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Transforming unstructured text into a formal representation is an important goal of the Semantic Web in order to facilitate the integration and retrieval of information. The construction of Knowledge Graphs (KGs) pursues such an idea, where named entities (real world things) and their relations are extracted from text. In recent years, many approaches for the construction of KGs have been proposed by exploiting Discourse Analysis, Semantic Frames, or Machine Learning algorithms with existing Semantic Web data. Although such approaches are useful for processing taxonomies and connecting beliefs, they provide several linguistic descriptions, which lead to semantic data heterogeneity and thus, complicating data consumption. Moreover, Open Information Extraction (OpenIE) approaches have been slightly explored for the construction of KGs, which provide binary relations representing atomic units of information that could simplify the querying and representation of data. In this paper, we propose an approach to generate KGs using binary relations produced by an OpenIE approach. For such purpose, we present strategies for favoring the extraction and linking of named entities with KG individuals, and additionally, their association with grammatical units that lead to producing more coherent facts. We also provide decisions for selecting the extracted information elements for creating potentially useful RDF triples for the KG. Our results demonstrate that the integration of information extraction units with grammatical structures provides a better understanding of proposition-based representations provided by OpenIE for supporting the construction of KGs. [ABSTRACT FROM AUTHOR]

Highlights: [•] An approach for learning concept hierarchies from textual resources is proposed. [•] Subject–verb–object relations are used for identifying nouns. [•] A clustering algorithm and linguistic patterns are applied for identifying concepts. [•] The Web is used for learning hypernym/hyponym relations between concepts. [•] The obtained concept hierarchies can be used for developing ontologies from scratch. [ABSTRACT FROM AUTHOR]

Entity extraction and linking (EEL) is an important task of the Semantic Web that allows to identify real-world objects from text and associate them with their respective resources from a Knowledge Base. Thus, one purpose of the EEL task is to extract knowledge from text. In recent years, several systems have been proposed for addressing such a task in several domains, languages, and knowledge bases. In this sense, some systems that combine the benefits of varied EEL systems have been proposed in a kind of ensemble system (like in Machine Learning) to provide better performance and extractions than using a single system. However, there are no clear indications for the selection, configuration, and result integration of EEL systems in an ensemble setting. This paper proposes a framework for the integration of EEL systems by providing recommendations for the selection of systems, the configuration of input parameters, the execution of systems, and the final integration of results through a filtering strategy that measures the occurrence of entities and detects the overlapping of entities. Based on the proposed framework, we implemented a system using existing EEL systems (through publicly available APIs). The experiments were performed through the GERBIL framework. Our results demonstrate an improvement of the micro/macro- precision and recall of the implemented system regarding the selected individual EEL systems over seven datasets. • Entity extraction and linking systems are integrated by a framework proposal. • The framework is based on an ensemble setting to integrate the output of systems. • The final output of EEL systems is filtered through an entity frequency strategy. • System parameters are tuned through experiments performed over diverse datasets. [ABSTRACT FROM AUTHOR]

This paper presents a novel transversal, agnostic-infrastructure, and generic processing model to build environmental big data services in the cloud. Transversality is used for building processing structures (PS) by reusing/coupling multiple existent software for processing environmental monitoring, climate, and earth observation data, even in execution time, with datasets available in cloud-based repositories. Infrastructure-agnosticism is used for deploying/executing PSs on/in edge, fog, and/or cloud. Genericity is used to embed analytic, merging information, machine learning, and statistic micro-services into PSs for automatically and transparently converting PSs into big data services to support decision-making procedures. A prototype was developed for conducting case studies based on the data climate classification, earth observation products, and making predictions of air data pollution by merging different monitoring climate data sources. The experimental evaluation revealed the efficacy and flexibility of this model to create complex environmental big data services. • A novel transversal processing model to build environmental big data services in clouds. • TCP: coupling points interconnect multiple existent services with data sources. • TEP: extracting points consume data to build bridges with existent data services. • TPS: Processing services embed analytic and machine learning into existent software. • Transversal big data services for climate, earth observation and environment studies. [ABSTRACT FROM AUTHOR]