Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System

Dosso, Dennis; Silvello, Gianmaria

doi:10.1109/access.2020.2966823

Cited by 13 publications

(24 citation statements)

References 41 publications

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“…LOTUS returns triples by providing the full URIs of the resources, while [8] returns triples and/or entities using an API. In general, most works (including [30,31]) do not pay attention to the presentation of results; they focus on the ranking of entities/subgraphs that they compute.…”

Section: Visualization Of Rdf Search Resultsmentioning

confidence: 99%

Keyword Search over RDF: Is a Single Perspective Enough?

Nikas

Kadilierakis

Fafalios

et al. 2020

BDCC

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Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach.

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Section: Visualization Of Rdf Search Resultsmentioning

confidence: 99%

Keyword Search over RDF: Is a Single Perspective Enough?

Nikas

Kadilierakis

Fafalios

et al. 2020

BDCC

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“…The complexity of search systems for RDF and their scalability issues have prevented the use of keyword search for RDF data in general and nanopublications in particular. NanoWeb, exploits a very recent advancement in virtual-document based systems (Dosso & Silvello, 2020), which enable fast and effective keyword search over RDF and nanopublications.…”

Section: Search Over Rdfmentioning

confidence: 99%

“…To allow users to exploit natural language to search for nanopublications, we realized a keyword search system over RDF data. The system we adopt is based on the virtual document strategy, first presented in Lopez-Veyna, Sosa & López-Arévalo (2012) and used in many other papers about keyword search on RDF graphs (Dosso & Silvello, 2020;Elbassuoni & Blanco, 2011;Mass & Sagiv, 2016). The underlying task of these papers is that, given an RDF graph, the user wants to query it, but for some reason, she is unable to use a SPARQL query.…”

Section: Search Systemmentioning

confidence: 99%

“…Since each graph is flattened to a document version of itself, information such as its topology and the disposition of words among nodes and edges is lost. This is certainly true, and in fact works such as (Dosso & Silvello, 2020;Elbassuoni & Blanco, 2011;Mass & Sagiv, 2016) do not limit themselves to virtual documents, but employ different kinds of heuristics to better leverage on the topology of the graphs.…”

Section: Search Systemmentioning

confidence: 99%

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Search, access, and explore life science nanopublications on the Web

Giachelle¹,

Dosso²,

Silvello³

2021

PeerJ Computer Science

Self Cite

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Nanopublications are Resource Description Framework (RDF) graphs encoding scientific facts extracted from the literature and enriched with provenance and attribution information. There are millions of nanopublications currently available on the Web, especially in the life science domain. Nanopublications are thought to facilitate the discovery, exploration, and re-use of scientific facts. Nevertheless, they are still not widely used by scientists outside specific circles; they are hard to find and rarely cited. We believe this is due to the lack of services to seek, find and understand nanopublications’ content. To this end, we present the NanoWeb application to seamlessly search, access, explore, and re-use the nanopublications publicly available on the Web. For the time being, NanoWeb focuses on the life science domain where the vastest amount of nanopublications are available. It is a unified access point to the world of nanopublications enabling search over graph data, direct connections to evidence papers, and scientific curated databases, and visual and intuitive exploration of the relation network created by the encoded scientific facts.

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“…Thus, it becomes crucial to analyze and process the articles such that these articles are very useful and informative for everyone in society. However, due to many text documents, it is hard to retrieve, classify and organize these data manually because it is time and energy-consuming [3,4,5]. As a result, humans tend to get tired and easily distracted, making mistakes.…”

Section: Introductionmentioning

confidence: 99%

Theme Identification using Machine Learning Techniques

Jayady

Antong

2021

JIAE

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With the abundance of online research platforms, much information presented in PDF files, such as articles and journals, can be obtained easily. In this case, students completing research projects would have many downloaded PDF articles on their laptops. However, identifying the target articles manually within the collection can be tiring as most articles consist of several pages that need to be analyzed. Reading each article to determine if the article relates theme and organizing the articles based on themes is time and energy-consuming. Referring to this problem, a PDF files organizer that implemented a theme identifier is necessary. Thus, work will focus on automatic text classification using the machine learning methods to build a theme identifier employed in the PDF files organizer to classify articles into augmented reality and machine learning. A total of 1000 text documents for both themes were used to build the classification model. Moreover, the pre-preprocessing step for data cleaning and TF-IDF feature extraction for text vectorization and to reduce sparse vectors were performed. 80% of the dataset were used for training, and the remaining were used to validate the trained models. The classification models proposed in this work are Linear SVM and Multinomial Naïve Bayes. The accuracy of the models was evaluated using a confusion matrix. For the Linear SVM model, grid-search optimization was performed to determine the optimal value of the Cost parameter.

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Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System

Cited by 13 publications

References 41 publications

Keyword Search over RDF: Is a Single Perspective Enough?

Keyword Search over RDF: Is a Single Perspective Enough?

Search, access, and explore life science nanopublications on the Web

Theme Identification using Machine Learning Techniques

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