de la partition, de la suite d'accords et des paroles. Pour chacun de ces sujets, nous nous attachons à définir le problème, les termes techniques propres au domaine, et nous nous attardons plus particulièrement sur les problèmes les plus saillants. Dans une seconde partie, nous décrivons le premier outil que nous avons développé : une distinction automatique entre les sons monophoniques et les sons polyphoniques. Nous avons proposé deux nouveaux paramètres, basés sur l'analyse d'un indice de confiance. La modélisation de la répartition bivariée de ces paramètre est réalisée par des distributions de Weibull bivariées. Le problème de l'estimation des paramètres de cette distribution nous a conduit à proposer une méthode originale d'estimation dérivée de l'analyse des moments de la loi. Une série d'expériences nous permet de comparer notre système à des approches classiques, et de valider toutes les étapes de notre méthode. Dans la troisième partie, nous proposons une méthode de détection du chant, accompagné ou non. Cette méthode se base sur la détection du vibrato, un paramètre défini à partir de l'analyse de la fréquence fondamentale, et défini a priori pour les sons monophoniques. A l'aide de deux segmentations, nous étendons ce concept aux sons polyphoniques, en introduisant un nouveau paramètre : le vibrato étendu. Les performances de cette méthode sont comparables à celles de l'état de l'art. La prise en compte du pré-traitement monophonique / polyphonique nous a amenés à adapter notre méthode de détection du chant à chacun de ces contextes. Les résultats s'en trouvent améliorés. Après une réflexion sur l'utilisation de la musique pour la description, l'annotation et l'indexation automatique des documents audiovisuels, nous nous posons la question de l'apport de chacun des outils décrits dans cette thèse au problème de l'indexation de la musique, et de l'indexation des documents audiovisuels par la musique et offrons quelques perspectives

Distantly Supervised Web Relation Extraction for Knowledge Base Population

Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%

An unsupervised data-driven method to discover equivalent relations in large linked datasets

This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the ‘well-formedness’ of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain- and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context

SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank

Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a ‘one-size-fit-all’ solution that may never exist, we propose to develop generic methods to ‘enhance’ existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness—an often overlooked venue—into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute ‘semantic importance’ scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K’s), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1

Moving towards the semantic web: enabling new technologies through the semantic annotation of social contents.

La Web Social ha causat un creixement exponencial dels continguts disponibles deixant enormes quantitats de recursos textuals electrònics que sovint aclaparen els usuaris. Aquest volum d’informació és d’interès per a la comunitat de mineria de dades. Els algorismes de mineria de dades exploten característiques de les entitats per tal de categoritzar-les, agrupar-les o classificar-les segons la seva semblança. Les dades per si mateixes no aporten cap mena de significat: han de ser interpretades per esdevenir informació. Els mètodes tradicionals de mineria de dades no tenen com a objectiu “entendre” el contingut d’un recurs, sinó que extreuen valors numèrics els quals esdevenen models en aplicar-hi càlculs estadístics, que només cobren sentit sota l’anàlisi manual d’un expert. Els darrers anys, motivat per la Web Semàntica, molts investigadors han proposat mètodes semàntics de classificació de dades capaços d’explotar recursos textuals a nivell conceptual. Malgrat això, normalment aquests mètodes depenen de recursos anotats prèviament per poder interpretar semànticament el contingut d’un document. L’ús d’aquests mètodes està estretament relacionat amb l’associació de dades i el seu significat. Aquest treball es centra en el desenvolupament d’una metodologia genèrica capaç de detectar els trets més rellevants d’un recurs textual descobrint la seva associació semàntica, es a dir, enllaçant-los amb conceptes modelats a una ontologia, i detectant els principals temes de discussió. Els mètodes proposats són no supervisats per evitar el coll d’ampolla generat per l’anotació manual, independents del domini (aplicables a qualsevol àrea de coneixement) i flexibles (capaços d’analitzar recursos heterogenis: documents textuals o documents semi-estructurats com els articles de la Viquipèdia o les publicacions de Twitter). El treball ha estat avaluat en els àmbits turístic i mèdic. Per tant, aquesta dissertació és un primer pas cap a l'anotació semàntica automàtica de documents necessària per possibilitar el camí cap a la visió de la Web Semàntica.La Web Social ha provocado un crecimiento exponencial de los contenidos disponibles, dejando enormes cantidades de recursos electrónicos que a menudo abruman a los usuarios. Tal volumen de información es de interés para la comunidad de minería de datos. Los algoritmos de minería de datos explotan características de las entidades para categorizarlas, agruparlas o clasificarlas según su semejanza. Los datos por sí mismos no aportan ningún significado: deben ser interpretados para convertirse en información. Los métodos tradicionales no tienen como objetivo "entender" el contenido de un recurso, sino que extraen valores numéricos que se convierten en modelos tras aplicar cálculos estadísticos, los cuales cobran sentido bajo el análisis manual de un experto. Actualmente, motivados por la Web Semántica, muchos investigadores han propuesto métodos semánticos de clasificación de datos capaces de explotar recursos textuales a nivel conceptual. Sin embargo, generalmente estos métodos dependen de recursos anotados previamente para poder interpretar semánticamente el contenido de un documento. El uso de estos métodos está estrechamente relacionado con la asociación de datos y su significado. Este trabajo se centra en el desarrollo de una metodología genérica capaz de detectar los rasgos más relevantes de un recurso textual descubriendo su asociación semántica, es decir, enlazándolos con conceptos modelados en una ontología, y detectando los principales temas de discusión. Los métodos propuestos son no supervisados para evitar el cuello de botella generado por la anotación manual, independientes del dominio (aplicables a cualquier área de conocimiento) y flexibles (capaces de analizar recursos heterogéneos: documentos textuales o documentos semi-estructurados, como artículos de la Wikipedia o publicaciones de Twitter). El trabajo ha sido evaluado en los ámbitos turístico y médico. Esta disertación es un primer paso hacia la anotación semántica automática de documentos necesaria para posibilitar el camino hacia la visión de la Web Semántica.Social Web technologies have caused an exponential growth of the documents available through the Web, making enormous amounts of textual electronic resources available. Users may be overwhelmed by such amount of contents and, therefore, the automatic analysis and exploitation of all this information is of interest to the data mining community. Data mining algorithms exploit features of the entities in order to characterise, group or classify them according to their resemblance. Data by itself does not carry any meaning; it needs to be interpreted to convey information. Classical data analysis methods did not aim to “understand” the content and the data were treated as meaningless numbers and statistics were calculated on them to build models that were interpreted manually by human domain experts. Nowadays, motivated by the Semantic Web, many researchers have proposed semantic-grounded data classification and clustering methods that are able to exploit textual data at a conceptual level. However, they usually rely on pre-annotated inputs to be able to semantically interpret textual data such as the content of Web pages. The usability of all these methods is related to the linkage between data and its meaning. This work focuses on the development of a general methodology able to detect the most relevant features of a particular textual resource finding out their semantics (associating them to concepts modelled in ontologies) and detecting its main topics. The proposed methods are unsupervised (avoiding the manual annotation bottleneck), domain-independent (applicable to any area of knowledge) and flexible (being able to deal with heterogeneous resources: raw text documents, semi-structured user-generated documents such Wikipedia articles or short and noisy tweets). The methods have been evaluated in different fields (Tourism, Oncology). This work is a first step towards the automatic semantic annotation of documents, needed to pave the way towards the Semantic Web vision