Semantic Annotation and Classification in Practice

Rocha, Oscar Rodríguez; Vagliano, Iacopo; Figueroa, Cristhian; Cairo, Federico; Futia, Giuseppe; Licciardi, Carlo Alberto; Marengo, Marco; Morando, Federico

doi:10.1109/mitp.2015.29

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…However, this research is also focused on querying historical data. The author of [32] employed linked data to improve telecommunication operation by enriching the textual documents. ReDy Artificial Intelligent (AI) method is used to engage mobile service with the web of data.…”

Section: Semantic Annotation Techniquesmentioning

confidence: 99%

SiSEG-Auto Semantic Annotation Service to Integrate Smart Energy Data

2022

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In a modern smart energy system, the amount of available data from various sources is growing significantly. Other sectors such as medical or social sectors exhibit the same phenomenon. Due to the amount, complexity and heterogeneity of data, a complex algorithm is required for the integration and analysis of heterogeneous data sources. The Web of Things and semantic-based approaches address the fragmentation of standards, platforms, services and technologies in smart energy and non-energy sectors, and enable heterogeneous data integration and interoperability. This paper presents SiSEG, a semantic annotation service that is developed to automate the process of annotating data and address the problem of heterogeneous data integration in a reusable and extensible way by using the fuzzy method. Moreover, the accuracy of SiGEG has been evaluated.

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Section: Semantic Annotation Techniquesmentioning

confidence: 99%

SiSEG-Auto Semantic Annotation Service to Integrate Smart Energy Data

2022

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“…However, approaches based on pure ontology [14] and NLP [15,16] have been shown to be effective, particularly in the scholarly domain. We decided to employ a pure NLP solution called TellMeFirst (TMF) based on our previous work [17]. Compared to the work of Salatino et al [14], which is focused on a specific domain, this solution allowed us to employ a general-purpose reference (DBpedia) that can be adapted to the variety of topics covered in the publications of Polito.…”

Section: Related Workmentioning

confidence: 99%

“…At the conclusion of this extraction and mapping process, the resulting RDF is augmented by TellMeFirst (TMF) [17]. TMF is used to semantically classify the abstracts of the publications by extracting the main topics from texts as DBpedia entities.…”

Section: Data Ingestionmentioning

confidence: 99%

The Geranium Platform: A KG-Based System for Academic Publications

Garifo

Futia²,

Vetrò

et al. 2021

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Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which is sharing their intellectual output, especially publications. In this paper, we describe and make available the Polito Knowledge Graph (PKG) –which semantically connects information on more than 23,000 publications and 34,000 authors– and Geranium, a semantic platform that leverages the properties of the PKG to offer advanced services for search and exploration. In particular, we describe the Geranium recommendation system, which exploits Graph Neural Networks (GNNs) to suggest collaboration opportunities between researchers of different disciplines. This work integrates the state of the art because we use data from a real application in the scholarly domain, while the current literature still explores the combination of KGs and GNNs in a prototypal context using synthetic data. The results shows that the fusion of these technologies represents a promising approach for recommendation and metadata inference in the scholarly domain.

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“…Another interesting system for document classification is introduced by Rocha et al in [28]. It uses semantic information contained in the linked data to improve the document classification score.…”

Section: Related Workmentioning

confidence: 99%

SAPKOS: Experimental Czech Multi-label Document Classification and Analysis System

Lenc

Král

2015

IFIP Advances in Information and Communication Technology

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This paper presents an experimental multi-label document classification and analysis system called SAPKOS. The system which integrates the state-of-the-art machine learning and natural language processing approaches is intended to be used by the Czech news Agency (ČTK). Its main purpose is to save human resources in the task of annotation of newspaper articles with topics. Another important functionality is automatic comparison of theČTK production with popular Czech media. The results of this analysis will be used to adapt theČTK production to better correspond to the today's market requirements. An interesting contribution is that, to the best of our knowledge, no other automatic Czech document classification system exists. It is also worth mentioning that the system accuracy is very high. This score is obtained due to the unique system architecture which integrates a maximum entropy based classification engine with the novel confidence measure method.

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Semantic Annotation and Classification in Practice

Cited by 6 publications

References 14 publications

SiSEG-Auto Semantic Annotation Service to Integrate Smart Energy Data

SiSEG-Auto Semantic Annotation Service to Integrate Smart Energy Data

The Geranium Platform: A KG-Based System for Academic Publications

SAPKOS: Experimental Czech Multi-label Document Classification and Analysis System

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