A Knowledge Graph (KG) is a model in which networks and knowledge structures can be mapped. By linking objects that are related to each other, data can be visualized easily and new knowledge can possibly be formed. Currently, they are mainly used in the field of Natural Language Processing to be able to map linguistic relationships.
How is a Knowledge Graph structured?
A Knowledge Graph attempts to represent objects and their relationships optimally in a network. The structure consists of these three components:
- Nodes: These represent objects, such as people, companies, or places, and are represented in the graph as circles or points.
- Edges: Edges connect nodes together, thereby building the network structure.
- Labels: These labels categorize the edges and indicate the type of relationship. Classic labels are, for example, “is a supplier of”, “is friends with”, and “is a part of”.
In many cases, it is a so-called directed graph. This means that the edges have a unique direction and start at one node and end at another.
In fact, indirect information is also stored in this direction. Let’s assume we want to create a network of customers and suppliers in the automotive sector. The edge “delivers to” then always starts at the supplier and ends at the customer. If this edge was not directed, it would not be possible to determine which company is the supplier and which is the customer.
How are Knowledge Graphs built?
There are several ways to build such a graph. In some cases, already structured data can be used to build the network. For example, one can display the different sub-pages of a news website in a graph with connections to the different subject areas. For each article, it is already stored whether it belongs to the “sports”, “finance” or “politics” section. Thus the values for nodes and edges are already stored. However, it becomes more difficult if the information is not yet clean and structured.
In the field of Natural Language Processing, attempts are made to extract the graph structure from the natural language and, for example, to represent the knowledge in a Wikipedia article clearly and simply in a graph. Then, using various machine learning models, an attempt can be made to extract the objects for nodes and edges from the text. For example, there are various algorithms for Named Entity Recognition, which automatically detect entities in texts that can potentially be used as nodes.
Which Applications use these Graphs?
Despite the sometimes complicated creation of knowledge graphs, they are already being used in various application fields:
- E-commerce: Many stores try to suggest suitable products to their customers in order to increase sales. These so-called recommendations can be obtained from Knowledge Graph by building a network of products there that has as edges the purchases from a shopping cart.
- Finance: In the banking sector, knowledge graphs are created to detect and report fraudulent transfers. For this purpose, knowledge can be generated from previous transactions to find out in which areas transfers are normal and in which they are not.
- Search Algorithms: In a general linguistic graph, different spellings or colloquial phrases can be collected and put into context. This allows the search to understand, for example, that mobile and smartphone are often used as synonyms.
- Chatbot: A chatbot draws on Knowledge Graphs to get a better understanding of the communication and provide more targeted responses. In addition, a chatbot also usually draws its knowledge to be able to provide answers from graph-like structures.
What are the public Knowledge Graphs?
There are already some large Knowledge Graphs, some of which are also publicly accessible. They originate from different areas, such as language, geography, or contexts in the field of search algorithms. The best-known knowledge graphs include:
- Google Knowledge Graph: Probably the best-known Knowledge Graph comes from Google and is primarily used to improve the search algorithm. Google started collecting data in 2012 and has so far revealed little information about the graph’s design and structure.
- DBPedia: This graph is built from Wikipedia’s short infoboxes and is intended to bundle knowledge in a wide variety of fields. It contains a wide variety of entities, such as people, places, movies, and companies.
- Geonames: This knowledge base provides access to many geographic entities, such as countries, cities, or places, as well as the associated information.
- Wordnet: This graph is probably the best-known knowledge structure for the English language and contains word definitions or matching synonyms.
How to use Knowledge Graphs in Python?
Knowledge graphs are powerful tools for representing and organizing structured information. In Python, there are several libraries and frameworks available that facilitate working with them. Here’s a section on how to use them in Python:
- Choose a Library: Python provides several libraries for working with knowledge graphs, such as RDFLib, PyKEEN, and NetworkX. Select a library that suits your specific requirements.
- Define the Schema: Before creating a knowledge graph, define the schema that describes the entities, relationships, and attributes you want to represent. This step helps in organizing and structuring the information effectively.
- Import and Create the Graph: Import the chosen library and create an empty graph object. For example, using RDFLib, you can create an RDF graph by instantiating the Graph class:
- Populate the Graph: Add nodes (entities) and edges (relationships) to the graph. Use the library-specific methods to define nodes, relationships, and their properties. For instance, in RDFLib, you can add triples using the
- Query and Manipulate the Graph: Once the knowledge graph is populated, you can query and manipulate the data using the library’s query languages or APIs. For example, RDFLib supports SPARQL for querying RDF graphs.
- Visualize the Graph: Use visualization libraries like NetworkX or Graph-tool to visualize the knowledge graph. These libraries provide functions to generate visual representations of the graph, making it easier to understand its structure and relationships.
- Integrate with Other Tools and Applications: The graphs can be integrated with other Python libraries and frameworks to perform tasks like Machine Learning, natural language processing, or semantic reasoning.
By following these steps, you can effectively use knowledge graphs in Python to represent, organize, and query structured information, enabling powerful data-driven applications and insights.
How are Knowledge Graphs integrated with Machine Learning an AI?
Knowledge graphs and Machine Learning (ML) are two powerful technologies that can complement each other and lead to enhanced understanding, reasoning, and decision-making. The integration of knowledge graphs with ML and AI techniques allows us to leverage structured knowledge to improve the performance and capabilities of various applications. Here are some ways in which knowledge graphs and ML/AI can be effectively combined:
- Knowledge-Enhanced Learning: By incorporating knowledge graphs as an additional source of information, ML models can benefit from structured knowledge during the learning process. This can help in feature engineering, entity resolution, or providing additional context to the learning algorithm.
- Knowledge Graph Embeddings: These embeddings aim to represent entities and relationships in a continuous vector space. They can be used as input features to ML models, enabling them to leverage the rich semantic information encoded in the knowledge graph.
- Semantic Similarity and Recommendation: The graphs can capture semantic relationships between entities. ML techniques, such as natural language processing and deep learning, can exploit these relationships to compute semantic similarity measures, enabling better recommendations and content understanding.
- Explainable AI: Knowledge graphs provide a transparent and interpretable representation of knowledge. ML models can leverage the explicit semantics of a graph to generate explanations for their predictions, enhancing the interpretability and trustworthiness of AI systems.
- Ontology-Aware ML: Ontologies can be represented as knowledge graphs and provide a structured schema for domain-specific knowledge. ML models can utilize ontology to guide the learning process, enforce constraints, or incorporate domain-specific rules.
- Question Answering and Information Retrieval: The graphs can serve as a valuable source of structured information for question-answering and information retrieval tasks. ML models can be trained to understand natural language queries and use them to retrieve relevant information and provide accurate answers.
- Knowledge-Driven Decision-Making: ML models can leverage the insights and reasoning capabilities of knowledge graphs to make informed decisions. By combining statistical learning with logical reasoning over the graph, AI systems can provide more intelligent and context-aware recommendations or decision support.
- Knowledge Graph Completion: The graphs often suffer from incompleteness, where certain relationships or facts are missing. ML techniques, such as link prediction or knowledge graph completion algorithms, can be applied to infer missing relationships based on the existing knowledge.
The integration of knowledge graphs with ML and AI is a rapidly evolving field, with ongoing research and practical applications in various domains. It enables the development of intelligent systems that leverage structured knowledge to improve accuracy, explainability, and domain understanding. By combining the strengths of knowledge graphs and ML/AI, we can unlock new possibilities for solving complex problems and advancing AI technologies.
This is what you should take with you
- A knowledge graph is a model in which networks and knowledge structures can be represented.
- It consists of nodes, edges, and labels.
- Knowledge graphs are used especially in the field of language processing, for example, to recognize synonyms and thus to better understand natural language.
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