Extraction of RDF Statements from Text

Martínez-Rodríguez, José-Lázaro; López-Arévalo, Iván; Ríos-Alvarado, Ana B.; Hernández, Julio; Aldana-Bobadilla, Edwin

doi:10.1007/978-3-030-21395-4_7

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Such approaches may not adequately capture topics of general interest since they miss contextual information present in crowd-sourced content. Semantic Web technologies [53] are frequently utilized to interpret documents with unique concepts that are machine-interpretable and interlinked with other data [54][55][56]. For example, events and news documents have been semantically annotated with ontologically represented elements (time, location, and persons) to obtain machine-interpretable data [57][58][59].…”

Section: Plos Onementioning

confidence: 99%

Microblog topic identification using Linked Open Data

Yildirim

Uskudarli

2020

PLoS ONE

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Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machineinterpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to the W3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.

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