Semantic Web: Past, Present, and Future
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called ``Semantic Web Layer Cake'' with an update of recent concepts that include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We conclude with an outlook on the future directions of the Semantic Web.
Code (1)
Tasks
Data IntegrationKnowledge GraphsSimilar Papers 제목 키워드 기반
Semantic Textual Similarity: past present and future
Multi-optional Many-sorted Past Present Future structures and its description
The cognitive theory of true conditions (CTTC) is a proposal to describe the model-theoretic semantics of symbolic cognitive architectures and design the implementation of cognitive abilities. The CTTC is formulated math…
Generating the Future With Adversarial Transformers
We learn models to generate the immediate future in video. This problem has two main challenges. Firstly, since the future is uncertain, models should be multi-modal, which can be difficult to learn. Secondly, since the …
Video UnderstandingRethinking Causal Mask Attention for Vision-Language Inference
Causal attention has become a foundational mechanism in autoregressive vision-language models (VLMs), unifying textual and visual inputs under a single generative framework. However, existing causal mask-based strategies…
JOADAA: joint online action detection and action anticipation
Action anticipation involves forecasting future actions by connecting past events to future ones. However, this reasoning ignores the real-life hierarchy of events which is considered to be composed of three main parts: …
Action AnticipationAction DetectionOnline Action Detection