Knowledge Graphs: Research Directions

Hogan, Aidan

doi:10.1007/978-3-030-60067-9_8

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…Future directions. Research on knowledge graphs can become a confluence of techniques from different areas with the common objective of maximising the knowledge-and thus value-that can be distilled from diverse sources at large scale using a graph-based data abstraction [56].…”

Section: Discussionmentioning

confidence: 99%

Knowledge Graphs

et al. 2021

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In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

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Section: Discussionmentioning

confidence: 99%

Knowledge Graphs

et al. 2021

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“…For practical reasons, most of the KBs are structured and represented according to the Resource Description Framework (RDF) [ 32 ] as a directed edge-labeled graph [ 33 ]. Formally, RDF is a data model (whose main component is the RDF triple) that defines elements such as Internationalized Resource Identifiers (IRIs) [ 34 ], to identify resources globally on the Web; blank nodes , particular components used for grouping data (acting as parent nodes); and literals , representing strings or datatype values.…”

Section: Methodology Designmentioning

confidence: 99%

An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web

Rubio-Sandoval

Martínez-Rodríguez

López-Arévalo

et al. 2021

Sensors

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Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.

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“…Furthermore, a large number of domain-specific KGs have been developed, for instance covering the needs of a specific business. We refer to [12,13] for a comprehensive overview about knowledge graphs. Here we focus on neural representations of KGs.…”

Section: Encoding Knowledge Graphsmentioning

confidence: 99%

Modelling Symbolic Knowledge Using Neural Representations

Schockaert

Gutiérrez-Basulto

2022

Lecture Notes in Computer Science

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Symbolic reasoning and deep learning are two fundamentally different approaches to building AI systems, with complementary strengths and weaknesses. Despite their clear differences, however, the line between these two approaches is increasingly blurry. For instance, the neural language models which are popular in Natural Language Processing are increasingly playing the role of knowledge bases, while neural network learning strategies are being used to learn symbolic knowledge, and to develop strategies for reasoning more flexibly with such knowledge. This blurring of the boundary between symbolic and neural methods offers significant opportunities for developing systems that can combine the flexibility and inductive capabilities of neural networks with the transparency and systematic reasoning abilities of symbolic frameworks. At the same time, there are still many open questions around how such a combination can best be achieved. This paper presents an overview of recent work on the relationship between symbolic knowledge and neural representations, with a focus on the use of neural networks, and vector representations more generally, for encoding knowledge.

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Knowledge Graphs: Research Directions

Cited by 8 publications

References 51 publications

Knowledge Graphs

Knowledge Graphs

An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web

Modelling Symbolic Knowledge Using Neural Representations

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