Web Annotation Research Articles

Smart Visualization for Online Aids Image Retrieval

Visual aids can be considered as a motivational tool in enhancing students’ attention and create positive perceptions. The use of new technologies has opened new possibilities to integrate online visual aids in the teaching process, which produce positive learning effects. In this paper, a novel technique employed to retrieve specific images based on the kind of query classification. The semantic dictionary built based on the specific classification correlate with the query intention. Singular Value Decomposition SVD training technique have been used to select the effective key templates in order to link the query with the web annotation directly. The present method can be considered as a strategic tool in the E-learning technique, which can provide variety of clustered images to help the students in creative and critical thinking skills and prevent the indoctrination method in learning the students. The qualitative results achieved high True Positive (TP) retrieved images that respect to the effectiveness of the E-learning task. Also, it provides a good 92% of learning reaction and superior learning behavior level.

PANNZER-A practical tool for protein function prediction.

The facility of next-generation sequencing has led to an explosion of gene catalogs for novel genomes, transcriptomes and metagenomes, which are functionally uncharacterized. Computational inference has emerged as a necessary substitute for first-hand experimental evidence. PANNZER (Protein ANNotation with Z-scoRE) is a high-throughput functional annotation web server that stands out among similar publically accessible web servers in supporting submission of up to 100,000 protein sequences at once and providing both Gene Ontology (GO) annotations and free text description predictions. Here, we demonstrate the use of PANNZER and discuss future plans and challenges. We present two case studies to illustrate problems related to data quality and method evaluation. Some commonly used evaluation metrics and evaluation datasets promote methods that favor unspecific and broad functional classes over more informative and specific classes. We argue that this can bias the development of automated function prediction methods. The PANNZER web server and source code are available at http://ekhidna2.biocenter.helsinki.fi/sanspanz/.

Entity-fishing: A DARIAH Entity Recognition and Disambiguation Service

This paper presents an attempt to provide a generic named-entity recognition and disambiguation module (NERD) called entity-fishing as a stable online service that demonstrates the possible delivery of sustainable technical services within DARIAH, the European digital research infrastructure for the arts and humanities. Deployed as part of the national infrastructure Huma-Num in France, this service provides an efficient state-of-the-art implementation coupled with standardised interfaces allowing an easy deployment on a variety of potential digital humanities contexts. Initially developed in the context of the FP9 EU project CENDARI, the software was well received by the user community and continued to be further developed within the H2020 HIRMEOS project where several open access publishers have integrated the service to their collections of published monographs as a means to enhance retrieval and access. entity-fishing implements entity extraction as well as disambiguation against Wikipedia and Wikidata entries. The service is accessible through a REST API which allows easier and seamless integration, language independent and stable convention and a widely used service-oriented architecture (SOA) design. Input and output data are carried out over a query data model with a defined structure providing flexibility to support the processing of partially annotated text or the repartition of text over several queries. The interface implements a variety of functionalities, like language recognition, sentence segmentation and modules for accessing and looking up concepts in the knowledge base. The API itself integrates more advanced contextual parametrisation or ranked outputs, allowing for the resilient integration in various possible use cases. The entity-fishing API has been used as a concrete use case to draft the experimental stand-off proposal, which has been submitted for integration into the TEI guidelines. The representation is also compliant with the Web Annotation Data Model (WADM). In this paper we aim at describing the functionalities of the service as a reference contribution to the subject of web-based NERD services. In this paper, we detail the workflow from input to output and unpack each building box in the processing flow. Besides, with a more academic approach, we provide a transversal schema of the different components taking into account non-functional requirements in order to facilitate the discovery of bottlenecks, hotspots and weaknesses. We also describe the underlying knowledge base, which is set up on the basis of Wikipedia and Wikidata content. We conclude the paper by presenting our solution for the service deployment: how and which the resources where allocated. The service has been in production since Q3 of 2017, and extensively used by the H2020 HIRMEOS partners during the integration with the publishing platforms.

Assessment of Annotation Needs of Botanists

Annotation (i.e., making comments on a resource) is an important part of the vision for the Semantic Web as defined by the standards set by the World Wide Web Consortium (W3C). Its goal is to make Internet-published information and data, machine-readable to better utilize it. Despite the important role that annotation plays in the Semantic Web, many cultural heritage institutions have been slow to adopt it. The access to open historical biological literature hosted in digital libraries, like the Biodiversity Heritage Library (BHL), has improved the efficiency of biodiversity research, especially in the taxonomic field. This amount of information has even greater potential for research if annotation capabilities are incorporated within those legacy digital repositories. As part of the project Consumers as Creators, developed by the Missouri Botanical Garden (MOBOT) with partners at Saint Louis University (SLU), the Web annotation needs of the botanical community were analyzed. Likewise, the practicality of using existing annotation tools to satisfy this community’s particular needs was assessed, including technical and operational considerations. To do so, 15 users of a botanical virtual library from five institutions were interviewed. Their answers were analyzed and classified taking into account the user role and purpose. Desirable functionalities of annotation software were classified into three orders of priority (Must, Should, and Could). Subsequently, six open-source annotation tools were evaluated (i.e. Digilib, hypothes.is, Pundit Annotator Pro, Recogito, rerum, and VGG Annotator) to explore if they fulfilled the annotation needs of botanists. The selected annotation tools were installed (when necessary), assessed based on different functional aspects, and their advantages and disadvantages were identified. Finally, a proof-of-concept prototype was developed to exemplify how those needs could be met within a digital library platform. Botanicus, a free portal to historic botanical literature from the Peter H. Raven Library at MOBOT, and rerum, functioning as a repository of annotations, were used to explore the implementation of a minimal subset of these requirements. A summary of the results of the assessment, the lessons learned and some of the best practices recommended are presented.

Semantic Annotation of Web of Things Using Entity Linking

The web of things (WoT) improves syntactic interoperability between internet of things (IoT) devices by leveraging web standards. However, the lack of a unified WoT data model remains a challenge for the semantic interoperability. Fortunately, semantic web technologies are taking this challenge over by offering numerous semantic vocabularies like the semantic sensor networks (SSN) ontology. Although it enables the semantic interoperability between heterogeneous devices, the manual annotation hinders the scalability of the WoT. As a result, the automation of the semantic annotation of WoT devices becomes a prior issue for researchers. This paper proposes a method to improve the semi-automatic semantic annotation of web of things (WoT) using the entity linking task and the well-known ontologies, mainly the SSN.

Reading and connecting: using social annotation in online classes

PurposeThe COVID-19 pandemic has forced many instructors to rapidly shift to online/distance teaching. With a narrow preparation window, many instructors are at a loss of strategies that are both effective in responding to the crisis and compatible with their professional practices. One urgent need in classrooms at all levels is to support social reading of course materials. To fulfill this need, this paper aims to present a systematic literature review on using Web annotation in K-12 and higher education to provide practical and evidence-based recommendations for educators to incorporate social annotation in online teaching.Design/methodology/approachThis paper presents a systematic literature review of the use of Web annotation in formal education. The authors reviewed 39 articles that met the inclusion criteria and extracted the following information from each article: level of education, subject area, learning theory, learning activity design, Web annotation technology, research methods and learning outcomes. Studies were further analyzed and synthesized by the genre of learning activity design.FindingsThe authors identified five types of social annotation activity design: processing domain-specific knowledge, supporting argumentation and inquiry, improving literacy skills, supporting instructor and peer assessment and connecting online learning spaces. In addition, the authors developed practical recommendations on setting pedagogical goals, selecting annotation tools, deciding instructor involvement and developing evaluation strategies.Originality/valueThis study provides a timely response to online/distance teaching under the COVID-19 pandemic. It is a hope that these identified application areas, in combination with four practical recommendations, would provide pragmatic and evidence-based support for educators to engage learners in reading, learning and connecting.

Blast2Fish: a reference-based annotation web tool for transcriptome analysis of non-model teleost fish

BackgroundTranscriptome analysis by next-generation sequencing has become a popular technique in recent years. This approach is quite suitable for non-model organism study, as de novo assembly is independent of prior genomic sequences of organisms. De novo sequencing has benefited many studies on commercially important fish species. However, to understand the functions of these assembled sequences, they still need to be annotated with existing sequence databases. By combining Basic Local Alignment Search Tool (BLAST) and Gene Ontology analysis, we were able to identify homologous sequences of assembled sequences and describe their characteristics using pre-defined tags for each gene, though the above conventional annotation results obtained for non-model assembled sequences was still associated with a lack of pre-defined tags and poorly documented records in the database.ResultsWe introduced Blast2Fish, a novel approach for performing functional enrichment analysis on non-model teleost fish transcriptome data. The Blast2Fish pipeline was designed to be a reference-based enrichment method. Instead of annotating the BLAST single top hit by a pre-defined gene-to-tag database, we included 500 hits to search related PubMed articles and parse biological terms. These descriptive terms were then sorted and recorded as annotations for the query. The results showed that Blast2Fish was capable of providing meaningful annotations on immunology topics for non-model fish transcriptome analysis.ConclusionBlast2Fish provides a novel approach for annotating sequences of non-model fish. The reference-based strategy allows annotation to be performed without pre-defined tags for each gene. This method strongly benefits non-model teleost fish studies for gene functional enrichment analysis.

Things2Vec: Semantic Modeling in the Internet of Things With Graph Representation Learning

The advent of fifth generation (5G) enables the Internet of Things (IoT) to connect a massive number of things. The interaction and communication among these things generate an enormous amount of context-aware data that is semantically diverse. Traditional data representation approaches, such as semantic annotation, ontology, and semantic Web technology are rule based, which lack flexibility and adaptability when applied to IoT. To address the challenge, this article mainly focuses on the problem of semantic representation, which is essential for processing and fusion of IoT data. To serve as a bridge, we propose a high-level framework, namely, Things2Vec, which aims to produce the latent semantic representations from the interaction of things through the graph embedding technique. These semantic representations benefit various IoT semantic analysis tasks, such as the IoT service recommendation and automation of things. In Things2Vec, we utilize the graph to model the function sequence relationships that are generated by the interaction of things, which is called the IoT context graph. Since these function sequence relationships are heterogeneous in terms of semantics, it causes general graph embeddings to fail to learn complete information. Thus, we propose a biased random walk procedure, which is tailored to capture the neighborhoods of nodes with different types of semantic relationships. Extensive experiments are carried out, and our results show that the proposed method can effectively capture the semantic relationships among context-aware data in IoT.

The role of social annotation in facilitating collaborative inquiry-based learning

This study examines whether a social annotation (SoAn) tool may help facilitate students' collaborative inquiry-based learning (CIL). This kind of SoAn tool enables students to bookmark, highlight, annotate, share, discuss, and collaborate on information sources collected for their collaborative inquiry-based learning assignments (CILA) online. An analysis of these interactions may help instructors understand how students learn and may provide useful advice to them during the learning processes. This study invited 377 freshmen or sophomores from three different general education courses at a Hong Kong university to try out a self-developed SoAn tool, the Web Annotation and Sharing Platform (WASP). Quantitative data were collected from a questionnaire survey, activity log files, and CILA marks, while qualitative data were collected from individual interviews. The survey revealed that WASP facilitated the early stages of students' CIL in identifying inquiry questions and searching information. Students agreed that the SoAn tool is useful and effective in facilitating their CIL, but there were no significant changes in their CILA marks. This study revealed that no correlation was found among students' perception of the CIL/SoAn tool, the usage of the SoAn tool, and their CILA marks. With students' high perceived usefulness and effectiveness of SoAn tools, further studies should focus on how to better utilise SoAn tools in students' CIL.

Civic Writing on Digital Walls

Civic writing has appeared on walls over centuries, across cultures, and in response to political concerns. This article advances a civic interrogation of how civic writing is publicly authored, read, and discussed as openly accessible and multimodal texts on digital walls. Drawing upon critical literacy perspectives, we examine how a repertoire of 10 civic writing practices associated with open web annotation (OWA) helped educators develop critical literacy. We introduce a social design experiment in which educators leveraged OWA to discuss educational equity across sociopolitical texts and contexts. We then describe a single case of OWA conversation among educators and use discourse analysis to examine shifting situated meanings and political expressions present in educators’ civic writing practices. We conclude by considering implications for theorizing the marginality of critical literacy, designing learning environments that foster educators’ civic writing, and facilitating learning opportunities that encourage educators’ civic writing across digital walls.

Improving spaCy dependency annotation and PoS tagging web service using independent NER services.

Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that including named entity annotations can improve the speed and quality of dependency parses. As part of BLAH5, we built a web service delivering improved dependency parses by taking into account named entity annotations obtained by third party services. Our evaluation shows improved results and better speed.

Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm

BackgroundShared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents in real time. To address this pressing need, a novel track called “Technical interoperability and performance of annotation servers” was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online biomedical named entity recognition systems of interest for medicinal chemistry applications.ResultsA total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format considerations, technical metrics and functional specifications. Participating annotation servers were implemented in seven different programming languages and covered 12 general entity types. The continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502 requests from three different document provider settings. The median response time was below 3.74 s, with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over 100,000 requests in a 5-day period.ConclusionsThe presented track was a novel experimental task that systematically evaluated the technical performance aspects of online entity recognition systems. It raised the interest of a significant number of participants. Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text documents.

Text-mining BHL: towards new interfaces to the biodiversity literature

The taxonomic literature is one of the largest resources of information on biodiversity, both current and in the past. Unlike many scientific disciplines this literature remains perpetually relevant as successive taxonomic work builds upon those earlier foundations. Projects such as the Biodiversity Heritage Library (BHL) have greatly increased access to that literature, as have numerous independent digitisation efforts by museums, herbaria, and publishers. But the focus of this access has been human readers, with limited use of text mining tools, mostly focussed on extracting taxonomic names. This talk explores other kinds of data that can be extracted from text on BHL and elsewhere, focusing on taxonomic names, geographic localities and specimen codes in the context of the BioStor project (https://biostor.org, Page 2011). The problem of finding taxonomic names in text has been well studied (e.g., Akella et al. 2012), and new BHL content is continuously indexed by names. Despite this, there is only weak linkage between taxonomic name databases and BHL. Even projects that create these links (e.g., BioNames, Page 2013) do not enable links in the reverse direction. In other words, a BHL reader is unaware whether the appearance of a name on a page is the first publication of that name, nor are they told of the fate of a name in subsequent research. The absence of these links reduces the value of BHL to working taxonomists. In addition to taxonomic names, a typical taxonomic paper often contains specimen codes. Extracting these from text and linking them to digital representations, such as occurrence records in GBIF, opens up the possibility to provide detailed provenance for occurrence data, as well as citation-based metrics for the utility of natural history collections. Taxonomic papers are also often rich in geographic information. A simple method for extracting locality information from text is to search for latitude and longitude coordinates, and BioStor currently does this. To date some 83,000 individual point localities have been extracted (Fig. 1 ). These are used to provide a simple geographic search interface in BioStor, and are also harvested by JournalMap (Karl et al. 2013). But these localities are not linked to the original location in the source text, nor are they linked to any associated specimens, so they cannot be interpreted as occurrences that could be harvested by GBIF. If the goal is to contribute to GBIF then we need tools that can parse locality information and link that to associated specimens. A general framework for handling data on taxonomic names, specimens, and geographic localities in text is to treat them as annotations (Batista-Navarro et al. 2017). By modelling annotations using the Web Annotation Data Model (https://www.w3.org/TR/annotation-model/ ) we can incorporate these annotations into biodiversity knowledge graphs (Page 2016). We can also combine these annotations with new standards for describing digitised content, such as the International Image Interoperability Framework (IIIF, https://iiif.io). The implications of this approach for developing new interfaces to the biodiversity literature will be discussed.

Open Web annotation as collaborative learning

This paper describes the use of open Web annotation (OWA) for collaborative learning among online communities. OWA is defined by the open standards, principles, and practices associated with the open Web. Specifically, this case study examines collaborative learning mediated by the OWA technology Hypothesis, a standards-compliant and open-source technology that situates collaboration in texts-as-contexts. Hypothesis OWA supports a repertoire of six collaborative learning practices: Affording multimodal expression, establishing connections across contexts, archiving activity, visualizing expertise and cognition, contributing to open educational resources, and fostering open educational practices. The use of Hypothesis OWA is then described in three online communities associated with scientific research and communication, educator professional development, and Web literacy and fact-checking. The article concludes by advancing three broad questions and related research agendas regarding how OWA as collaborative learning attends to linkages among formal and informal learning environments, the growth of both open educational resources and practices, and the use of open data as learning analytics.

Upgrading security and protection in ear biometrics

Biometrics is being widely accepted for user authentication across the globe. Integration of biometrics in the daily life provokes the need to design secure authentication systems. This study proposes the use of outer ear images as a biometric modality. The comparable complexity between the human outer ear and face in terms of its uniqueness and permanence has increased interest in the use of ear as a biometric. However, similar to face recognition, it poses challenges of variation in illumination, contrast, rotation, scale and pose. Owing to the extensive work in the field of computer vision using convolutional neural networks (CNNs), its feasibility in the field of ear biometrics has been presented in this work. The proposed technique uses a CNN as a feature extractor and a support vector machine (SVM) for the classification task. The joint CNN-SVM framework is used for mapping ear images to random base- n codes. The codes are further hashed using the secure hash algorithm SHA-3 to generate secure ear templates. The feasibility of the proposed technique has been evaluated on annotated web ears dataset. This work demonstrates 12.52% average equal error rate without any image pre-processing, which shows that the proposed approach is promising in the field of secure ear biometrics.

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal ("may cause") relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.

Semantic Web Annotation using Deep Learning with Arabic Morphology

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language.

Configurable web-services for biomedical document annotation

The need to efficiently find and extract information from the continuously growing biomedical literature has led to the development of various annotation tools aimed at identifying mentions of entities and relations. Many of these tools have been integrated in user-friendly applications facilitating their use by non-expert text miners and database curators. In this paper we describe the latest version of Neji, a web-services ready text processing and annotation framework. The modular and flexible architecture facilitates adaptation to different annotation requirements, while the built-in web services allow its integration in external tools and text mining pipelines. The evaluation of the web annotation server on the technical interoperability and performance of annotation servers track of BioCreative V.5 further illustrates the flexibility and applicability of this framework.

SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes

BackgroundDespite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French. Yet a majority of natural language processing or semantic tools as well as domain terminologies or ontologies are only available in English, and cannot be readily applied to other languages, due to fundamental linguistic differences. However, semantic resources are required to design semantic indexes and transform biomedical (text)data into knowledge for better information mining and retrieval.ResultsWe present the SIFR Annotator (http://bioportal.lirmm.fr/annotator), a publicly accessible ontology-based annotation web service to process biomedical text data in French. The service, developed during the Semantic Indexing of French Biomedical Data Resources (2013–2019) project is included in the SIFR BioPortal, an open platform to host French biomedical ontologies and terminologies based on the technology developed by the US National Center for Biomedical Ontology. The portal facilitates use and fostering of ontologies by offering a set of services –search, mappings, metadata, versioning, visualization, recommendation– including for annotation purposes. We introduce the adaptations and improvements made in applying the technology to French as well as a number of language independent additional features –implemented by means of a proxy architecture– in particular annotation scoring and clinical context detection. We evaluate the performance of the SIFR Annotator on different biomedical data, using available French corpora –Quaero (titles from French MEDLINE abstracts and EMEA drug labels) and CépiDC (ICD-10 coding of death certificates)– and discuss our results with respect to the CLEF eHealth information extraction tasks.ConclusionsWe show the web service performs comparably to other knowledge-based annotation approaches in recognizing entities in biomedical text and reach state-of-the-art levels in clinical context detection (negation, experiencer, temporality). Additionally, the SIFR Annotator is the first openly web accessible tool to annotate and contextualize French biomedical text with ontology concepts leveraging a dictionary currently made of 28 terminologies and ontologies and 333 K concepts. The code is openly available, and we also provide a Docker packaging for easy local deployment to process sensitive (e.g., clinical) data in-house (https://github.com/sifrproject).

OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies.

Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.