Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

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

Entity-centric knowledge discovery for idiosyncratic domains

Technical and scientific knowledge is produced at an ever-accelerating pace, leading to increasing issues when trying to automatically organize or process it, e.g., when searching for relevant prior work. Knowledge can today be produced both in unstructured (plain text) and structured (metadata or linked data) forms. However, unstructured content is still themost dominant formused to represent scientific knowledge. In order to facilitate the extraction and discovery of relevant content, new automated and scalable methods for processing, structuring and organizing scientific knowledge are called for. In this context, a number of applications are emerging, ranging fromNamed Entity Recognition (NER) and Entity Linking tools for scientific papers to specific platforms leveraging information extraction techniques to organize scientific knowledge. In this thesis, we tackle the tasks of Entity Recognition, Disambiguation and Linking in idiosyncratic domains with an emphasis on scientific literature. Furthermore, we study the related task of co-reference resolution with a specific focus on named entities. We start by exploring Named Entity Recognition, a task that aims to identify the boundaries of named entities in textual contents. We propose a newmethod to generate candidate named entities based on n-gram collocation statistics and design several entity recognition features to further classify them. In addition, we show how the use of external knowledge bases (either domain-specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic domains. Subsequently, we move to Entity Disambiguation, which is typically performed after entity recognition in order to link an entity to a knowledge base. We propose novel semi-supervised methods for word disambiguation leveraging the structure of a community-based ontology of scientific concepts. Our approach exploits the graph structure that connects different terms and their definitions to automatically identify the correct sense that was originally picked by the authors of a scientific publication. We then turn to co-reference resolution, a task aiming at identifying entities that appear using various forms throughout the text. We propose an approach to type entities leveraging an inverted index built on top of a knowledge base, and to subsequently re-assign entities based on the semantic relatedness of the introduced types. Finally, we describe an application which goal is to help researchers discover and manage scientific publications. We focus on the problem of selecting relevant tags to organize collections of research papers in that context. We experimentally demonstrate that the use of a community-authored ontology together with information about the position of the concepts in the documents allows to significantly increase the precision of tag selection over standard methods

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Tag-Aware Recommender Systems: A State-of-the-Art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithm

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Exploiting extensible background knowledge for clustering-based automatic keyphrase extraction

Keyphrases are single- or multi-word phrases that are used to describe the essential content of a document. Utilizing an external knowledge source such as WordNet is often used in keyphrase extraction methods to obtain relation information about terms and thus improves the result, but the drawback is that a sole knowledge source is often limited. This problem is identified as the coverage limitation problem. In this paper, we introduce SemCluster, a clustering-based unsupervised keyphrase extraction method that addresses the coverage limitation problem by using an extensible approach that integrates an internal ontology (i.e., WordNet) with other knowledge sources to gain a wider background knowledge. SemCluster is evaluated against three unsupervised methods, TextRank, ExpandRank, and KeyCluster, and under the F1-measure metric. The evaluation results demonstrate that SemCluster has better accuracy and computational efficiency and is more robust when dealing with documents from different domains