Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents

The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications

Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledge

Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This mapping allows them to effectively leverage the information contained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting---these semantic parsers can only assign meaning to language that falls within the KB's manually-produced schema. Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base. However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models. We show how to combine the disparate representations used by these two approaches, presenting for the first time a semantic parser that (1) produces compositional, executable representations of language, (2) can successfully leverage the information contained in both a formal KB and a large corpus, and (3) is not limited to the schema of the underlying KB. We demonstrate significantly improved performance over state-of-the-art baselines on an open-domain natural language question answering task.Comment: Re-written abstract and intro, other minor changes throughout. This version published at AAAI 201

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Traceability Links Recovery among Requirements and BPMN models

Tesis por compendio[EN] Throughout the pages of this document, I present the results of the research that was carried out in the context of my PhD studies. During the aforementioned research, I studied the process of Traceability Links Recovery between natural language requirements and industrial software models. More precisely, due to their popularity and extensive usage, I studied the process of Traceability Links Recovery between natural language requirements and Business Process Models, also known as BPMN models. In order to carry out the research, I focused my work on two main objectives: (1) the development of the Traceability Links Recovery techniques between natural language requirements and BPMN models, and (2) the validation and analysis of the results obtained by the developed techniques in industrial domain case studies. The results of the research have been redacted and published in forums, conferences, and journals specialized in the topics and context of the research. This thesis document introduces the topics, context, and objectives of the research, presents the academic publications that have been published as a result of the work, and then discusses the outcomes of the investigation.[ES] A través de las páginas de este documento, presento los resultados de la investigación realizada en el contexto de mis estudios de doctorado. Durante la investigación, he estudiado el proceso de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y modelos de software industriales. Más concretamente, debido a su popularidad y uso extensivo, he estudiado el proceso de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y Modelos de Procesos de Negocio, también conocidos como modelos BPMN. Para llevar a cabo esta investigación, mi trabajo se ha centrado en dos objetivos principales: (1) desarrollo de técnicas de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y modelos BPMN, y (2) validación y análisis de los resultados obtenidos por las técnicas desarrolladas en casos de estudio de dominios industriales. Los resultados de la investigación han sido redactados y publicados en foros, conferencias y revistas especializadas en los temas y contexto de la investigación. Esta tesis introduce los temas, contexto y objetivos de la investigación, presenta las publicaciones académicas que han sido publicadas como resultado del trabajo, y expone los resultados de la investigación.[CA] A través de les pàgines d'aquest document, presente els resultats de la investigació realitzada en el context dels meus estudis de doctorat. Durant la investigació, he estudiat el procés de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i models de programari industrials. Més concretament, a causa de la seua popularitat i ús extensiu, he estudiat el procés de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i Models de Processos de Negoci, també coneguts com a models BPMN. Per a dur a terme aquesta investigació, el meu treball s'ha centrat en dos objectius principals: (1) desenvolupament de tècniques de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i models BPMN, i (2) validació i anàlisi dels resultats obtinguts per les tècniques desenvolupades en casos d'estudi de dominis industrials. Els resultats de la investigació han sigut redactats i publicats en fòrums, conferències i revistes especialitzades en els temes i context de la investigació. Aquesta tesi introdueix els temes, context i objectius de la investigació, presenta les publicacions acadèmiques que han sigut publicades com a resultat del treball, i exposa els resultats de la investigació.Lapeña Martí, R. (2020). Traceability Links Recovery among Requirements and BPMN models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149391TESISCompendi