50% Off Paramount+ Annual Subscriptions

Rising Streaming Costs: With the continuously increasing costs of TV streaming services, finding value deals can become rare. However, such opportunities emerge from time to time, and this is currently the case with Paramount+. You can take advantage of a 50% discount on annual subscriptions until September 18. This means that an Essential plan (ad-supported) will be priced at $30 for a full year instead of $60, while the Premium plan will be available for $60 for one year instead of $120.

Excellent Opportunity: This offer is an excellent opportunity that both new and existing subscribers can take advantage of; it's not common for such significant discounts to be offered and not limited to new subscribers only. Anyone who subscribes to Paramount+ for one year from now until September 18 will be able to get this reduced price. The only condition for this offer is pre-payment of the full annual subscription, while monthly subscriptions will remain at their regular price.

Wide and Distinguished Selection: Paramount+ offers a wide and distinguished selection of programs, especially if you are a fan of all things RuPaul. It is also the exclusive platform for the series Star Trek: Strange New Worlds and Star Trek: Lower Decks, which are considered among the most prominent recent additions to the famous science fiction series, in addition to other previous and current Star Trek series. The platform also provides a strong array of diverse sports content. And if you choose to subscribe to the Premium Plan, you will also get access to exclusive Showtime shows like Yellowjackets and the reboot of the series Dexter: Resurrection. These offers reflect the continuous development and innovation in the world of technology and digital entertainment.

What is a Knowledge Graph?

The Knowledge Graph: A Knowledge Graph, also known as a semantic network, is a structured representation of real-world entities (such as people, places, events, or concepts) and the relationships between them. This type of information is typically stored in a graph database and depicted as a graphical structure that connects data within its context through semantic links and information. The Knowledge Graph aims to provide a framework for integrating, unifying, analyzing, and sharing data, allowing for a deeper understanding of complex relationships between different pieces of information. Knowledge Graphs can help both humans and machines better understand the world, and are used to organize vast amounts of data, making them easy to understand and use for intelligent systems and artificial intelligence applications. (Source: IBM, Ontotext, Wikipedia, Neo4j).

Benefits of Using Knowledge Graphs

Key Benefits: Knowledge Graphs offer numerous significant benefits for organizations and data-driven systems. They unify and integrate data from various sources, breaking down information silos and making data more effectively usable. By connecting entities and defining relationships between them, Knowledge Graphs provide a rich context for data, enhancing its understanding and helping to extract deeper insights. They also improve the accuracy and effectiveness of artificial intelligence applications, such as search engines, recommendation systems, and natural language processing, by providing them with a structural understanding of the world. Thanks to their ability to organize and interpret large amounts of complex data, Knowledge Graphs enable better and more informed decision-making. (Source: Ontotext, Neo4j, Yext).

Use Cases of Knowledge Graphs

Wide Range of Applications: Knowledge Graphs are used in a wide range of applications across various industries, due to their superior ability to organize and connect data. Among their most prominent uses is improving search engines, where they help Google and other search engines understand relationships between entities and provide more accurate and relevant results. They also form the basis for advanced recommendation systems in e-commerce and streaming services, suggesting products or content based on user preferences and complex relationships between items. In healthcare, Knowledge Graphs are used to link and analyze medical data to detect patterns and potential causes of diseases. In the financial sector, they contribute to fraud detection and risk analysis by identifying unusual relationships between transactions and entities. Additionally, they are used in artificial intelligence to assist in natural language processing and building smarter conversational systems. (Source: IBM, Ontotext, Wikipedia, Neo4j).

Challenges in Building Knowledge Graphs

Significant Challenges: Despite the many benefits of Knowledge Graphs, their construction and maintenance face significant challenges. One of the main challenges is integrating data from multiple and disparate sources, which may be unstructured or in different formats, requiring significant efforts to unify and process them. Ensuring data quality, accuracy, and consistency is also vital, as inaccurate data can lead to erroneous conclusions. The process of creating Knowledge Graphs also requires precise definition and classification of relationships between entities, which can be complex and time-consuming, especially in large knowledge domains. Additionally, Knowledge Graphs need continuous updating to keep pace with changes in information and entities, posing an ongoing maintenance challenge. Other challenges include the difficulty of automatically extracting knowledge from unstructured texts and the need for domain experts to effectively build and develop these Knowledge Graphs. (Source: Analytics Vidhya, PoolParty, Quantexa).