Knowledge Graph Completion is a task in which computers predict unseen relationships between two already known entities or predict the tail entity when the head entity and the query relationship are known. Knowledge graphs are collections of triples that represent entities and relationships among them.
What is a Knowledge Graph?
A knowledge graph is a collection of interconnected triples that represent real-world objects and their relationships. Each triple consists of three parts: a head entity, a relationship, and a tail entity. An entity is an object in the world, such as a person, a place, or a thing. A relationship is a connection between two entities and is represented by a verb or a preposition. For example, the relationship "lives in" connects a person to a place.
Knowledge graphs are used in artificial intelligence and machine learning to provide contextual information to help computers understand real-world concepts. They're used by search engines like Google, which uses them to better understand user queries and provide relevant results.
What is Knowledge Graph Completion?
Knowledge Graph Completion is a natural language processing task that involves completing or predicting missing information in a knowledge graph. There are two types of predictions made in this task.
The first type of prediction is predicting unseen relationships between two previously known entities. For example, if we know that "Barack Obama" is the head entity and "President of the United States" is the tail entity in a triple, we can use a knowledge graph completion algorithm to predict the missing relationship "served as" between the two entities.
The second type of prediction involves predicting the tail entity given the head entity and query relationship. For example, if we know that "Apple Inc." and "founded" are the head entity and relationship in a triple, we can use a knowledge graph completion algorithm to predict the missing tail entity "Steve Jobs".
Why is Knowledge Graph Completion important?
Knowledge Graph Completion is important because it helps fill in gaps in our understanding of real-world concepts. By automatically predicting missing relationships between entities, we can build more comprehensive knowledge graphs that better represent the world around us. This can lead to better search results, more accurate language models, and improved natural language understanding.
Knowledge Graph Completion also has important applications in fields like medicine, where knowledge graphs can be used to represent relationships between diseases, symptoms, and treatments. By completing missing information in these graphs, we can gain a better understanding of complex medical concepts and discover new connections between diseases and treatments.
How is Knowledge Graph Completion done?
Knowledge Graph Completion is typically done using machine learning algorithms that learn to predict missing relationships based on patterns found in the existing knowledge graph. These algorithms can be trained on large datasets of known relationships and used to predict missing relationships in new data.
One approach to Knowledge Graph Completion is to use embedding models to represent each entity and relationship as a vector in high-dimensional space. These vectors can be used to calculate the similarity between entities and relationships and predict missing relationships based on their proximity in the vector space.
Another approach is to use rule-based systems that encode expert knowledge about relationships between entities. These systems can be used to predict missing relationships based on a set of predefined rules, such as "if X is a parent of Y and Y is a child of Z, then X is a grandparent of Z."
Knowledge Graph Completion is a task that involves predicting missing relationships between entities in a knowledge graph. It plays an important role in natural language processing and has applications in fields like medicine and information retrieval.
Knowledge Graph Completion is typically done using machine learning algorithms like embedding models or rule-based systems. By improving our ability to automatically predict missing information in knowledge graphs, we can build more comprehensive models of the world around us and improve our understanding of complex concepts.
