Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

Liu, Xiulei; Tong, Qiang; Liu, Xuhong; Qin, Zhihui

doi:10.1109/access.2021.3057081

Cited by 12 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dataset contained 127, 546 distinct words after preprocessing, presenting a challenge due to its substantial size and potential computational complexity in subsequent analyses. In this study, the WordNet ontology [49], [50] technique was employed to hierarchically organize words based on their semantic relationships and contextual meanings. This method categorized words into clusters or groups according to their similarities in meaning or usage, effectively consolidating redundant or closely related terms.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Enhancing Hate Speech Detection in the Digital Age: A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

Sharif,

Abdullah,

Iftikhar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the era of digital communication, social media platforms have experienced exponential growth, becoming primary channels for information exchange. However, this surge has also amplified the rapid spread of hate speech, prompting extensive research efforts for effective mitigation. These efforts have prominently featured advanced natural language processing techniques, particularly emphasizing deep learning methods that have shown promising outcomes. This article presents a novel approach to address this pressing issue, combining a comprehensive dataset of 18 sources. It includes 0.45 million comments sourced from various digital platforms spanning different time frames. There were two models utilized to address the diversity in the data and leverage distinct strengths found within deep learning frameworks: CNN and BiLSTM with an attention mechanism. These models were tailored to handle specific subsets of the data, allowing for a more targeted approach. The unique outputs from both models were then fused into a unified model. This methodology outperformed recent models, showcasing enhanced generalization capabilities even when tested on the largest and most diverse dataset. Our model achieved an impressive accuracy of 89%, while maintaining a high precision of 0.88 and recall of 0.91.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

Enhancing Hate Speech Detection in the Digital Age: A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

Sharif,

Abdullah,

Iftikhar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…for graph querying (De Virgilio et al, 2013), where one measures the similarity between a small query graph and parts of the a larger RDF graph (representing the dataset on which the query is executed) by looking for the portions of the larger graph most similar to the query. Similar metrics are used in the field of ontology matching (Shvaiko and Euzenat, 2013;Liu et al, 2021), where the aim is to handle the problem of semantic heterogeneity, by finding relations between entities from distinct ontologies that share a similar description. Another possibility to assess the similarity of semantic graph nodes is to use graph embeddings (Trisedya et al, 2019) that map graph nodes into points in a high dimensional metric space, and measure node similarity through the distance between the corresponding points.…”

Section: Semantic Aspectmentioning

confidence: 99%

Automatic Structuring of Photographic Collections for Spatio-Temporal Monitoring of Restoration Sites: Problem Statement and Challenges

Willot

Vodislav

Luca

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Over the last decade, a large number of digital documentation projects have demonstrated the potential of image-based modelling of heritage objects in the context of documentation, conservation, and restoration. The inclusion of these emerging methods in the daily monitoring of the activities of a heritage restoration site (context in which hundreds of photographs per day can be acquired by multiple actors, in accordance with several observation and analysis needs) raises new questions at the intersection of big data management, analysis, semantic enrichment, and more generally automatic structuring of this data. In this article we propose a data model developed around these questions and identify the main challenges to overcome the problem of structuring massive collections of photographs through a review of the available literature on similarity metrics used to organise the pictures based on their content or metadata. This work is realized in the context of the restoration site of the Notre-Dame de Paris cathedral that will be used as the main case study.

show abstract

“…This is where ontology alignment, also known as ontology matching, plays a crucial role. However, the challenges inherent in the reuse process are also reflected in ontology alignment [13][14][15][16], given that ontologies are developed independently by different knowledge engineers, each with their unique perspectives. Therefore, a holistic approach must be taken when aligning ontologies, accounting for variations in vocabulary, concept semantics, and structures.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

Rudwan

Fonou-Dombeu

2023

Future Internet

View full text Add to dashboard Cite

Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching algorithms and the Deep Bidirectional Transformer (BERT) deep learning model with three machine learning regression classifiers, namely, K-Nearest Neighbor Regression (kNN), Decision Tree Regression (DTR), and Support Vector Regression (SVR), to perform the alignment of ontologies. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three similarity models (SM), encoded SM-kNN, SM-SVR, and SM-DTR, respectively. The experiments were conducted on a dataset obtained from the anatomy track in the Ontology Alignment and Evaluation Initiative 2022 (OAEI 2022). The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97, and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 challenge, indicating the superior capability of the proposed method.

show abstract

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

Cited by 12 publications

References 41 publications

Enhancing Hate Speech Detection in the Digital Age: A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

Enhancing Hate Speech Detection in the Digital Age: A Novel Model Fusion Approach Leveraging a Comprehensive Dataset

Automatic Structuring of Photographic Collections for Spatio-Temporal Monitoring of Restoration Sites: Problem Statement and Challenges

Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

Contact Info

Product

Resources

About