Technology Investments and Knowledge Graphs

White House Dinner and Tech Giants' Pledges

At a recent White House dinner, where top tech CEOs gathered with President Donald Trump, a moment occurred that revealed Mark Zuckerberg's, Meta's CEO, embarrassment. During the event held on September 4, 2025, after Trump asked him about Meta's future investment plans in the United States, Zuckerberg publicly pledged to spend at least $600 billion by 2028. This figure seemed to mirror Apple's recent announcement of a similar investment.

Following this statement, an open microphone captured a conversation between Zuckerberg and Trump, where Zuckerberg whispered an apology, saying: "Sorry, I wasn't prepared... I wasn't sure what number you wanted me to mention." This moment was met with a smile from Trump, who commented to his wife Melania that Zuckerberg was not ready for the question.

The dinner also included investment declarations from other tech leaders; Sundar Pichai, Google's CEO, stated that his company would invest "over $100 billion" and grow to $250 billion in the next two years. Satya Nadella, Microsoft's CEO, declared that they are close to investing $80 billion annually in the United States. These massive investments are primarily aimed at building data centers and infrastructure to support the next wave of innovation, especially in the field of Artificial Intelligence. These investments demonstrate a strong commitment from tech giants to enhance technological capabilities in the United States to keep pace with rapid developments in the AI sector and develop the necessary infrastructure for it.

Later, Zuckerberg clarified in a post on Threads that he had informed the President about Meta's potential spending until 2028 and through the end of the decade, and that he wasn't sure what number Trump was asking for, so he shared the lower figure until 2028 and clarified the matter with him afterward. This event highlights the atmosphere that prevailed at the dinner, where tech leaders, including those previously considered critics of Trump like Bill Gates and Sam Altman, praised his leadership and focus on innovation. This article is dedicated to the Arab reader, offering a detailed look at this important event.

What is a Knowledge Graph?

A Knowledge Graph is a structured database that stores information in the form of a network of entities and relationships between them, enabling systems to understand context and provide more accurate and intelligent answers. Knowledge graphs are used to represent knowledge structurally, allowing machines to process and understand this data better by showing how things are related to each other. IBM

Key Components of a Knowledge Graph

Knowledge graphs consist of three main components: Entities, Relationships, and Attributes. Entities are the nodes that represent people, places, things, or concepts. Relationships are the edges that connect entities and describe how they are linked, such as "authored by" or "headquartered in." Attributes provide additional details about entities or relationships, such as birth date or alias. Neo4j

Benefits of Using Knowledge Graphs

Knowledge graphs offer many important benefits, most notably improving search operations through a deeper understanding of queries and providing more relevant results. They also facilitate the integration of data from diverse sources, creating a comprehensive and integrated view of information. Knowledge graphs support artificial intelligence and machine learning applications by providing structured and context-rich data, which enhances the ability of systems to make smart decisions and provide accurate analyses. Ontotext

Use Cases of Knowledge Graphs

Knowledge graphs are used in multiple fields to provide innovative solutions. In search engines, they help understand user intent and provide direct and specific answers. In healthcare, they are used to link medical information, from symptoms to treatments, supporting diagnosis and research analysis. They are also essential in corporate cognitive systems, such as customer relationship management and data analysis, to provide actionable insights and enhance user experience. Stardog

Building a Knowledge Graph

Building a knowledge graph requires following several systematic steps to ensure its effectiveness. The process begins with data collection from diverse sources, followed by the extraction of entities and relationships from this data. Then, a schema is defined that specifies the types of possible entities and relationships. After that, data is ingested into the graph structure, and entities are linked to each other to create an interconnected network. Finally, data quality and accuracy are verified to ensure the precision of the knowledge graph. DataStax

Challenges in Building Knowledge Graphs

Building knowledge graphs faces several challenges. Foremost among these challenges is ensuring data quality and standardization from different sources, as data can be inconsistent or incomplete. Scalability also presents a significant challenge, especially with the increasing volume of data and the complexity of relationships. Knowledge graphs also require continuous maintenance and updating to preserve their accuracy and relevance, in addition to the need for specialized expertise in data modeling and graph technologies. Ontotext

Future Trends in Knowledge Graphs

Knowledge graphs are heading towards a future characterized by exciting and innovative developments. Their importance is expected to grow in enhancing explainable artificial intelligence, allowing systems to clarify their decisions more clearly. They will also see deeper integration with machine learning techniques, enabling them to learn and adapt more effectively. Future trends also include the development of decentralized and collaborative knowledge graphs, where multiple sources can contribute to building and maintaining these graphs. Towards Data Science