Personalized and context-aware retrieval based on fuzzy ontology profiling

Besbes, Ghada; Baazaoui-Zghal, Hajer

doi:10.3233/ica-160525

Cited by 10 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any clustering method that possesses these properties may also serve as candidates. Other contextaware similarity measures such as the one described in [40] may be explored. The topic diversity function F d can be represented using such a semantic subtopic clustering algorithms.…”

Section: Semantic Subtopic Clusteringmentioning

confidence: 99%

An unsupervised semantic sentence ranking scheme for text documents

Zhang¹,

Wang²

2020

ICA

View full text Add to dashboard Cite

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge's ranking and compares favorably with the combined ranking of all judges.

show abstract

Section: Semantic Subtopic Clusteringmentioning

confidence: 99%

An unsupervised semantic sentence ranking scheme for text documents

Zhang¹,

Wang²

2020

ICA

View full text Add to dashboard Cite

show abstract

“…It also provides the image semantic by constructing visual words using the probabilistic latent semantic. Other researches [45]- [47] also proposed formalize and semantic visualization models based on the fuzzy rule set.…”

Section: Related Workmentioning

confidence: 99%

Random Forest Based Searching Approach for RDF

Soliman

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…It also provides the image semantic by constructing visual words using the probabilistic latent semantic. Other researches [50][51][52] also proposed formalize and semantic visualization models based on the fuzzy rule set.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based searching approach for RDF graphs

Soliman¹

2020

PLoS ONE

View full text Add to dashboard Cite

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.

show abstract

Personalized and context-aware retrieval based on fuzzy ontology profiling

Cited by 10 publications

References 44 publications

An unsupervised semantic sentence ranking scheme for text documents

An unsupervised semantic sentence ranking scheme for text documents

Random Forest Based Searching Approach for RDF

Deep learning based searching approach for RDF graphs

Contact Info

Product

Resources

About