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Sunday, July 14, 2024

Explained: Knowledge Graph in ML

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Explore how knowledge graphs organize information and connect concepts to enhance machine learning. From search results to chatbots, knowledge graphs improve AI applications by providing context and relationships between data.

What is a knowledge graph in ML?

In machine learning (ML), a knowledge graph is a graphical representation that captures the connections between different entities. It consists of nodes, which represent entities or concepts, and edges, which represent the relationships between those entities.

Google coined the term knowledge graph in 2012 to refer to its general-purpose knowledge base. However, knowledge graphs have existed since the beginning of modern artificial intelligence (AI). They are used in areas such as knowledge representation, knowledge acquisition, natural language processing (NLP), ontology engineering, and the Semantic Web.

Knowledge graphs are particularly useful in data science for adding identifiers and descriptions to various types of data, enabling sense-making, integration, and explainable analysis. They improve applications such as chatbots, search engines, product recommenders, and autonomous systems.

How a knowledge graph works

A knowledge graph functions by structuring and linking information in a formatted, graph-like arrangement. It extracts data from several data sets and applies identities and schemas to provide context and organization to the data. Knowledge graphs are specifically designed to quickly store, retrieve, and evaluate factual data in an easily navigable manner.

To determine the relationships between data and objects, knowledge graphs also use ML and NLP.

The following is a broad overview of how a knowledge graph operates:

  • Unification of disparate data sources. Knowledge graphs can be constructed using various data sources, including semi-structured, unstructured, and structured data from relational databases. Unstructured data include free text, photos, and documents, whereas semi-structured data include Hypertext Markup Language, JavaScript Object Notation, and Extensible Markup Language. Common sources of knowledge graphs include Wikipedia and domain-specific project repositories. When knowledge graphs are blended with generative AI methods such as NLP, organizations can gather valuable insights from different data sources to create a cohesive knowledge representation.
  • Knowledge extraction. Once the data is gathered, the information extraction process begins. To accomplish this, essential details from the incoming data—such as entities, relationships, and attributes—must be extracted. Techniques such as text mining, machine learning, and NLP are commonly used for this purpose.
  • Graph representation. Next, the extracted knowledge is displayed in a graph format. A knowledge graph’s edges show the connections between the nodes, which represent entities or concepts. Attributes can also be connected to nodes and edges to provide more information.
  • Schema and ontology. A schema or an ontology is frequently used in knowledge graphs to specify the graph’s structure and semantics. Usually based on a taxonomy, an ontology formally represents the items and their relationships. It aids in encoding the data’s meaning for programmatic usage.
  • Reasonings and inference. Knowledge graphs can use reasoning techniques to draw conclusions based on the information already available or to generate new knowledge. Reasoning fills in knowledge gaps and facilitates deeper analysis and decision-making by highlighting connections that might be overlooked.
  • Integration and exploration. Knowledge graphs facilitate the assimilation of fresh data sets and formats by connecting them to preexisting nodes and relationships. This makes it easier for users to explore the graph and lets them move easily between sections by clicking on related links. Because of the built-in graph structure, the information can be efficiently retrieved and explored.

Are knowledge graphs a part of machine learning?

Knowledge graphs are frequently used in tandem with machine learning techniques. Machine learning is a subfield of artificial intelligence and computer science that uses data and algorithms to mimic how humans learn. It entails creating algorithms that can learn from data and improve their accuracy over time without having to be explicitly coded. Machine learning algorithms search for patterns in massive volumes of data and use those patterns to generate predictions or conduct actions.

While knowledge graphs aren’t fundamentally a part of machine learning, they can significantly improve the capabilities and performance of machine learning models.

Both machine learning and knowledge graphs complement each other. Knowledge graphs provide organized knowledge and relationships that can improve the performance of machine learning models by reducing the need for huge, labeled data sets, facilitating transfer learning, and improving the predictability and trustworthiness of the models’ predictions.

The significance of combining knowledge graphs with AI

Knowledge graphs and AI go hand in hand in intelligent systems and information processing. Incorporating AI with knowledge graphs provides the following benefits:

  • Improved context and understanding. Knowledge graphs offer an organized representation of data by capturing relationships and semantics. Large language models (LLMs) and knowledge graphs can be combined to improve the context and comprehension of AI systems. The structured representation of knowledge graphs enhances the semantic depth, making AI systems more accurate, understandable, and context-aware.
  • Works in tandem with existing tools. Knowledge graphs that offer virtualization maintain data accuracy and improve productivity while easily integrating with existing tools and frameworks, such as Python and R. The semantic layer of a knowledge graph encourages reuse and interoperability, eliminating the need to start from scratch each time.
  • High productivity for data workers. Data scientists and machine learning engineers often spend significant time on data-wrangling techniques that involve manual data gathering and cleansing. Since knowledge graphs enable AI models to be trained directly on unified data with uniform terminologies and synthesized sources, they can save data workers a substantial amount of time.
  • Improved natural language understanding. Although knowledge graphs are excellent at capturing organized data, they can have trouble comprehending unstructured text and natural language. LLMs, which excel at comprehending natural language, can be integrated with knowledge graphs to close this gap and improve the ability of AI systems to grasp and analyze unstructured text.
  • Offers enhanced decision-making. Knowledge graphs organize data relationships logically. When combined with AI, this increases the intelligence of the data, providing AI systems with the background necessary to make trustworthy decisions. This integration empowers AI systems to use structured information in knowledge graphs for improved predictions, recommendations, and insights.
  • Advanced applications. AI and knowledge graphs can create new opportunities for complex applications. For instance, chatbots can use knowledge graphs to deliver context-aware responses and have deeper dialogues. Knowledge graphs include structured information that AI systems can use for various functions, including information retrieval, recommendation systems, and question-answering.

The use cases for knowledge graphs with machine learning

Combining knowledge graphs and machine learning has significant applications in various disciplines. Common use cases include the following:

  • Improved search and recommendation systems. Knowledge graphs can improve search engine results and recommendation systems by comprehending the context and relationships between things. Search engines and recommendation systems can provide users with more complete and pertinent results using knowledge graphs’ structured data.
  • Chatbots and virtual assistants. Knowledge graphs can help with meaningful dialogue and question-answering. Virtual assistants and chatbots can deliver precise and context-aware responses by finding pertinent data and connections within the network.
  • Semantic search. Because knowledge graphs can interpret the semantic meaning of documents and queries, they can improve search capabilities. This enhances the user experience by enabling more precise and context-aware search results.
  • Model training. Machine learning models can be trained using knowledge graphs, especially in graph-native learning methods. By calculating machine learning problems inside a graph structure, a process known as graph-native learning, models can learn generalized, predictive properties directly from the network. This approach is beneficial when the most important features or data structures aren’t known.
  • Comprehensive customer view. Knowledge graphs can be utilized to generate an all-encompassing perspective of consumers or organizations by integrating and analyzing data from multiple sources. Thanks to this uniform representation, organizations may obtain insights, make wise decisions, and customize consumer experiences.
  • Modernization of analytics. Knowledge graphs offer an organized method for organizing and representing data, which can be used to modernize analytics operations. They support advanced analytics approaches, enhance data exploration, and aid in integrating different data sources.
  • Data science and analytics. Knowledge graphs can effectively represent and store large volumes of related data. In addition to managing large data sets, they can carry out inference and reasoning tasks, find new links, and validate previously discovered knowledge in data science and analytics applications.

Examples of knowledge graphs

Various knowledge graph providers are available. While some graphs are proprietary, others are open-source and can be used by anyone.

Examples of knowledge graphs include the following:

  • DBpedia. The large-scale, open-source knowledge repository DBpedia was created in 2007 using the structured data found in Wikipedia. It depicts data using a knowledge graph and attempts to improve Wikipedia’s content accessibility, machine-readable quality, and suitability for various uses, including scholarly study. DBpedia pulls structured data from Wikipedia infoboxes, categories, links, and other content and uses the DBpedia ontology to transform it into a standard format.
  • Diffbot. Diffbot offers a massive knowledge graph encompassing more than 10 billion entities, such as individuals, organizations, goods, publications and conversations. The Diffbot knowledge graph is designed to deliver organized and clear internet content.
  • GeoNames. GeoNames is an open and freely accessible knowledge graph for global geographical entities. It provides users with convenient access to more than 11 million place names under a Creative Commons Attribution license and records coordinates and population density, which can be valuable information for campers or travelers seeking directions.
  • Google Knowledge Graph. Google’s Knowledge Graph gives consumers more contextual, relevant, and educational search results by determining the connections between various entities. This knowledge graph is a Google search engine results page offering information derived from global user searches. Encompassing more than 500 million entities, including people, places, businesses, and objects, it aggregates data from diverse sources such as Wikipedia, Freebase, and the CIA World Factbook. This feature is beneficial for students and researchers engaged in extensive research projects.
  • Neo4j. Neo4j is a graph database for building knowledge graphs. It provides sophisticated reasoning and decision-making, letting users build linked data models enhanced with semantics.
  • Stardog. Stardog is an enterprise knowledge graph platform that uses semantic graph technology to help businesses combine and query their data. It offers a comprehensive method for handling mixed data and provides conversational data access features.
  • WordNet. WordNet is a lexical knowledge graph that focuses on words and their relationships. It offers word definitions, synonyms, and semantic correlations in more than 200 languages.

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