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This paper stems from the project A World of Possibilities. Modal pathways over an extra-long period of time: the diachrony of modality in the Latin language (WoPoss) which involves a corpus-based approach to the study of modality in the history of the Latin language. Linguistic annotation and, in particular, the semantic annotation of modality is a keystone of the project. Besides the difficulties intrinsic to any annotation task dealing with semantics, our annotation scheme involves multiple layers of annotation that are interconnected, adding complexity to the task. Considering the intricacies of our fine-grained semantic annotation, we needed to develop well-documented schemas in order to control the consistency of the annotation, but also to enable an efficient reuse of our annotated corpus. This paper presents the different elements involved in the annotation task, and how the description and the relations between the different linguistic components were formalised and documented, combining schema languages with XML documentation. [ABSTRACT FROM AUTHOR]

• Generation of a specific data ontology for underground built heritage spaces. • Specific data structure for the documentation, management and conservation of the typology of Underground built heritage spaces. • Annotation of the alphanumeric data of the heritage asset on the three-dimensional model using web applications, facilitating the reading and understanding of the heritage information. Progress in the field of documentation and safeguarding of heritage buildings has evolved considerably since the implementation and use of information models. However, these models are being refined and particularised in an attempt to cover the full range of elements to be protected. There is one case that has not yet been sufficiently studied, namely the specific typology of underground built heritage spaces, where the procedures designed for more conventional buildings are not applicable. A lack of consensus has been detected in the standardisation of data processing, which is fundamentally since these spaces do not respond to the classification nor as a building or as an archaeological site. In addition, the morphology of these spaces, -with irregular surfaces of complex geometry-, makes the three-dimensional representation indispensable to record concise and exact documentation. This research proposes a specific methodology for processing heritage data from CIDOC-based models for underground built heritage spaces that fills the identified gap. The research also proposes solutions to the ontological problem of linking information and annotated data, establishing a methodology for performing this kind of work directly on three-dimensional models using web applications. This simplification of the procedure facilitates the reading and multidisciplinary access to the information of the agents involved in safeguarding heritage, replacing two-dimensional projections representation with the introduction of three-dimensional tools. [ABSTRACT FROM AUTHOR]

The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems. [ABSTRACT FROM AUTHOR]

This article evaluates the integration of digital editions, computational text analysis and digital scholarly editing in the context of an introductory undergraduate course on Italian literature and digital humanities taught at a US university. It offers specific examples of employing the apparatus of several digital platforms dedicated to the study of foundational authors in the Italian literary tradition (Dante, Petrarch, Boccaccio and Leopardi), and of gaining familiarity with a suite of digital tools for text analysis and editing, namely Voyant Tools, Recogito, Oxygen, Gephi, Transkribus Lite and OpenRefine. The discussion of digital project interfaces examines the student user experience of different design approaches, while the illustrations of tool exercises explore how these could support the close attention to a text and facilitate the navigation between its micro and macro frameworks of interpretation. The article furthermore suggests that digital text analysis could reinforce student appreciation of the signifying value of textual form and genre, and that the pedagogical method of digital text editing creates opportunities for situated learning. In conclusion, it argues that the academic work of students at the undergraduate level could be harnessed by the scaffolded methods of faculty-led digital research projects and contribute to the creation of public knowledge. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Aiming at the semantic missing and incomplete problems in the process of image organization and retrieval, a framework for semantic description of images in social media is proposed to enrich the existing theoretical system of image description, improve the efficiency and utilization of image retrieval, and provide a reference for the realization of the automatic semantic annotation of images. [Method/Process] First, we conducted a survey and analysis of research progress related to image description both domestically and internationally, summarizing the existing theories of image description and annotation, metadata specifications, and related technical methods. Second, based on the image metadata standards and the theory of hierarchical and categorical description of image features, we constructed a semantic description framework for social media images, focusing on seven layers: external feature layer, content layer, object layer, relationship layer, scene layer, event layer, and emotional layer. We also elaborated in detail the various semantic layers and their interrelationships. Finally, we verified the feasibility of the image semantic description framework by describing the examples of character images and landscape images. [Results/Conclusions] The results of the descriptive examples of character images and landscape images indicate that the image semantic description framework can eliminate the "semantic gap" in image description through semantic associations between different layers, and achieve a multi-faceted, multi-dimensional, and multi-level structured and semantic description of the external and content features of images. It has strong portability and flexibility. However, there are also certain limitations and areas for improvement in this paper: 1) Based on the image semantic description framework proposed in this paper, a prototype system based on image annotation needs to be developed; 2) The images posted by users on social media are closely related to the situation, and they are more likely to express emotions. In the future, more research on the semantic layer of images can be conducted based on the text information posted by users; 3) Future research can further explore the application of deep learning in image and text fusion to achieve more accurate event and emotion recognition. By constructing a more complex neural network structure, the event and emotion information in the image can be deeply mined and fused; 4) When describing images, the study should pay attention not only to static visual features, but also to consider the dynamic course of events. Future frameworks could attempt to combine static and dynamic information to provide richer, more vivid descriptions of images. [ABSTRACT FROM AUTHOR]

• Use of CNN to segment 3D reality capture 3D point clouds of built heritage. • Use ML for identification of the period of monuments' building parts. • Trained CNN on Cypriot built heritage. • Dataset of segmented and annotated built heritage examples. • Open, online accessible tool for education. The article presents a workflow based on Deep Neural Networks (DNNs) and Support Vector Machine (SVM) for identifying architectural stylistic influences of segmented building parts of Cypriot historical architecture in 3D. The research contributes in the field of Digital Cultural Heritage (DCH) by applying Machine Learning (ML) and Deep Learning (DL) on recently published DCH data [1] , with the aim to accelerate the segmentation and annotation process of Historic Building Information modelling (HBIM) that is currently based on time-consuming manual processes. The method presented works on reality captured data by 3D documentation techniques, precisely, Terrestrial Laser Scanning (TLS) or Photogrammetry. This workflow was developed to enable the operation of an online platform, 1 1 https://annfass-srv.cs.ucy.ac.cy. which also provides access to the building data presented here. Ultimately, the results of the presented method are accessible to scholars and students via this platform which provides multiple functionalities for researchers in the field to use. [ABSTRACT FROM AUTHOR]

In the contribution, we provide a theory-based and corpus-verified description of expressions for measure in Czech. We demonstrate that the measure expressions may modify quantity of entities (approximately ten boys), internal characteristics of events (he works a lot), properties (very big) and relations (completely without sound). We distinguish between the measure expressions that are an answer to the question To what extent? (Extent-modifiers) and expressions that modify an answer to the question How many? (Quantity-modifiers). The Extent-modifiers are formally, structurally and semantically more diverse than the Quantity-modifiers. For the Quantity-modifiers a list of forms and functions is provided. Theoretical knowledge stemming from the analysis will subsequently be used to improve the annotation in the Prague Dependency Treebanks. It can be also useful for other semantically-oriented descriptions of language. [ABSTRACT FROM AUTHOR]

When treating structured health-system-related knowledge, the establishment of an over-dimension to guide the separation of entities becomes essential. This is consistent with the information retrieval processes aimed at defining a coherent and dynamic way - meaning by that the multilevel integration of medical textual inputs and computational interpretation - to replicate the flow of data inserted in the clinical records. This study presents a strategic technique to categorize the clinical entities related to patients affected by neurodegenerative diseases. After a pre-processing range of tasks over paper-based and handwritten medical records, and through subsequent machine learning and, more specifically, natural language processing operations over the digitized clinical records, the research activity provides a semantic support system to detect the main symptoms and locate them in the appropriate clusters. Finally, the supervision of the experts proved to be essential in the correspondence sequence configuration aimed at providing an automatic reading of the clinical records according to the clinical data that is needed to predict the detection of neurodegenerative disease symptoms. [ABSTRACT FROM AUTHOR]

The development of Social Media has caused an incredible change in the way people communicate and share information. It provides a set of platforms, web-based applications and services that facilitate the collaborative creation of content and the sharing of ideas and interests. Since its inception, Social Media technologies have been increasingly used in different fields that have integrated them into their daily lives. In Software Engineering, for example, it has caused a disruptive change in the software development model, changing the way that the projects are approached by promoting collaborative environments. This effect has led to the proliferation of the software development communities where huge amounts of information are published every day. Therefore, when a project is started and a development team needs to be assembled, it is difficult to select and identify the most suitable developer profiles for such a project by considering all the disseminated information. To solve this problem, we have proposed an ontology-based system to help find a suitable group of developers to develop a project. The system uses web services to extract user profiles from GitHub, and semantic technologies to represent and annotate the features of the extracted data. Then, when the system receives the natural language description of the project to be developed, it identifies and extracts relevant concepts such as technologies, platforms, tools, among others. As a result, it analyzes the extracted information and lists the most suitable developers to assemble a team of developers with the right technical skills to tackle the software project. For evaluation purposes, we generated a random list of GitHub profiles, and collected a corpus of documents describing research projects and patents. The system produced very promising results, achieving a MAP@5 and F-Measure of 0.68. • Setting up a team with the right skills is a key for project success. • Features gathered from project descriptions help identify required skills. • Semantic technologies facilitates the integration and processing of heterogeneous data. • Semantically-enriched recommender system matches projects with developer profiles. [ABSTRACT FROM AUTHOR]

Semantic annotation of images is the process of assigning metadata in the form of captions to a digital image. This is an important process for the indexing and searching of images in a big database. In this paper, we present the framework of automatic semantic annotation of images and explore the effectiveness of using it to annotate images based on both the image's low-level features and its surrounding text. In the proposed framework, the image's features have been extracted by using convolutional neural networks, while words in the surrounding text have been represented by word-embedding vectors. Both modalities are further processed using recurrent neural networks with long short-term memory cells that possess an attention mechanism to generate an annotation sentence that describes the image. Empirical evaluations of the proposed framework, acquired using a news dataset, show promising performance results and are comparable to the results of recent image annotation systems. The produced semantic annotations in free-text format can be further converted into a structured resource description framework that enables more expressive queries across a diverse source of images. [ABSTRACT FROM AUTHOR]

Networked robots will play an important role in lunar exploration. Communication is key to enable cooperation among robots for information sharing, and to remotely control robots with lower degree of autonomy from a lander or habitat. Operators and scientists must be able to make sound decisions on communication availability before or during sending robots to regions of interest for exploration. In this work we have a closer look at the communication coverage prediction for lunar exploration. We present an interdisciplinary and modular framework, which exploits terrain information to predict the data rate for exploring robots. Additionally, we create intuitively usable coverage maps for operators and scientists, and show how connectivity can be improved in unstructured environments by using a relay rover. This paper provides an overview of this framework, details on individual framework components, and simulation results for two exemplary exploration scenarios. • Wireless communication is key for cooperating and remote-controlled robots. • Communication coverage is limited caused by signal blockage due to the terrain. • A framework predicting communication coverage for networked robots is implemented. • Radio propagation models are taken into account. • Intuitively usable maps to make sound decisions by operators are created. • A communication relay can greatly enhance communication link availability. [ABSTRACT FROM AUTHOR]

The ubiquitous availability of datasets has spurred the utilization of Artificial Intelligence methods and models to extract valuable insights, unearth hidden patterns, and predict future trends. However, the current process of data collection and linking heavily relies on expert knowledge and domain-specific understanding, which engenders substantial costs in terms of both time and financial resources. Therefore, streamlining the data acquisition, harmonization, and enrichment procedures to deliver high-fidelity datasets readily usable for analytics is paramount. This paper explores the capabilities of SemTUI , a comprehensive framework designed to support the enrichment of tabular data by leveraging semantics and user interaction. Utilizing SemTUI, an iterative and interactive approach is proposed to enhance the flexibility, usability and efficiency of geospatial data enrichment. The approach is evaluated through a pilot case study focused on urban planning, with a particular emphasis on geocoding. Using a real-world scenario involving the analysis of kindergarten accessibility within walking distance, the study demonstrates the proficiency of SemTUI in generating precise and semantically enriched location data. The incorporation of human feedback in the enrichment process successfully enhances the quality of the resulting dataset, highlighting SemTUI's potential for broader applications in geospatial analysis and its usability for users with limited expertise in manipulating geospatial data. [ABSTRACT FROM AUTHOR]

Background: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. Methods: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. Results: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. Conclusion: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus. [ABSTRACT FROM AUTHOR]

This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process. [ABSTRACT FROM AUTHOR]

The paper presents work in progress on the compilation and automatic annotation of a dataset comprising examples of stative verbs in parallel Bulgarian-Russian corpora with the goal of facilitating the elaboration of a classification of stative verbs in the two languages based on their lexical and semantic properties. We extract stative verbs from the Bulgarian and the Russian WordNets with their assigned conceptual information (frames) from FrameNet. We then assign the set of probable Bulgarian and Russian stative verbs to the verb instances in a parallel Bulgarian-Russian corpus using WordNet correspondences to filter out unlikely stative candidates. Further, manual inspection will ensure high quality of the resource and its application for the purposes of semantic analysis. [ABSTRACT FROM AUTHOR]

Increasing videos available in educational content repositories makes searching difficult, and recommendation systems have been used to help students and teachers receive a content of interest. Speech is an important carrier of information in video lectures and is used by content-based video recommendation systems. Although automatic speech recognition (ASR) transcripts have been used in modern video recommendation systems, it is not clear how annotation techniques work with noisy text. This article presents an analysis on a set of semantic annotation techniques when applied to text extracted from video lecture speech and their impact on two tasks: annotation and similarity analysis. Experiments show that topic models have good results in this scenario. Besides, a new benchmark for this task has been created and researchers can use it to evaluate new techniques. [ABSTRACT FROM AUTHOR]

The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document's repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation. [ABSTRACT FROM AUTHOR]

This article, which forms the second part of a two-part article in this volume, reflects on what we argue to be the most crucial element of any Linked Data enterprise: its community. The first section discusses a variety of funded and unfunded activities conducted under the aegis of Pelagios, and specifically two 'small grant' programmes that dramatically expanded its scope and reach. The second section describes the evolution of Pelagios as a 'community of practice', in which the work of the initiative, conceived of in terms of both method and tool development, has been facilitated by, and coordinated with, individuals and groups external to its investigative teams. This growing and maturing community has ultimately been realized in the foundation of a formal voluntary organization: the Pelagios Network Association. The paper concludes with brief reflections on some interlinked sister initiatives that have emerged from the Pelagios community and continue to support scholars and practitioners working in related fields. [ABSTRACT FROM AUTHOR]

This article presents a case study for the digital mapping of an ancient Greek geographical compendium, the Sketch of Geography by Agathemerus. We examine various possibilities of investigation, including semantic annotation, georeferencing and network analysis, to verify how the digital mapping of a text can contribute to a better understanding of its underlying spatial perception. We examine the following aspects: spatial distribution, functionality and frequency of place types, semantic/symbolic definition of boundaries, place connectivity and problems of textual corruption. In the conclusion, we show that, while the general perspective of the work is programmatically speculative, Agathemerus' way of modelling the world is navigational and pragmatic. A predominantly non-cartographic perspective dictates a way of reasoning about space that is highly semantical in the definition of important landmarks and spatial relations. However, it also determines a strongly navigational approach in the treatment of geographical problems. Finally, we emphasize the value of an integrated semantic and mapped approach to the investigation of premodern geographies, and the opportunities of using these methods to address old and new research questions. [ABSTRACT FROM AUTHOR]

This article provides a short history of the methods and tools developed by the Pelagios initiative: a series of seven projects dedicated to linking digital historical resources based on the geographic places to which they relate and refer. The first section of the article situates the work within the wider field of semantic and geospatial technologies. It then describes the initial development of the Pelagios methodology through an open consortium of ancient Mediterranean data resource providers, in association with the Pleiades Gazetteer of the Ancient World. The second section details how this approach was expanded for application in other historical and archaeological periods and domains, with a special focus on tool development. In particular, we reflect on the development and application of Recogito, an online semantic geoannotation platform that allows anyone to identify place references within historical texts, imagery or datasets. [ABSTRACT FROM AUTHOR]

Background: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus. Methods: We annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models. Results: This resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure. Conclusions: Our results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm%5fen.html. The methods are generalizable to other languages with similar available sources. [ABSTRACT FROM AUTHOR]

Background: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus. Methods: We annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models. Results: This resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure. Conclusions: Our results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm%5fen.html. The methods are generalizable to other languages with similar available sources. [ABSTRACT FROM AUTHOR]

Background: The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus.Methods: We annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models.Results: This resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure.Conclusions: Our results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm_en.html . The methods are generalizable to other languages with similar available sources. [ABSTRACT FROM AUTHOR]

With the development and application of Web Semantic, users are no longer satisfied with basic metadata descriptions such as titles and link texts and string-matching search results. They hope that the resource description can provide the theme ideas, topics, and topics involved in the resource. Potential semantic information contains documents such as teaching methods and knowledge-concept relationships. This research starts from the demand for semantic annotation of resources in the process of resource library construction and sharing, and uses the LDA model to semantically model the document resources in the resource library to mine potential topics in the document. From “document-topic-keyword” scheme, the semantic description of teaching resources at different levels enriches the metadata attributes and content of resources, and adds more related topics and corresponding keyword descriptions related to disciplines, teaching content, teaching methods, etc., providing resource retrieval and sharing. The experimental results show that the LDA model can catalogues teaching resources from a macro perspective, and model LDA on teaching subject resources of the same teaching content. It can mine the inherent semantic topic features and detailed differences of resources. The final performance analysis verifies LDA’s advantages of the model in parallel computing in the big data environment. [ABSTRACT FROM AUTHOR]

Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web's real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools' degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented. [ABSTRACT FROM AUTHOR]

In the big data era, the volume of streaming data produced by sensor networks is staggeringly large that enables business intelligence to make well-informed decisions on emerging modern applications. Performing the data analytics and query processing over the fast arriving data streams is a tedious process. The semantic annotation of the data stream provides a high-level description, and a semantic context supports intelligent querying and data analytics. This paper presents a framework called SEmantic Annotation over Summarized sensOr Data stReam (SEASOR) that includes summarization, semantic annotation, and query processing that facilitates sensor data stream analytics. The summarization merges these types of stream values to increase the query performance and decrease the memory space. The semantic annotation is scripted with the help of application-dependent base ontology that extends the Semantic Sensor Network (SSN) ontology. The annotation of the sensor stream provides detailed descriptions for the observation of sensors using the base ontology, and it divides the streaming sensor data into several subsets according to the sensing features. The domain model enables the query processor to access the relevant results via an annotated Resource Description Framework (RDF). The query processor uses the extended SPARQL (Cs-SPARQL) to access only the relatively small subset via an annotated RDF file and allows extending the query processing to support windows and the parallel processing of data streams. The experimental results prove that the proposed SEASOR provides timely answers to the user queries and achieves better performance in terms of result accuracy by 95%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for assessing text coherence. Central to this approach is an ontology‐based representation of text, which captures the level of relatedness between consecutive sentences via ontologies. Our method encompasses annotating text using ontological concepts and assessing text coherence based on relatedness measurement among these concepts. The ontology‐based relatedness measurement method used in this study considers various types of relationships in ontologies and derived relationships via an inference engine for computing relatedness. We hypothesized that rich variety of relationships and inferred facts in ontologies would improve the success of text coherence assessment. Our results demonstrate that the use of ontologies yields to coherence values that have a higher correlation with human ratings. [ABSTRACT FROM AUTHOR]

There is a large amount of semi-structured information on Web pages. Comprehensive and accurate annotation of Web page information with uniform semantics can enhance the use value of information and provide support for Web site information integration. According to the characteristics of semi-structured information on Web pages, a semantic annotation method based on header recognition and data item classification is proposed. Firstly, a description model is constructed for the domain to be annotated. Secondly, header recognition is used to annotate data items on extracted pages. For those data items fail to be annotated by header recognition, feature vectors are constructed based on the feature sets in the domain description model and semantics of those data items are annotated by the classification results of back-propagation neural network. The proposed method is tested on 19,657 data items in the domain of agricultural product price and 8089 data items in the domain of recruitment information. The annotation precision is 97.39% and 95.67% respectively, and the annotation recall is 95.41% and 95.67%, respectively. These results show that the proposed method can annotate semi-structured information on Web pages accurately and completely. [ABSTRACT FROM AUTHOR]

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications. [ABSTRACT FROM AUTHOR]

Corpora of child speech and child-directed speech (CDS) have enabled major contributions to the study of child language acquisition, yet semantic annotation for such corpora is still scarce and lacks a uniform standard. Semantic annotation of CDS is particularly important for understanding the nature of the input children receive and developing computational models of child language acquisition. For example, under the assumption that children are able to infer meaning representations for (at least some of) the utterances they hear, the acquisition task is to learn a grammar that can map novel adult utterances onto their corresponding meaning representations, in the face of noise and distraction by other contextually possible meanings. To study this problem and to develop computational models of it, we need corpora that provide both adult utterances and their meaning representations, ideally using annotation that is consistent across a range of languages in order to facilitate cross-linguistic comparative studies. This paper proposes a methodology for constructing such corpora of CDS paired with sentential logical forms, and uses this method to create two such corpora, in English and Hebrew. The approach enforces a cross-linguistically consistent representation, building on recent advances in dependency representation and semantic parsing. Specifically, the approach involves two steps. First, we annotate the corpora using the Universal Dependencies (UD) scheme for syntactic annotation, which has been developed to apply consistently to a wide variety of domains and typologically diverse languages. Next, we further annotate these data by applying an automatic method for transducing sentential logical forms (LFs) from UD structures. The UD and LF representations have complementary strengths: UD structures are language-neutral and support consistent and reliable annotation by multiple annotators, whereas LFs are neutral as to their syntactic derivation and transparently encode semantic relations. Using this approach, we provide syntactic and semantic annotation for two corpora from CHILDES: Brown’s Adam corpus (English; we annotate ≈\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\approx$$\end{document} 80% of its child-directed utterances), all child-directed utterances from Berman’s Hagar corpus (Hebrew). We verify the quality of the UD annotation using an inter-annotator agreement study, and manually evaluate the transduced meaning representations. We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages. [ABSTRACT FROM AUTHOR]

Tabular data are often published on the Web without meta-data or a clear description of their content, which makes it difficult for such data to be utilised. Semantic labelling has been proposed in the literature to address this problem and facilitate the utilisation of tabular data (e.g., for data integration). It is the assignment of meanings (labels or annotations) to tabular data. Several semantic labelling approaches have been developed that often predict the labels in a (semi-)automatic fashion. However, semantic labelling approaches that assign labels to textual columns are not suitable for predicting the labels of numeric columns due to missing values, rounding errors, or data expiry. To overcome this, we proposed a modified quantile–quantile plot technique with linear approximation. We experimented with the T2Dv2 semantic labelling benchmark and analysed the results. We showed an increase in the performance of our approach as the size of the table grew. We also discussed how the effects of different parameters diminish as the size of the table increase, and the accuracy with the different parameters converges as the table size gets closer to 200 rows. Finally, we showed that our approach using the quantile–quantile plot outperforms Kolmogorov–Smirnov test. • A normalised linear approximation of the quantile–quantile plot is proposed. • High performance is achieved using the qq-plot for clustering similar columns. • QQ-plot outperforms KS-test in labelling numeric columns. • Adding clustering to qq-plot improves semantic labelling performance. • Semantic labelling of numeric columns in small datasets is still a challenge. [ABSTRACT FROM AUTHOR]

Two recent methods based on distributional semantic models (DSMs) have proved very successful in learning high-quality vector representations of words from large corpora: word2vec and GloVe. Once trained on a very large corpus, these algorithms produce distributed representations for words in the form of vectors. DSMs based on deep learning and neural networks have proved efficient in representing the meaning of individual words. In this paper, I assess to what extent state-of-the-art word-vector semantics can help corpus linguists annotate large datasets for semantic classes. Although word vectors suggest exciting opportunities for resolving semantic annotation issues, there is still room for improvement in terms of the representation of polysemy, homonymy, and multiword expressions. [ABSTRACT FROM AUTHOR]

Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas. [ABSTRACT FROM AUTHOR]

Annotation paths are a technique for the semantic annotation of XML-Schemas. The design rationale was to develop an embedded annotation method on top of SAWSDL which is fully declarative, easily applicable and still provides the proper expressiveness for high-quality logic-based schema matching. Annotation paths capture significantly more semantics than plain model references, the declarative annotation method of the W3C standard SAWSDL. While the concept of annotation paths was introduced in earlier works, we provide a new formalization of their structure and based thereon define their semantics and introduce matching methods to derive simple and complex value correspondences. Such correspondences can be used for the generation of executable schema mappings using state of the art mapping tools. We provide a comprehensive evaluation of our annotation method and the proposed matching algorithms using real-world schemas and reference ontologies and demonstrate the feasibility of generating executable mappings using a state of the art mapping system. Our evaluations show that our annotation-based matcher achieves outstanding matching quality (avg. f-measure between 0.98 and 1.0). [ABSTRACT FROM AUTHOR]

This paper presents the DialogBank, a new language resource consisting of dialogues with gold standard annotations according to the ISO 24617-2 standard. Some of these dialogues have been taken from existing corpora and have been re-annotated, offering the possibility to compare annotations according to different schemes; others have been newly annotated directly according to the standard. The ISO standard annotations in the DialogBank make use of three alternative representation formats, which are shown to be interoperable. The (re-)annotation brought certain deficiencies and limitations of the ISO standard to light, which call for considering possible revisions and extensions, and for exploring the possible integration of dialogue act annotations with other semantic annotations. [ABSTRACT FROM AUTHOR]

The article details the formational process of the FinnTransFrame corpus, a part of the FinnFrameNet project. In addition to a large annotated frame semantic corpus of natural language examples, the project created a separate corpus of examples translated from English to Finnish. The research question when creating the FinnTransFrame corpus was to see to what extent the various frames of the original Berkeley FrameNet transfer into Finnish in translated examples, i.e. what are the main problems and how can they be categorized? A variety of Berkeley FrameNet examples were chosen from different frames and then translated by professionals. The FinnFrameNet annotation team checked all the examples and their translations to see if the frames remained intact in translation. Problematic examples were tagged according to the type of the encountered problem, with the main focus on the type of fine-grained mismatches of meaning that caused frame changes even when the translation was the best possible one. The frame-loss amounted to 4.2% of the 88,209 relevant example sentences. Filtering out sentences with other types of problems, we found that 88.1% of all the frame instances still translated into Finnish with their frame intact. In addition, the article analyzes the error types in the problematic frames. [ABSTRACT FROM AUTHOR]

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation. [ABSTRACT FROM AUTHOR]

The semantic annotation of spatial information aims to identify words or phrases describing geographical references (place names) as well as various associated spatial expressions. One of the major difficulties in designing an automatic annotation system for such information is due to ambiguities related to spatial entities. A modular approach based on Web services was chosen. The proposed methodology is based on the combination of a pre-processing step using external morphosyntactic analysers, a cascade of transducers, and a classification step using linked data. An advantage of this approach is the possibility to obtain partial processing or to evaluate competing methods doing the same task. [ABSTRACT FROM AUTHOR]

Human perception of location and space forms the basis upon which the interaction with location-based services (LBS) takes place. Our work aims to develop a shared awareness and common understanding of location and space,between machines and their users by building upon research into the numerical representation of the visual perception of space. Different structures in buildings like rooms, hallways and doorways form different, corresponding patterns in these representations. Thanks to recent advances in the field of deep learning with neural networks, it now seems possible to explore the idea of automatically learning these recurring structures. This article presents a complete framework: starting from the collection of isovist measures along geospatial trajectories on indoor floor plans,over statistical data analysis, the unsupervised extraction of meaningful structure, up to the training of models that generalize to different environments. We show that isovist measures do reflect the recurring structures found in different buildings, that these recurring patterns are encoded in the data in a way that unsupervised machine learning can identify them andthat the identified structures are meaningful as they represent human relatable concepts.Furthermore, we propose to use cluster similarity analysis as a promising concept for quantifying visual perception similarity. [ABSTRACT FROM AUTHOR]

Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen's k of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset. [ABSTRACT FROM AUTHOR]

Social image annotation, which aims at inferring a set of semantic concepts for a social image, is an effective and straightforward way to facilitate social image search. Conventional approaches mainly demonstrated on adopting the visual features and tags, without considering other types of metadata. How to enhance the accuracy of social image annotation by fully exploiting multi-modal features is still an opening and challenging problem. In this paper, we propose an improved Multi-Modal Data Fusion based Latent Dirichlet Allocation (LDA) topic model (MMDF-LDA) to annotate social images via fusing visual content, user-supplied tags, user comments, and geographic information. When MMDF-LDA samples annotations for one data modality, all the other data modalities are exploited. In MMDF-LDA, geographical topics are generated from GPS locations of social images, and annotations have different probability to be used in different geographical regions. A social image is divided into several patches in advance, and then MMDF-LDA assigns annotations for the patches of social images by estimating the probability of annotation-patch assignment. Through experiments in social image annotation and retrieval on several datasets, we demonstrate the effectiveness of the proposed MMDF-LDA model in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The main problems of traditional machine learning text classification method which based on BOW ( hag of words) are high dimension and high sparseness, can not identify synonyms and lack of semantic information etc. Meanwhile, rule based methods have high precision hut have weaker robustness. In order to solve these problems, this paper proposed a novel method which based on lexical-semantic patterns to extract event semantic annotations from financial news text, and applied these annotations as features in machine learning method. 'Hie experiment shows that this method lifts F, value 8. 6% than BOW, and the precision is increased by 7.1% , recall is increased by 8.8% , which based on same feature selection algorithm and classification method. This method combines the advantages of the two methods of knowledge driven and data driven in text classification, at the same time avoids the major drawbacks of last two methods, it has a good practical and research reference value. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an explosion of geospatial data, especially in the form of Volunteered Geographic Information (VGI). As a prominent example, OpenStreetMap (OSM) creates a free editable map of the world from a large number of contributors. On the other hand, social media platforms such as Twitter or Instagram supply dynamic social feeds at population level. As much of such data is geo-tagged, there is a high potential on integrating social media with OSM to enrich OSM with semantic annotations, which will complement existing objective description oriented annotations to provide a broader range of annotations. In this paper, we propose a comprehensive framework on integrating social media data and VGI data to derive knowledge about geographical objects, specifically, top relevant annotations from tweets for objects in OSM. We first integrate geo-tagged tweets with OSM data with scalable spatial queries running on MapReduce. We propose a frequency based method for annotating boundary based geographic objects (a polygon), and a probability based method for annotating point based geographic objects (Latitude and Longitude), with consideration of noise. We evaluate our methods using a large geo-tagged tweets corpus and representative geographic objects from OSM, which demonstrates promising results through ground-truth comparison and case studies. We are able to produce up to 80% correct names for geographical objects and discover implicitly relevant information, such as popular exhibitions of a museum, the nicknames or visitors’ impression to a tourism attraction. [ABSTRACT FROM AUTHOR]

Advanced digital capturing technologies have led to the explosive growth of images on the Web. To retrieve the desired image from a huge amount of images, textual query is handier to represent the user's interest than providing a visually similar image as a query. Semantic annotation of images' has been identified as an important step towards more efficient manipulation and retrieval of images. The aim of the semantic annotation of images is to annotate the existing images on the Web so that the images are more easily interpreted by searching programs. To annotate the images effectively, extensive image interpretation techniques have been developed to explore the semantic concept of images. But, due to the complexity and variety of backgrounds, effective image annotation is still a very challenging and open problem. Semantic annotation of Web contents manually is not feasible or scalable too, due to the huge amount and rate of emerging Web content. In this paper, we have surveyed the existing image annotation models and developed a hierarchical classification-based image annotation framework for image categorization, description and annotation. Empirical evaluation of the proposed framework with respect to its annotation accuracy shows high precision and recall compared with other annotation models with significant time and cost. An important feature of the proposed framework is that its specific annotation techniques, suitable for a particular image category, can be easily integrated and developed for other image categories. [ABSTRACT FROM AUTHOR]

This paper studies the problem of automatic categorization of Arabic normative provisions. An automatic categorization approach based on a semantic annotation model is proposed. Coupling a taxonomy of Arabic normative provisions’ categories, an Arabic normative terminological base and a rule-based semantic annotator, the proposed semantic annotation model enables the automatic categorization of normative provisions in Arabic legal texts. The robustness and the language independency of the proposed approach are also studied. Robustness is measured with the comparison of the constructed model against Machine Learning (ML) approaches. The language independency level is evaluated through the adaptation of the proposed model to the French language. The performance of the approach is evaluated in terms of Precision, Recall and F-score. The obtained results for the different experiments are very promising. [ABSTRACT FROM AUTHOR]

Background and objectives Semantic text annotation enables the association of semantic information (ontology concepts) to text expressions (terms), which are readable by software agents. In the scientific scenario, this is particularly useful because it reveals a lot of scientific discoveries that are hidden within academic articles. The Biomedical area has more than 300 ontologies, most of them composed of over 500 concepts. These ontologies can be used to annotate scientific papers and thus, facilitate data extraction. However, in the context of a scientific research, a simple keyword-based query using the interface of a digital scientific texts library can return more than a thousand hits. The analysis of such a large set of texts, annotated with such numerous and large ontologies, is not an easy task. Therefore, the main objective of this work is to provide a method that could facilitate this task. Methods This work describes a method called Text and Ontology ETL (TOETL), to build an analytical view over such texts. First, a corpus of selected papers is semantically annotated using distinct ontologies. Then, the annotation data is extracted, organized and aggregated into the dimensional schema of a data mart. Results Besides the TOETL method, this work illustrates its application through the development of the TaP DM (Target Prioritization data mart). This data mart has focus on the research of gene essentiality, a key concept to be considered when searching for genes showing potential as anti-infective drug targets. Conclusions This work reveals that the proposed approach is a relevant tool to support decision making in the prioritization of new drug targets, being more efficient than the keyword-based traditional tools. [ABSTRACT FROM AUTHOR]

Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations. [ABSTRACT FROM AUTHOR]

Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain’s semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform’s content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain. [ABSTRACT FROM AUTHOR]