Natural Language Processing for the Semantic Web

Abstract

is book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks.e first half of this book describes the basic NLP processing tools: tokenization, part-ofspeech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. e second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. ese chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. e book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future. KEYWORDSnatural language processing, semantic web, semantic search, social media analysis, text mining, linked data, entity linking, information extraction, sentiment analysis is book is a timely exposition of natural language processing and its role and importance for those seeking to apply semantic technologies. Clearly written with good coverage of the key topics and a comprehensive bibliography, the text will be invaluable for semantic web practitioners and more widely.

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

“…Most of the analyzed investigations use machine learning to evaluate sentiment. Natural language processing techniques are explored in the approaches proposed in [52,58,60,[62][63][64]74,75]; data mining and semantic analysis are used in [51]. In general, the studies presented better assertiveness measures in the categorization of sentiment using complementary approaches (e.g., semantic descriptors with ML) as compared with a single one.…”

A Framework for Real-Time Semantic Social Media Analysis

“…This builds on previous work where LOD vocabularies were applied to semantic enrichment of environmental texts in the EnviLOD project [22]. A more detailed explanation of the general semantic annotation and querying process used here can be found in [23,13].…”

Engineering Reliable Interactions in the Reality-Artificiality Continuum

“…Once ontologies enter the RAC, they can also serve as a means to ensure semantic interoperability among different users and to ease natural language interaction. This would be a more standard way to use ontologies in RAC, since the relationships between natural language processing and ontologies have been studied since the advent of the semantic web [42,43,55].…”