Linked Open Data: Uncertainty in Equivalence of Properties

To, Nhuan D.; Reformat, Marek; Yager, Ronald R.

doi:10.1007/978-3-319-66827-7_38

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Previously, we have introduced a simple approach to determine degrees of equivalences between properties defined by different vocabularies [20]. In this paper, we propose its modified version EQ(p, p 0 ) = a ⇤ labelSim(p, p…”

Section: Property Equivalencementioning

confidence: 99%

Data Fusion in Question Answering Systems over Multiple-Knowledge Bases

To¹,

Reformat²

2021

Atlantis Studies in Uncertainty Modelling

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The additional ability to span over multiple Knowledge Bases would further increase the usefulness and potentially comprehensiveness of the system's responses. Multiple different Knowledge Bases, as much as they can complement each other regarding the lack of specific information, quite often contain inconsistent pieces of information. This makes data fusing a difficult task.In this paper, we propose a methodology for fusing data retrieved from multiple different Knowledge Bases that use different naming schemas. It also contains a procedure for determining degrees of trustworthiness in the Bases. These degrees are included in the data fusion process.

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Section: Property Equivalencementioning

confidence: 99%

Data Fusion in Question Answering Systems over Multiple-Knowledge Bases

To¹,

Reformat²

2021

Atlantis Studies in Uncertainty Modelling

Self Cite

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“…Nhuan et al [48] proposed an approach that computes the degrees of equality among relations (properties) defined by different repositories. They consider the frequencies of matching pairs of RDF triples to find the intervals representing upper and lower levels of property equality.…”

Section: Related Workmentioning

confidence: 99%

Random Forest Based Searching Approach for RDF

Soliman

2020

IEEE Access

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The blend of digital and physical worlds changed the Internet significantly. Accordingly, trends to collect, access, and deliver information have changed over the Web. Such changes raised the problems of information retrieval. Search engines retrieve requested information based on the provided keywords which is not an efficient way for rich information retrieval. Consequently, the fetching of the required information is difficult without understanding the syntax and semantics of the content. The multiple existing approaches to resolve this problem by exploiting linked data and semantic Web techniques. Such approaches serialize the content leveraging the Resource Description Framework (RDF) and process the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although it improves the keyword-based search, it does not provide probabilistic reasoning to find the relationship accuracy between the query and results. In this perspective, this paper proposes a machine learning (random forest) based approach to predict the fetching status of RDF by treating RDFs' requests as a classification problem. First, we preprocess the RDF to convert them into N-Triples format. Then, a feature vector is constructed for each RDF using the preprocessed RDF. After that, a random forest classifier is trained for the prediction of the fetching status of RDFs. The proposed approach is evaluated on an open-source DBpedia dataset. The 10-fold cross-validation results indicate that the performance of the proposed approach is accurate and surpasses the state-of-the-art.

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“…Nhuan et al [53] proposed an approach that determines the degrees of equality between relations (properties) defined by different vocabularies. They consider the occurrences of matching pairs of RDF triples to find the intervals representing lower and upper levels of property equality.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based searching approach for RDF graphs

Soliman¹

2020

PLoS ONE

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The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.

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Linked Open Data: Uncertainty in Equivalence of Properties

Cited by 4 publications

References 9 publications

Data Fusion in Question Answering Systems over Multiple-Knowledge Bases

Data Fusion in Question Answering Systems over Multiple-Knowledge Bases

Random Forest Based Searching Approach for RDF

Deep learning based searching approach for RDF graphs

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