Techniques to integrate Knowledge Graphs into Language Models Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

Integrating temporal data into static knowledge graphs Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Understanding the Power and Applications of Natural Language Processing Author Dhruv Gupta ( ORCID: 0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

Incorporating Knowledge Graphs to explain reasoning processes Author Amanda Kau ( ORCID: 0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

The Annual International Biocuration Conference (AIBC) was held for the first in India, at the Indian Biological Data Centre (IBDC), Regional Centre for Biotechnology (RCB), Faridabad and co-hosted by the Department of Plant Molecular Biology, University of Delhi South Campus. As usual, GigaDB had representation at the event (see write-ups of many previous meetings here), Mary Ann Tuli and Chris Hunter. Both of whom were wearing two hats!

Prompt Engineering — Part 2 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering Author Dhruv Gupta (ORCID: 0009–0004–7109–5403 ) Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Understanding how RNNs work and its applications Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

Harvesting the Power and Knowledge of Large Language Models Author Amanda Kau (ORCID: 0009–0004–4949–9284 ) Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge