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Artigos de revistas sobre o assunto "Annotation via web"
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Islamaj, Rezarta, Dongseop Kwon, Sun Kim e Zhiyong Lu. "TeamTat: a collaborative text annotation tool". Nucleic Acids Research 48, W1 (8 de maio de 2020): W5—W11. http://dx.doi.org/10.1093/nar/gkaa333.
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Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.
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Mazhoud, Omar, Anis Kalboussi e Ahmed Hadj Kacem. "Educational Recommender System based on Learner’s Annotative Activity". International Journal of Emerging Technologies in Learning (iJET) 16, n.º 10 (25 de maio de 2021): 108. http://dx.doi.org/10.3991/ijet.v16i10.19955.
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In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.
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Wang, Han, Xinxiao Wu e Yunde Jia. "Video Annotation via Image Groups from the Web". IEEE Transactions on Multimedia 16, n.º 5 (agosto de 2014): 1282–91. http://dx.doi.org/10.1109/tmm.2014.2312251.
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Ma, Zhigang, Feiping Nie, Yi Yang, Jasper R. R. Uijlings e Nicu Sebe. "Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection". IEEE Transactions on Multimedia 14, n.º 4 (agosto de 2012): 1021–30. http://dx.doi.org/10.1109/tmm.2012.2187179.
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Wei, Chih-Hsuan, Alexis Allot, Robert Leaman e Zhiyong Lu. "PubTator central: automated concept annotation for biomedical full text articles". Nucleic Acids Research 47, W1 (22 de maio de 2019): W587—W593. http://dx.doi.org/10.1093/nar/gkz389.
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AbstractPubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.
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Hu, Mengqiu, Yang Yang, Fumin Shen, Luming Zhang, Heng Tao Shen e Xuelong Li. "Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge". IEEE Transactions on Image Processing 26, n.º 10 (outubro de 2017): 4871–84. http://dx.doi.org/10.1109/tip.2017.2717185.
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Wang, Han, Xiabi Liu, Xinxiao Wu e Yunde Jia. "Cross-domain structural model for video event annotation via web images". Multimedia Tools and Applications 74, n.º 23 (30 de julho de 2014): 10439–56. http://dx.doi.org/10.1007/s11042-014-2175-z.
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Lelong, Sebastien, Xinghua Zhou, Cyrus Afrasiabi, Zhongchao Qian, Marco Alvarado Cano, Ginger Tsueng, Jiwen Xin et al. "BioThings SDK: a toolkit for building high-performance data APIs in biomedical research". Bioinformatics 38, n.º 7 (10 de janeiro de 2022): 2077–79. http://dx.doi.org/10.1093/bioinformatics/btac017.
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Abstract Summary To meet the increased need of making biomedical resources more accessible and reusable, Web Application Programming Interfaces (APIs) or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info and MyChem.info for integrating annotations on genes, variants and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. Availability and implementation The BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api). Supplementary information Supplementary data are available at Bioinformatics online.
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Park, Yeon-Ji, Min-a. Lee, Geun-Je Yang, Soo Jun Park e Chae-Bong Sohn. "Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset". Applied Sciences 12, n.º 23 (24 de novembro de 2022): 12012. http://dx.doi.org/10.3390/app122312012.
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In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.
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Barrett, Kristian, Cameron J. Hunt, Lene Lange e Anne S. Meyer. "Conserved unique peptide patterns (CUPP) online platform: peptide-based functional annotation of carbohydrate active enzymes". Nucleic Acids Research 48, W1 (14 de maio de 2020): W110—W115. http://dx.doi.org/10.1093/nar/gkaa375.
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Abstract The CUPP platform includes a web server for functional annotation and sub-grouping of carbohydrate active enzymes (CAZymes) based on a novel peptide-based similarity assessment algorithm, i.e. protein grouping according to Conserved Unique Peptide Patterns (CUPP). This online platform is open to all users and there is no login requirement. The web server allows the user to perform genome-based annotation of carbohydrate active enzymes to CAZy families, CAZy subfamilies, CUPP groups and EC numbers (function) via assessment of peptide-motifs by CUPP. The web server is intended for functional annotation assessment of the CAZy inventory of prokaryotic and eukaryotic organisms from genomic DNA (up to 30MB compressed) or directly from amino acid sequences (up to 10MB compressed). The custom query sequences are assessed using the CUPP annotation algorithm, and the outcome is displayed in interactive summary result pages of CAZymes. The results displayed allow for inspection of members of the individual CUPP groups and include information about experimentally characterized members. The web server and the other resources on the CUPP platform can be accessed from https://cupp.info.
Teses / dissertações sobre o assunto "Annotation via web"
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Mitran, Mădălina. "Annotation d'images via leur contexte spatio-temporel et les métadonnées du Web". Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2399/.
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En Recherche d'Information (RI), les documents sont classiquement indexés en fonction de leur contenu, qu'il soit textuel ou multimédia. Les moteurs de recherche s'appuyant sur ces index sont aujourd'hui des outils performants, répandus et indispensables. Ils visent à fournir des réponses pertinentes selon le besoin de l'utilisateur, sous forme de textes, images, sons, vidéos, etc. Nos travaux de thèse s'inscrivent dans le contexte des documents de type image. Plus précisément, nous nous sommes intéressés aux systèmes d'annotation automatique d'images qui permettent d'associer automatiquement des mots-clés à des images afin de pouvoir ensuite les rechercher par requête textuelle. Ce type d'annotation cherche à combler les lacunes des approches d'annotation manuelle et semi-automatique. Celles-ci ne sont plus envisageables dans le contexte actuel qui permet à chacun de prendre de nombreuses photos à faible coût (en lien avec la démocratisation des appareils photo numériques et l'intégration de capteurs numériques dans les téléphones mobiles). Parmi les différents types de collections d'images existantes (par exemple, médicales, satellitaires) dans le cadre de cette thèse nous nous sommes intéressés aux collections d'images de type paysage (c. -à-d. Des images qui illustrent des points d'intérêt touristiques) pour lesquelles nous avons identifié des défis, tels que l'identification des nouveaux descripteurs pour les décrire et de nouveaux modèles pour fusionner ces derniers, l'identification des sources d'information pertinentes et le passage à l'échelle. Nos contributions portent sur trois principaux volets. En premier lieu, nous nous sommes attachés à exploiter différents descripteurs qui peuvent influencer la description des images de type paysage : le descripteur de spatialisation (caractérisé par la latitude et la longitude des images), le descripteur de temporalité (caractérisé par la date et l'heure de la prise de vue) et le descripteur de thématique (caractérisé par les tags issus des plate formes de partage d'images). Ensuite, nous avons proposé des approches pour modéliser ces descripteurs au regard de statistiques de tags liées à leur fréquence et rareté et sur des similarités spatiale et temporelle. Deuxièmement, nous avons proposé un nouveau processus d'annotation d'images qui vise à identifier les mots-clés qui décrivent le mieux les images-requêtes données en entrée d'un système d'annotation par un utilisateur. Pour ce faire, pour chaque image-requête nous avons mis en œuvre des filtres spatial, temporel et spatio-temporel afin d'identifier les images similaires ainsi que leurs tags associés. Ensuite, nous avons fédéré les différents descripteurs dans un modèle probabiliste afin de déterminer les termes qui décrivent le mieux chaque image-requête. Enfin, le fait que les contributions présentées ci-dessus s'appuient uniquement sur des informations issues des plateformes de partage d'images (c. -à-d. Des informations subjectives) a suscité la question suivante : les informations issues du Web peuvent-elles fournir des termes objectifs pour enrichir les descriptions initiales des images. À cet effet, nous avons proposé une approche basée sur les techniques d'expansion de requêtes du domaine de la RI. Elle porte essentiellement sur l'étude de l'impact des différents algorithmes d'expansion, ainsi que sur l'agrégation des résultats fournis par le meilleur algorithme et les résultats fournis par le processus d'annotation d'images. Vu qu'il n'existe pas de cadre d'évaluation standard d'annotation automatique d'images, plus particulièrement adapté aux collections d'images de type paysage, nous avons proposé des cadres d'évaluation appropriés afin de valider nos contributions. En particulier, les différentes approches proposées sont évaluées au regard de la modélisation des descripteur de spatialisation, de temporalité et de thématique. De plus, nous avons validé le processus d'annotation d'images, et nous avons montré qu'il surpasse en qualité deux approches d'annotation d'images de la littérature. Nous avons comparé également l'approche d'enrichissement avec le processus d'annotation d'image pour souligner son efficacité et l'apport des informations issues du Web. Ces expérimentations ont nécessité le prototypage du logiciel AnnoTaGT, qui offre aux utilisateurs un cadre technique pour l'annotation automatique d'imagesThe documents processed by Information Retrieval (IR) systems are typically indexed according to their contents: Text or multimedia. Search engines based on these indexes aim to provide relevant answers to users' needs in the form of texts, images, sounds, videos, and so on. Our work is related to "image" documents. We are specifically interested in automatic image annotation systems that automatically associate keywords to images. Keywords are subsequently used for search purposes via textual queries. The automatic image annotation task intends to overcome the issues of manual and semi-automatic annotation tasks, as they are no longer feasible in nowadays' context (i. E. , the development of digital technologies and the advent of devices, such as smartphones, allowing anyone to take images with a minimal cost). Among the different types of existing image collections (e. G. , medical, satellite) in our work we are interested in landscape image collections for which we identified the following challenges: What are the most discriminant features for this type of images ? How to model and how to merge these features ? What are the sources of information that should be considered ? How to manage scalability issues ? The proposed contribution is threefold. First, we use different factors that influence the description of landscape images: The spatial factor (i. E. , latitude and longitude of images), the temporal factor (i. E. , the time when the images were taken), and the thematic factor (i. E. , tags crowdsourced and contributed to image sharing platforms). We propose various techniques to model these factors based on tag frequency, as well as spatial and temporal similarities. The choice of these factors is based on the following assumptions: A tag is all the more relevant for a query-image as it is associated with images located in its close geographical area ; A tag is all the more relevant for a query-image as it is associated with images captured close in time to it ; sourcing concept). Second, we introduce a new image annotation process that recommends the terms that best describe a given query-image provided by a user. For each query-image we rely on spatial, temporal, and spatio-temporal filters to identify similar images along with their tags. Then, the different factors are merged through a probabilistic model to boost the terms best describing each query-image. Third, the contributions presented above are only based on information extracted from image photo sharing platforms (i. E. , subjective information). This raised the following research question: Can the information extracted from the Web provide objective terms useful to enrich the initial description of images? We tackle this question by introducing an approach relying on query expansion techniques developed in IR. As there is no standard evaluation protocol for the automatic image annotation task tailored to landscape images, we designed various evaluation protocols to validate our contributions. We first evaluated the approaches defined to model the spatial, temporal, and thematic factors. Then, we validated the annotation image process and we showed that it yields significant improvement over two state-of-the-art baselines. Finally, we assessed the effectiveness of tag expansion through Web sources and showed its contribution to the image annotation process. These experiments are complemented by the image annotation prototype AnnoTaGT, which provides users with an operational framework for automatic image annotation
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Bedoya, Ramos Daniel. "Capturing Musical Prosody Through Interactive Audio/Visual Annotations". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS698.
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Des projets de science participative (SP) ont stimulé la recherche dans plusieurs disciplines au cours des dernières années. Des citoyens scientifiques contribuent à cette recherche en effectuant des tâches cognitives, favorisant l'apprentissage, l'innovation et l'inclusion. Bien que le crowdsourcing ait servi à recueillir des annotations structurelles en musique, la SP reste sous-utilisée pour étudier l'expressivité musicale. On introduit un nouveau protocole d'annotation pour capturer la prosodie musicale, associée aux variations acoustiques introduites par les interprètes pour rendre la musique expressive. Notre méthode descendante, centrée sur l'humain, donne la priorité à l'auditeur dans la production d'annotations des fonctions prosodiques de la musique. On se concentre sur la segmentation et la proéminence, qui véhiculent la structure et l'affect. Ce protocole fournit un cadre de SP et une approche expérimentale pour réaliser des études systématiques et extensibles. On met en œuvre ce protocole d'annotation dans CosmoNote, un logiciel web personnalisable, conçu pour faciliter l'annotation de structures musicales expressives. CosmoNote permet aux utilisateurs d'interagir avec des couches visuelles, y compris la forme d'onde, les notes enregistrées, les attributs audio extraits et les caractéristiques de la partition. On peut placer des frontières de niveaux différents, des régions, des commentaires et des groupes de notes. On a mené deux études visant à améliorer le protocole et la plateforme. La première, examine l'impact des stimuli auditifs et visuels simultanés sur les frontières de segmentation. On compare les différences dans les distributions de frontières dérivées d'informations intermodales (auditives et visuelles) et unimodales (auditives ou visuelles). Les distances entre les distributions unimodales-visuelles et intermodales sont plus faibles qu'entre les distributions unimodales-auditives et intermodales. On montre que l'ajout de visuels accentue les informations clés et fournit un échafaudage cognitif aidant à marquer clairement les frontières prosodiques, bien qu'ils puissent détourner l'attention de structures spécifiques. À l'inverse, sans audio, la tâche d'annotation devient difficile, masquant des indices subtils. Malgré leur exagération ou inexactitude, les repères visuels sont essentiels pour guider les annotations de frontières en interprétation, ce qui améliore les résultats globaux. La deuxième étude utilise tous les types d'annotations de CosmoNote et analyse comment les participants annotent la prosodie musicale, avec des instructions minimales ou détaillées, dans un cadre d'annotations libres. On compare la qualité des annotations entre musiciens et non-musiciens. On évalue la composante de SP dans un cadre écologique où les participants sont totalement autonomes dans une tâche où le temps, l'attention et la patience sont valorisés. On présente trois méthodes basées sur des étiquettes d'annotation, des catégories et des propriétés communes pour analyser et agréger les données. Les résultats montrent une convergence dans les types d'annotations et les descriptions utilisées pour marquer les éléments musicaux récurrents, pour toute condition expérimentale et aptitude musicale. On propose des stratégies pour améliorer le protocole, l'agrégation des données et l'analyse dans des applications à grande échelle. Cette thèse enrichit la représentation et la compréhension des structures en musique interprétée en introduisant un protocole et une plateforme d'annotation, des expériences adaptables et des méthodes d'agrégation et d'analyse. On montre l'importance du compromis entre l'obtention de données plus simples à analyser et celle d'un contenu plus riche, capturant une pensée musicale complexe. Notre protocole peut être généralisé aux études sur les décisions d'interprétation afin d'améliorer la compréhension des choix expressifs dans l'interprétation musicaleThe proliferation of citizen science projects has advanced research and knowledge across disciplines in recent years. Citizen scientists contribute to research through volunteer thinking, often by engaging in cognitive tasks using mobile devices, web interfaces, or personal computers, with the added benefit of fostering learning, innovation, and inclusiveness. In music, crowdsourcing has been applied to gather various structural annotations. However, citizen science remains underutilized in musical expressiveness studies. To bridge this gap, we introduce a novel annotation protocol to capture musical prosody, which refers to the acoustic variations performers introduce to make music expressive. Our top-down, human-centered method prioritizes the listener's role in producing annotations of prosodic functions in music. This protocol provides a citizen science framework and experimental approach to carrying out systematic and scalable studies on the functions of musical prosody. We focus on the segmentation and prominence functions, which convey structure and affect. We implement this annotation protocol in CosmoNote, a web-based, interactive, and customizable software conceived to facilitate the annotation of expressive music structures. CosmoNote gives users access to visualization layers, including the audio waveform, the recorded notes, extracted audio attributes (loudness and tempo), and score features (harmonic tension and other markings). The annotation types comprise boundaries of varying strengths, regions, comments, and note groups. We conducted two studies aimed at improving the protocol and the platform. The first study examines the impact of co-occurring auditory and visual stimuli on segmentation boundaries. We compare differences in boundary distributions derived from cross-modal (auditory and visual) vs. unimodal (auditory or visual) information. Distances between unimodal-visual and cross-modal distributions are smaller than between unimodal-auditory and cross-modal distributions. On the one hand, we show that adding visuals accentuates crucial information and provides cognitive scaffolding for accurately marking boundaries at the starts and ends of prosodic cues. However, they sometimes divert the annotator's attention away from specific structures. On the other hand, removing the audio impedes the annotation task by hiding subtle, relied-upon cues. Although visual cues may sometimes overemphasize or mislead, they are essential in guiding boundary annotations of recorded performances, often improving the aggregate results. The second study uses all CosmoNote's annotation types and analyzes how annotators, receiving either minimal or detailed protocol instructions, approach annotating musical prosody in a free-form exercise. We compare the quality of annotations between participants who are musically trained and those who are not. The citizen science component is evaluated in an ecological setting where participants are fully autonomous in a task where time, attention, and patience are valued. We present three methods based on common annotation labels, categories, and properties to analyze and aggregate the data. Results show convergence in annotation types and descriptions used to mark recurring musical elements across experimental conditions and musical abilities. We propose strategies for improving the protocol, data aggregation, and analysis in large-scale applications. This thesis contributes to representing and understanding performed musical structures by introducing an annotation protocol and platform, tailored experiments, and aggregation/analysis methods. The research shows the importance of balancing the collection of easier-to-analyze datasets and having richer content that captures complex musical thinking. Our protocol can be generalized to studies on performance decisions to improve the comprehension of expressive choices in musical performances
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Wang, Sheng-Ren, e 王聖仁. "Web-based Summary Writing Learning Environment via the Model of Integrating Concept Mapping and Sharing Annotation". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/16309342212500880796.
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碩士國立臺北教育大學
教育傳播與科技研究所
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In order to solve the difficulties of learners’ summary writing in web-based learning environment, the present study brings up a web-based summary writing model integrating concept mapping, annotation, and CSCL(computer support collaborative learning). Summary writing is beneficial for learners’ reading comprehension, recall, and recognizing the text’s main idea, but it’s still difficult for some learners. Take the judgment of importance for example. When reading a longer or more complicated text, many learners always couldn’t determine what should be deleted and what should be put in the summary. The present study regards this difficulty as two parts: the judgment of importance and cognitive overloading. Thus, the present study is to construct and practice a web-based summary writing learning environment integrating concept mapping and sharing annotation, regarding concept mapping as the scaffold of learners’ catching the main idea of the text. When the learners have some problems in concept mapping in this learning environment, peer collaborative sharing annotation will be applied. And at last, the learners would view the complete concept mapping as a writing frame and then proceed with summary writing. The concept mapping of the present study is a detecting-fault one. It would help learners reduce cognitive loading, recognize the main idea, and find the unknown main idea. When the learners make an annotation in the text, they also help other learners in this learning environment. In conclusion, the present study provides one way of thinking and practice for computer-based summary writing.
Livros sobre o assunto "Annotation via web"
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Anne of Green Gables: Englische Lektüre mit Audio-CD für das 1. und 2. Lernjahr. Illustrierte Lektüre mit Annotationen und Zusatztexten. Klett Sprachen GmbH, 2015.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Annotation via web"
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Xu, Hongtao, Xiangdong Zhou, Lan Lin, Yu Xiang e Baile Shi. "Automatic Web Image Annotation via Web-Scale Image Semantic Space Learning". In Advances in Data and Web Management, 211–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_20.
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De Virgilio, Roberto, e Lorenzo Dolfi. "Web Navigation via Semantic Annotations". In Lecture Notes in Computer Science, 347–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33999-8_41.
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Collmer, C. W., M. Lindeberg e Alan Collmer. "Gene Ontology (GO) for Microbe–Host Interactions and Its Use in Ongoing Annotation of Three Pseudomonas syringae Genomes via the Pseudomonas–Plant Interaction (PPI) Web Site". In Pseudomonas syringae Pathovars and Related Pathogens – Identification, Epidemiology and Genomics, 221–28. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6901-7_23.
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Bensmann, Felix, Andrea Papenmeier, Dagmar Kern, Benjamin Zapilko e Stefan Dietze. "Semantic Annotation, Representation and Linking of Survey Data". In Semantic Systems. In the Era of Knowledge Graphs, 53–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59833-4_4.
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Abstract Semantic technologies offer significant potential for improving data search applications. Ongoing work thrives to equip data catalogs with new semantic search features to supplement existing keyword search and browsing capabilities. In particular within the social sciences, searching and reusing data is essential to foster efficient research. In this paper, we introduce an approach and experimental results aimed at improving interoperability and findability of social sciences survey items. Our contributions include a conceptual model for semantically representing survey items and questions, detailing meaningful dimensions of items, as well as experimental results geared towards the automated prediction of such item features using state-of-the-art machine learning models. Dimensions of interest include, for instance, references to geolocation and time periods or the scope and style of particular questions. We define classification tasks using neural and traditional machine learning models combined with sentence structure features. Applications of our work include semantic and faceted search for questions as part of our GESIS Search. We also provide the lifted data as a knowledge graph via a SPARQL endpoint for further reuse and sharing.
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Koutavas, Vasileios, Yu-Yang Lin e Nikos Tzevelekos. "From Bounded Checking to Verification of Equivalence via Symbolic Up-to Techniques". In Tools and Algorithms for the Construction and Analysis of Systems, 178–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_10.
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AbstractWe present a bounded equivalence verification technique for higher-order programs with local state. This technique combines fully abstract symbolic environmental bisimulations similar to symbolic game semantics, novel up-to techniques, and lightweight state invariant annotations. This yields an equivalence verification technique with no false positives or negatives. The technique is bounded-complete, in that all inequivalences are automatically detected given large enough bounds. Moreover, several hard equivalences are proved automatically or after being annotated with state invariants. We realise the technique in a tool prototype called Hobbit and benchmark it with an extensive set of new and existing examples. Hobbit can prove many classical equivalences including all Meyer and Sieber examples.
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Beyer, Dirk, e Martin Spiessl. "The Static Analyzer Frama-C in SV-COMP (Competition Contribution)". In Tools and Algorithms for the Construction and Analysis of Systems, 429–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99527-0_26.
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AbstractFrama-C is a well-known platform for source-code analysis of programs written in C. It can be extended via its plug-in architecture by various analysis backends and features an extensive annotation language called ACSL. So far it was hard to compare Frama-C to other software verifiers. Our competition participation contributes an adapter named Frama-C-SV, which makes it possible to evaluate Frama-C against other software verifiers. The adapter transforms standard verification tasks (from the well-known SV-Benchmarks collection) in a way that can be understood by Frama-C and produces a verification witness as output. While Frama-C provides many different analyses, we focus on the Evolved Value Analysis (EVA), which uses a combination of different domains to over-approximate the behavior of the analyzed program.
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Ciavotta, Michele, Vincenzo Cutrona, Flavio De Paoli, Nikolay Nikolov, Matteo Palmonari e Dumitru Roman. "Supporting Semantic Data Enrichment at Scale". In Technologies and Applications for Big Data Value, 19–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-78307-5_2.
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AbstractData enrichment is a critical task in the data preparation process in which a dataset is extended with additional information from various sources to perform analyses or add meaningful context. Facilitating the enrichment process design for data workers and supporting its execution on large datasets are only supported to a limited extent by existing solutions. Harnessing semantics at scale can be a crucial factor in effectively addressing this challenge. This chapter presents a comprehensive approach covering both design- and run-time aspects of tabular data enrichment and discusses our experience in making this process scalable. We illustrate how data enrichment steps of a Big Data pipeline can be implemented via tabular transformations exploiting semantic table annotation methods and discuss techniques devised to support the enactment of the resulting process on large tabular datasets. Furthermore, we present results from experimental evaluations in which we tested the scalability and run-time efficiency of the proposed cloud-based approach, enriching massive datasets with promising performance.
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Wood, James, e Robert Atkey. "A Framework for Substructural Type Systems". In Programming Languages and Systems, 376–402. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_14.
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AbstractMechanisation of programming language research is of growing interest, and the act of mechanising type systems and their metatheory is generally becoming easier as new techniques are invented. However, state-of-the-art techniques mostly rely on structurality of the type system — that weakening, contraction, and exchange are admissible and variables can be used unrestrictedly once assumed. Linear logic, and many related subsequent systems, provide motivations for breaking some of these assumptions.We present a framework for mechanising the metatheory of certain substructural type systems, in a style resembling mechanised metatheory of structural type systems. The framework covers a wide range of simply typed syntaxes with semiring usage annotations, via a metasyntax of typing rules. The metasyntax for the premises of a typing rule is related to bunched logic, featuring both sharing and separating conjunction, roughly corresponding to the additive and multiplicative features of linear logic. We use the uniformity of syntaxes to derive type system-generic renaming, substitution, and a form of linearity checking.
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Fiaidhi, Jinan, Sabah Mohammed e Yuan Wei. "Implications of Web 2.0 Technology on Healthcare". In Healthcare and the Effect of Technology, 269–89. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-733-6.ch016.
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Now that the health and medical sector is slowly but surely beginning to embrace Web 2.0 technologies and tactics such as social networking, blogging, and sharing health information, such usage may become an everyday occurrence. This new trend is emerging under the Health 2.0 umbrella where it has important effects on the future of medicine. This chapter introduces some important Health 2.0 concepts and discusses their advantages for health care and medical practice. In addition, this chapter provides a case study for building a Semantic Blog for Gene Annotation and Searching (GAS) among social network users. The GAS Blog enables users to syndicate and aggregate gene case studies via the RSS protocol, annotate gene case studies with the ability to add new tags (folksonomy), and search for/navigate gene case studies among a group or cross-groups based on FOAF, GO, and SCORM metadata. The GAS Blog is built upon an open source toolkit (WordPress) and further programmed via PHP. The GAS Blog is found to be very effective for annotation and navigation when compared with the traditional gene annotation and navigation systems, as well as with traditional search engines such as XPath.
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Wong, Zoie S. Y., Yuchen Qiao, Ryohei Sasano, Hongkuan Zhang, Kenichiro Taneda e Shin Ushiro. "Annotation Guidelines for Medication Errors in Incident Reports: Validation Through a Mixed Methods Approach". In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220095.
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At present no adequate annotation guidelines exists for incident report learning. This study aims at utilizing multiple quantitative and qualitative evidence to validate annotation guidelines for incident reporting of medication errors. Through multiple approaches via annotator training, annotation performance evaluation, exit surveys, and user and expert interviews, a mixed methods explanatory sequential design was utilized to collect 2-stage evidence for validation. We recruited two patient safety experts to participate in piloting, three annotators to receive annotation training and provide user feedback, and two incident report system designers to offer expert comments. Regarding the annotation performance evaluation, the overall accuracy reached 97% and 90% for named entity identification and attribute identification respectively. Participants provided invaluable comments and opinions towards improving the annotation methods. The mixed methods approach created a significant evidential basis for the use of annotation guidelines for incident report of medication errors. Further expansion of the guidelines and external validity present options for future research.
Trabalhos de conferências sobre o assunto "Annotation via web"
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Braylan, Alexander, e Matthew Lease. "Modeling and Aggregation of Complex Annotations via Annotation Distances". In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380250.
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Yagnik, Jay, e Atiq Islam. "Learning people annotation from the web via consistency learning". In the international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1290082.1290121.
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Gong, Xinyu, Yuefu Zhou, Yue Bi, Mingcheng He, Shiying Sheng, Han Qiu, Ruan He e Jialiang Lu. "Estimating Web Attack Detection via Model Uncertainty from Inaccurate Annotation". In 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2019. http://dx.doi.org/10.1109/cscloud/edgecom.2019.00019.
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Shen, Hualei, Dianfu Ma, Yongwang Zhao e Rongwei Ye. "Collaborative annotation of medical images via web browser for teleradiology". In 2012 International Conference on Computerized Healthcare (ICCH). IEEE, 2012. http://dx.doi.org/10.1109/icch.2012.6724483.
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Cao, Liangliang, Jie Yu, Jiebo Luo e Thomas S. Huang. "Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression". In the seventeen ACM international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631292.
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Voloshina, Ekaterina, e Polina Leonova. "The Universal Database for Lexical Typology". In INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-1133-1140.
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The paper presents the principles of creating a database for research in lexical typology and describes the possibilities of its use as a linguistic resource. The database is built around semantic fields and frames, i. e. units of analysis in the frame-based theory of lexical typology. The database provides a universal format for storing the data; therefore, any project in lexical typology can be easily added. The database does not only store the data from previous research projects but allows anyone who wants to contribute to submit data via its web interface. The database includes examples provided by native speakers and manually annotated with translations, semantic fields, and frames, following the annotation principles adopted within the frame approach to lexical typology.
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Jia, Jimin, Nenghai Yu e Xian-Sheng Hua. "Annotating personal albums via web mining". In Proceeding of the 16th ACM international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1459359.1459421.
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Pattanaik, Vishwajeet, Shweta Suran e Dirk Draheim. "Enabling Social Information Exchange via Dynamically Robust Annotations". In iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3366030.3366060.
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