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Academic literature on the topic 'RDF-To-Text'

Author: Grafiati
Published: 4 May 2024

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Journal articles on the topic "RDF-To-Text"

1

Chellali, Mustapha, and Nader Jafari Rad. "Trees with independent Roman domination number twice the independent domination number." Discrete Mathematics, Algorithms and Applications 07, no. 04 (2015): 1550048. http://dx.doi.org/10.1142/s1793830915500482.

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A Roman dominating function (RDF) on a graph [Formula: see text] is a function [Formula: see text] satisfying the condition that every vertex [Formula: see text] for which [Formula: see text] is adjacent to at least one vertex [Formula: see text] for which [Formula: see text]. The weight of a RDF [Formula: see text] is the value [Formula: see text]. The Roman domination number, [Formula: see text], of [Formula: see text] is the minimum weight of a RDF on [Formula: see text]. An RDF [Formula: see text] is called an independent Roman dominating function (IRDF) if the set [Formula: see text] is an independent set. The independent Roman domination number, [Formula: see text], is the minimum weight of an IRDF on [Formula: see text]. In this paper, we study trees with independent Roman domination number twice their independent domination number, answering an open question.
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2

Gryaznov, Yevgeny, and Pavel Rusakov. "Analysis of RDF Syntaxes for Semantic Web Development." Applied Computer Systems 18, no. 1 (2015): 33–42. http://dx.doi.org/10.1515/acss-2015-0017.

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Abstract In this paper authors perform a research on possibilities of RDF (Resource Description Framework) syntaxes usage for information representation in Semantic Web. It is described why pure XML cannot be effectively used for this purpose, and how RDF framework solves this problem. Information is being represented in a form of a directed graph. RDF is only an abstract formal model for information representation and side tools are required in order to write down that information. Such tools are RDF syntaxes – concrete text or binary formats, which prescribe rules for RDF data serialization. Text-based RDF syntaxes can be developed on the existing format basis (XML, JSON) or can be an RDF-specific – designed from scratch to serve the only purpose – to serialize RDF graphs. Authors briefly describe some of the RDF syntaxes (both XML and non-XML) and compare them in order to identify strengths and weaknesses of each version. Serialization and deserialization speed tests using Jena library are made. The results from both analytical and experimental parts of this research are used to develop the recommendations for RDF syntaxes usage and to design a RDF/XML syntax subset, which is intended to simplify the development and raise compatibility of information serialized with this RDF syntax.
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Meddah, Nacéra, and Mustapha Chellali. "Roman domination and 2-independence in trees." Discrete Mathematics, Algorithms and Applications 09, no. 02 (2017): 1750023. http://dx.doi.org/10.1142/s1793830917500239.

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A Roman dominating function (RDF) on a graph [Formula: see text] is a function [Formula: see text] satisfying the condition that every vertex [Formula: see text] with [Formula: see text] is adjacent to at least one vertex [Formula: see text] of [Formula: see text] for which [Formula: see text]. The weight of a RDF is the sum [Formula: see text], and the minimum weight of a RDF [Formula: see text] is the Roman domination number [Formula: see text]. A subset [Formula: see text] of [Formula: see text] is a [Formula: see text]-independent set of [Formula: see text] if every vertex of [Formula: see text] has at most one neighbor in [Formula: see text] The maximum cardinality of a [Formula: see text]-independent set of [Formula: see text] is the [Formula: see text]-independence number [Formula: see text] Both parameters are incomparable in general, however, we show that if [Formula: see text] is a tree, then [Formula: see text]. Moreover, all extremal trees attaining equality are characterized.
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4

Samodivkin, Vladimir. "Roman domination in graphs: The class ℛUV R". Discrete Mathematics, Algorithms and Applications 08, № 03 (2016): 1650049. http://dx.doi.org/10.1142/s179383091650049x.

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For a graph [Formula: see text], a Roman dominating function (RDF) [Formula: see text] has the property that every vertex [Formula: see text] with [Formula: see text] has a neighbor [Formula: see text] with [Formula: see text]. The weight of a RDF [Formula: see text] is the sum [Formula: see text], and the minimum weight of a RDF on [Formula: see text] is the Roman domination number [Formula: see text] of [Formula: see text]. The Roman bondage number [Formula: see text] of [Formula: see text] is the minimum cardinality of all sets [Formula: see text] for which [Formula: see text]. A graph [Formula: see text] is in the class [Formula: see text] if the Roman domination number remains unchanged when a vertex is deleted. In this paper, we obtain tight upper bounds for [Formula: see text] and [Formula: see text] provided a graph [Formula: see text] is in [Formula: see text]. We present necessary and sufficient conditions for a tree to be in the class [Formula: see text]. We give a constructive characterization of [Formula: see text]-trees using labelings.
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5

Cui, Hong, Kenneth Yang Jiang, and Partha Pratim Sanyal. "From text to RDF triple store: An application for biodiversity literature." Proceedings of the American Society for Information Science and Technology 47, no. 1 (2010): 1–2. http://dx.doi.org/10.1002/meet.14504701415.

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6

Khoeilar, R., and S. M. Sheikholeslami. "Rainbow reinforcement numbers in digraphs." Asian-European Journal of Mathematics 10, no. 01 (2017): 1750004. http://dx.doi.org/10.1142/s1793557117500048.

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Let [Formula: see text] be a finite and simple digraph. A [Formula: see text]-rainbow dominating function ([Formula: see text]RDF) of a digraph [Formula: see text] is a function [Formula: see text] from the vertex set [Formula: see text] to the set of all subsets of the set [Formula: see text] such that for any vertex [Formula: see text] with [Formula: see text] the condition [Formula: see text] is fulfilled, where [Formula: see text] is the set of in-neighbors of [Formula: see text]. The weight of a [Formula: see text]RDF [Formula: see text] is the value [Formula: see text]. The [Formula: see text]-rainbow domination number of a digraph [Formula: see text], denoted by [Formula: see text], is the minimum weight of a [Formula: see text]RDF of [Formula: see text]. The [Formula: see text]-rainbow reinforcement number [Formula: see text] of a digraph [Formula: see text] is the minimum number of arcs that must be added to [Formula: see text] in order to decrease the [Formula: see text]-rainbow domination number. In this paper, we initiate the study of [Formula: see text]-rainbow reinforcement number in digraphs and we present some sharp bounds for [Formula: see text]. In particular, we determine the [Formula: see text]-rainbow reinforcement number of some classes of digraphs.
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7

Dosso, Dennis, and Gianmaria Silvello. "Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System." IEEE Access 8 (2020): 14089–111. http://dx.doi.org/10.1109/access.2020.2966823.

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8

Dong, Ngan T., and Lawrence B. Holder. "Natural Language Generation from Graphs." International Journal of Semantic Computing 08, no. 03 (2014): 335–84. http://dx.doi.org/10.1142/s1793351x14500068.

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The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.
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9

Devi, Runumi, Deepti Mehrotra, and Hajer Baazaoui-Zghal. "RDF Model Generation for Unstructured Dengue Patients' Clinical and Pathological Data." International Journal of Information System Modeling and Design 10, no. 4 (2019): 71–89. http://dx.doi.org/10.4018/ijismd.2019100104.

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The automatic extraction of triplets from unstructured patient records and transforming them into resource description framework (RDF) models has remained a huge challenge so far, and would provide significant benefit to potential applications like knowledge discovery, machine interoperability, and ontology design in the health care domain. This article describes an approach that extracts semantics (triplets) from dengue patient case-sheets and clinical reports and transforms them into an RDF model. A Text2Ontology framework is used for extracting relations from text and was found to have limited capability. The TypedDependency parsing-based algorithm is designed for extracting RDF facts from patients' case-sheets and subsequent conversion into RDF models. A mapping-driven semantifying approach is also designed for mapping clinical details extracted from patients' reports to its corresponding triplet components and subsequent RDF model generations. The exhaustiveness of the RDF models generated are measured based on the number of axioms generated with respect to the facts available.
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10

Mountantonakis, Michalis, and Yannis Tzitzikas. "Linking Entities from Text to Hundreds of RDF Datasets for Enabling Large Scale Entity Enrichment." Knowledge 2, no. 1 (2021): 1–25. http://dx.doi.org/10.3390/knowledge2010001.

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There is a high increase in approaches that receive as input a text and perform named entity recognition (or extraction) for linking the recognized entities of the given text to RDF Knowledge Bases (or datasets). In this way, it is feasible to retrieve more information for these entities, which can be of primary importance for several tasks, e.g., for facilitating manual annotation, hyperlink creation, content enrichment, for improving data veracity and others. However, current approaches link the extracted entities to one or few knowledge bases, therefore, it is not feasible to retrieve the URIs and facts of each recognized entity from multiple datasets and to discover the most relevant datasets for one or more extracted entities. For enabling this functionality, we introduce a research prototype, called LODsyndesisIE, which exploits three widely used Named Entity Recognition and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for recognizing the entities of a given text. Afterwards, it links these entities to the LODsyndesis knowledge base, which offers data enrichment and discovery services for millions of entities over hundreds of RDF datasets. We introduce all the steps of LODsyndesisIE, and we provide information on how to exploit its services through its online application and its REST API. Concerning the evaluation, we use three evaluation collections of texts: (i) for comparing the effectiveness of combining different Named Entity Recognition tools, (ii) for measuring the gain in terms of enrichment by linking the extracted entities to LODsyndesis instead of using a single or a few RDF datasets and (iii) for evaluating the efficiency of LODsyndesisIE.
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