Note: The above opportunities are explained in more detail in the seminar report of Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web (Dagstuhl Seminar 18371) Some Use Cases Knowledge Graph can alleviate this problem by mapping the explanations to some proper nodes in the graph and summarizing the decision-taking process. One issue is the implicit representations causing the predictions from the machine learning models. One of the major problems in machine learning industry is explaining the predictions made by machine learning systems. The induction from the Machine Learning model can be complemented with a deduction from the Knowledge Graph, e.g., with pictures where the type of situation did not appear in the training data. This is where knowledge graphs can play a very big role. In Machine Learning, this is considered as Zero-Shot Learning problem. Today, the main challenge with a Machine Learning model is that without a properly trained data it can not distinguish between two data points. This way a huge number of both positive and negative examples can be created using Knowledge Graph. In the case of sparse data, Knowledge Graph can be used to augment the training data, e.g., replacing the entity name from original training data with an entity name of a similar type. Having a sufficient amount of data to train a machine learning model is very important. For example, results inferred from Machine Learning models will have better explainability and trustworthiness.īelow are some of the opportunities that can be availed by bringing Knowledge Graph to Machine Learning: Data Insufficiency However, bringing knowledge graphs and machine learning together will systematically improve the accuracy of the systems and extend the range of machine learning capabilities. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In a broader perspective, a Knowledge Graph is a variant of semantic network with added constraints whose scope, structure, characteristics and even uses are not fully realized and in the process of development. So, there is no formal definition of Knowledge Graph. ![]() For example, Google’s Knowledge Graph, Knowledge Vault, Microsoft’s Satori, Facebook’s Entities Graph, etc. An Undefined DefinitionĮvery Company/Group/Individual creates their own version of the Knowledge Graph to limit complexity and organize information into data and knowledge. In 2012, Google named its Knowledge Graph as Knowledge Graph. Tim Berners-Lee stated that “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” In 2001, Tim Berners-Lee coined the term Semantic Web which is an application of Semantic Network combined with the Web. The main idea behind Semantic Networks was to capture a wide range of issues which includes the representation of plans, actions, time, individuals’ beliefs and intentions, and be general enough to accommodate each issue.Īccording to Wikipedia, in late 1980, two Netherlands universities started a project called Knowledge Graph which was kind of a semantic network, but with some added restrictions to facilitate algebraic operations on the graph. In 1960, Semantic Networks were invented to address the growing need for a knowledge representation framework that can capture a wide range of entities - real-world objects, events, situations or abstract concepts and relations and in the end can be applied to extended English Dialogue tasks. ![]() Not only search engines, social network sites (e.g., Facebook, etc.), e-commerce sites (e.g., Amazon, etc.) are also using Knowledge Graphs to store and retrieve useful information. In most of those successful search engines, the most important denominator is the use of Knowledge Graphs. ![]() For example, there are more than 1.94 billion websites that are linked with the World Wide Web and search engines (e.g., Google, Bing, etc.) can go through those links and serve useful information with great precision and speed. Amount of information available today on the web is astounding and it is ever-expanding.
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