SVG-to-RDF Image Semantization

Salameh, Khouloud; Tekli, Joe; Chbeir, Richard

doi:10.1007/978-3-319-11988-5_20

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…6). Nonetheless, template querying could be particularly useful in searchby-document and search-by-image systems for instance, where the query could be a whole document or an SVG image [62] the user is searching for in the XML repository. A template style query could be any of the sub-trees in the XML tree of Fig.…”

Section: Structure-and-content Query Modelmentioning

confidence: 99%

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Tekli

Chbeir

2023

ComSIS

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Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.

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Section: Structure-and-content Query Modelmentioning

confidence: 99%

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Tekli

Chbeir

2023

ComSIS

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“…In [20], a framework to realise domain-based Resource Description Framework (RDF) annotation of SVG images is presented. In this framework, the SVG image is initially processed to extract relevant graphical features, which is used to create a layer of RDF data.…”

Section: Related Workmentioning

confidence: 99%

“…The semantic annotation generated by the application is also checked by a domain expert, which is fed back into the RDF domain database. Comparing the prototype discussed in this paper and the annotation framework presented in [20], it is evident that both approaches use declarative domain knowledge to assist with the interpretation of SVG images. However, the approach in this work is focussed on spatial patterns in diagrams, and how an ontology of such patterns can be used for diagram interpretation.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Spatial Pattern Recognition in Diagrams

Thomas¹,

Gerber²,

Merwe³

2018

IFIP Advances in Information and Communication Technology

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Diagrams are widely used in our day to day communication. A knowledge of the spatial patterns used in diagrams is essential to read and understand them. In the context of diagrams, spatial patterns mean accepted spatial arrangements of graphical and textual elements used to represent diagram-specific concepts. In order to assist with the automated understanding of diagrams by computer applications, this paper presents an ontology-based approach to recognise diagram-specific concepts from the spatial patterns in diagrams. Specifically, relevant spatial patterns of diagrams are encoded in an ontology, and the automated instance classification feature of the ontology reasoners is utilised to map spatial patterns to diagram-specific concepts depicted in a diagram. A prototype of this approach to support automated recognition of UML and domain concepts from class diagrams and its performance are also discussed in this paper. This paper concludes with a reflection of the strengths and limitations of the proposed approach.

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Low-Light Image Enhancement Using Image-to-Frequency Filter Learning

Sobbahi

Tekli

2022

Lecture Notes in Computer Science

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SVG-to-RDF Image Semantization

Cited by 11 publications

References 21 publications

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Ontology-Based Spatial Pattern Recognition in Diagrams

Low-Light Image Enhancement Using Image-to-Frequency Filter Learning

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