Abstract. Entity resolution, which seeks to identify records that represent the same entity, is an important step in many data integration and data cleaning applications. However, entity resolution is challenging both in terms of scalability (all-against-all comparisons are computationally impractical) and result quality (syntactic evidence on record equivalence is often equivocal). As a result, end-to-end entity resolution proposals involve several stages, including blocking to efficiently identify candidate duplicates, detailed comparison to refine the conclusions from blocking, and clustering to identify the sets of records that may represent the same entity. However, the quality of the result is often crucially dependent on configuration parameters in all of these stages, for which it may be difficult for a human expert to provide suitable values. This paper describes an approach in which a complete entity resolution process is optimized, on the basis of feedback (such as might be obtained from crowds) on candidate duplicates. Given such feedback, an evolutionary search of the space of configuration parameters is carried out, with a view to maximizing the fitness of the resulting clusters. The approach is payas-you-go in that more feedback can be expected to give rise to better outcomes. An empirical evaluation shows that the co-optimization of the different stages in entity resolution can yield significant improvements over default parameters, even with small amounts of feedback.
In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.
<span>Smart cities are covering the population that are seeking the best lifestyle and fulfilling their needs. Through smart cities, necessary modern facilities using ICT emerging technologies such as the internet of things (IoT) had been installed to ensure the sustainability of the city. In the perspective of waste management, several different IoT-based solutions also had been proposed as an alternative to monitor and to ensure the health of communities. This paper reviews existing IoT-based solutions in smart cites’ waste level management system to bring together the state-of-the-art. We performed reviews on 16 research articles from the past 5 years in the literature to provide a comprehensive review of different works on IoT-based solutions related to the smart waste level monitoring system, possible solutions and technologies used. The results obtained shows that existing solutions were similar in the platform used to integrate with the IoT technologies but had some differences in term of the used of sensors and communication technologies. The study also shows that many of the prior studies used Arduino Uno. Results from this study will assist the researcher, focusing on expanding further the used of different technologies or improved the existing system.</span>
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