Alignment and dataset identification of linked data in Semantic Web

Gunaratna, Kalpa; Lalithsena, Sarasi; Sheth, Amit P.

doi:10.1002/widm.1121

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…Clearly, the summaries generated by REMES are more related according to both the measures and they further confirm the achievement of our objective of creating relatedness based entity summaries for entity collections. Further, we intend to investigate more on what properties to select and similarities and alignments among the properties and entities [Gunaratna et al ., 2013; 2014] in creating high quality summaries.…”

Section: Evaluation and Results Discussionmentioning

confidence: 99%

Relatedness-based Multi-Entity Summarization

Gunaratna

Yazdavar

Thirunarayan

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.

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Section: Evaluation and Results Discussionmentioning

confidence: 99%

Relatedness-based Multi-Entity Summarization

Gunaratna

Yazdavar

Thirunarayan

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

Self Cite

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“…Ontology alignment is the process of finding correspondences [26] between concepts, properties, or instances in two or more ontologies, based on their similarities [27].…”

Section: Using Ontologies In Data Interlinking Alignmentmentioning

confidence: 99%

LinkD: element-based data interlinking of RDF datasets in linked data

Kettouch

Luca

2022

Computing

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One of the main obstacles in publishing in a Linked Data way is to connect the dataset being published externally with related data sources in the cloud, known as Data Interlinking. This paper proposes LinkD, a new element-based interlinking approach. LinkD interlinks an RDF dataset, resulted from transformed semi-structured data, with its counterparts in the web of Linked Data. To provide similarity links, the existence of published data in the Linked Data cloud is done in the first place. Different algorithms for similarity measurement are employed while the domain of the dataset being interlinked is taken into account. The techniques utilised allow the processing of a large number of Linked Data datasets. The evaluation of LinkD shows high precision, recall and performance.

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“…In the Semantic Web domain, Genetic Algorithm-based approaches can be observed in a variety of applications (Chen, Wu, & Cudré-Mauroux, 2012) such as finding optimal ontology alignments between multiple ontologies (J. J. Martinez-Gil & J.F. Aldana-Montes, 2008); identification and alignment of datasets of the Linked Open Data cloud (Gunaratna, Lalithsena, & Sheth, 2014); RDF query answering (Oren, Guéret, & Schlobach, 2008); and semantic Web service discovery (Sangers, Frasincar, Hogenboom, & Chepegin, 2013) and composition (Fanjiang & Syu, 2014), among others, and have shown to be effective for such optimization problems.…”

Section: Defaultmentioning

confidence: 99%

Evolutionary fine-tuning of automated semantic annotation systems

Cuzzola¹,

Jovanović²,

Bagheri³

et al. 2015

Expert Systems with Applications

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a b s t r a c tConsidering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.

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Alignment and dataset identification of linked data in Semantic Web

Cited by 7 publications

References 54 publications

Relatedness-based Multi-Entity Summarization

Relatedness-based Multi-Entity Summarization

LinkD: element-based data interlinking of RDF datasets in linked data

Evolutionary fine-tuning of automated semantic annotation systems

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