Knowledge graphs are widely used in industry and studied within the academic community.
However, the models applied in the development of knowledge graphs vary. Analysing and …

The necessity for scholarly knowledge mining and management has grown significantly as
academic literature and its linkages to authors produce enormously. Information extraction …

Instant analysis of cybersecurity reports is a fundamental challenge for security experts as
an immeasurable amount of cyber information is generated on a daily basis, which …

Quantitative investment (``quant'') is an interdisciplinary field combining financial
engineering, computer science, mathematics, statistics, etc. Quant has become one of the …

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying …

Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of
meteorological conditions. Scattered and multi-source big data cannot be fully utilized to …

Massive amount of unstructured text data are generated daily, ranging from news articles to
scientific papers. How to mine structured knowledge from the text data remains a crucial …

Knowledge graph has become an essential tool for semantic analysis with the development
of natural language processing and deep learning. A high-quality knowledge graph is handy …

Online communities, where lead users openly share their experiences and knowledge on
product and technology in the form of a post, have become a fruitful source of innovation …

Event extraction (EE) is a challenging task in information extraction, which aims at extracting
the event details including event type, trigger, argument and its corresponding role from the …