Semantically-enhanced information retrieval using multiple knowledge sources

Jiang, Yuncheng

doi:10.1007/s10586-020-03057-7

Cited by 15 publications

(3 citation statements)

References 63 publications

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“…The visual matching feature is similar to Ou et al 19 but uses the LSI method for obtaining a high retrieval accuracy of more than 74%, which is 18% higher than the work in Ou et al 19 Ontology-based techniques are replaced with deep learning-based retrieval techniques in order to improve their efficiency. For instance, the work in Jiang 29 proposes using multiple knowledge sources and then combining them using deep learning models for effective document representation. The system generates a deep linking graph, similar to the one showcased in Jiang, 29 wherein multiple entities are linked with multiple other entities with the help of data obtained from multidomain sources.…”

Section: Effective Document Retrieval Techniquesmentioning

confidence: 99%

“…For instance, the work in Jiang 29 proposes using multiple knowledge sources and then combining them using deep learning models for effective document representation. The system generates a deep linking graph, similar to the one showcased in Jiang, 29 wherein multiple entities are linked with multiple other entities with the help of data obtained from multidomain sources. Once the graph is generated, a weighted graph is created with a weighted dynamic semantic network for giving weights to each node in the network.…”

Section: Literature Reviewmentioning

confidence: 99%

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A novel semantic representation approach for web documents using deep entity linking and multidocument support

Urkude

Pandey

2022

Int J Communication

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Summary Representing semantic web information in the form of existing formats like extensible mark‐up language (XML), resource description framework (RDF) or web ontology language (OWL) is limited by the inability to maintain intradocument relationships between different semantic entities. Moreover, these standards are built for generic data representation and thus do not have inherent support for the semantic description of the document and, consequently, do not support features like multidocument linkages and context‐sensitivity. In order to remove these drawbacks, a semantic web‐specific document representation approach, namely, deep semantic XML (DS‐XML) using deep linking and machine learning, is presented in this paper. The DS‐XML approach facilitates the use of semantics with the help of a novel document structure that focuses on defining any web document in a semantic form. It includes ontologies and linkage between different vocabularies, which assists in achieving semantic interoperability. In order to make this approach future proof, a novel deep linking approach is integrated at the document level, which allows DS‐XML to understand more about the contents of the document and represent information in an aggregated manner. The deep linking layer is combined with the machine learning‐based classification layer that allows the document information to be represented in an interactive manner. The proposed DS‐XML approach is tested on different document retrieval applications, including a few Internet of Things (IoT) applications, and the representation performance is found to be superior to the existing semantic approach (XML, RDF and OWL).

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Section: Effective Document Retrieval Techniquesmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A novel semantic representation approach for web documents using deep entity linking and multidocument support

Urkude

Pandey

2022

Int J Communication

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“…Unlike keyword search, semantic search improves search precision and recall by understanding the user's intent and the contextual meaning of concepts in documents and queries [3,12,19,24]. This paper proposes a semantic search engine for full-text retrieval of historical handwritten document images based on named entity (NE), keyword (KW) and knowledge graph (KG).…”

Section: Introductionmentioning

confidence: 99%

A Semantic Search Engine for Historical Handwritten Document Images

Ngo

Munnelly

Orlandi

et al. 2021

Linking Theory and Practice of Digital Libraries

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A very large number of historical manuscript collections are available in image formats and require extensive manual processing in order to search through them. So, we propose and build a search engine for automatically storing, indexing and efficiently searching the manuscript images. Firstly, a handwritten text recognition technique is used to convert the images into textual representations. In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user’s intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.

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