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The knowledge graph is a conceptual graphical knowledge base designed to realize intelligent information processing. It is based on a graph structure and aims to capture and express the relationship between things. The knowledge graph contains a series of entities, attributes, and relationships, where entities represent various objects in the real world, attributes represent the characteristics of entities, and relations represent the connections between entities.
The construction of a knowledge map requires a large number of data sources, including structured data, semi-structured data, and unstructured data. These data need to be processed and analyzed by various algorithms and techniques, and then a knowledge map is built.
In the development of knowledge graphs, many well-known knowledge graphs have emerged, such as DBpedia, Freebase, Wikidata, etc. At the same time, many large companies have also begun to focus on the application and research of knowledge graphs, such as Google, Facebook, Microsoft, etc. The development of knowledge graphs shows a trend of diversification and openness, and the application of knowledge graphs is becoming more and more extensive.
1. What is a knowledge graph
A knowledge graph is a tool that graphically displays knowledge structures and knowledge relationships. It is different from ordinary knowledge bases. The knowledge map uses a graph database, which can handle a large amount of knowledge and relationships more efficiently.
Composition
The knowledge graph mainly consists of three parts: entities, attributes, and relationships. Entities refer to specific things or concepts, attributes refer to the characteristics of entities, and relationships refer to the connections between entities. For example, a person can be an entity, his name, gender, age, etc. are attributes, and his relationship with other people is a relationship.
2. Application of Knowledge Graph
2.1 Search Engines
Search engines are one of the most common knowledge graph application scenarios. Search engines can use knowledge graphs to understand users' query intentions and improve the accuracy and relevance of search results. For example, when a user searches for "Paris Tower", the search engine can associate the search result with "Eiffel Tower" through the entity relationship of the knowledge graph.
2.2 Question Answering System
Question answering system is another common knowledge graph application scenario. The question answering system can answer users' questions through the knowledge base of the knowledge graph. For example, when a user asks "what is artificial intelligence?", the question answering system can leverage the relevant entities and attributes in the knowledge graph to generate accurate answers.
2.3 Intelligent Customer Service
Knowledge graphs can provide intelligent customer service with a knowledge base and semantic understanding capabilities, so that they can better understand users' questions and give accurate answers. For example, when a user consults the customer service about reporting the loss of a bank card, the intelligent customer service can provide relevant services and solutions through the entities and relationships in the knowledge graph.
2.4 Smart Recommendation
The knowledge graph can provide more accurate recommendations for the recommendation system. For example, when a user browses products on a shopping website, the recommendation system can analyze the user's interest and behavior through the entities and relationships in the knowledge graph, and recommend products that may be of interest.
2.5 Natural Language Processing
Knowledge graph can provide knowledge base and semantic understanding ability for natural language processing, so as to better understand natural language text. For example, in text classification, knowledge graphs can help identify and classify entities and relationships in text, improving classification accuracy.
3. The development trend of knowledge graph
With the rapid development of the Internet and the continuous maturity of knowledge graph technology, knowledge graph will be applied in more fields. In the future, the development trend of knowledge graph is mainly reflected in the following aspects:
3.1 Multimodal Knowledge Graph
The future knowledge graph includes not only text and structured data, but also various forms of data such as images, videos, and audios. Multimodal knowledge graphs will bring richer and more comprehensive knowledge representations, and can provide richer data sources for image recognition, speech recognition, and natural language processing in the field of artificial intelligence.
3.2 Open and share
Knowledge graphs need to be continuously updated and expanded, so openness and sharing will become the development trend of knowledge graphs in the future. Openness and sharing can promote knowledge exchange and cooperation between different fields, and improve the accuracy and completeness of knowledge graphs.
3.3 Self-directed learning
The future knowledge map needs to have the ability to learn and update independently. Self-learning can make the knowledge map more adaptable to practical application scenarios, and can also reduce the maintenance cost of the knowledge map.
3.4 Knowledge reasoning
The future knowledge map needs to have the ability of knowledge reasoning, which can generate new knowledge by reasoning out the relationship and laws between entities. Knowledge reasoning can bring higher accuracy and intelligence to the application of knowledge graphs.
3.5 Decentralization
The future knowledge map may move towards decentralization and no longer depends on a specific institution or organization for maintenance. Decentralization can make the knowledge graph more democratic and open, and it can also reduce the maintenance cost and risk of the knowledge graph.
4. Common native knowledge graph storage management methods include:
4.1 Graph database
A graph database is a database dedicated to storing graph data. It represents entities, attributes, and relationships in the form of nodes and edges, and provides efficient data query and analysis functions through technologies such as indexing and query optimization. Common graph databases include Neo4j, JanusGraph, etc.
4.2 Triple storage
Triple storage refers to storing entities, attributes, and relationships as triples, where each triple represents an entity and its corresponding attributes and relationships. Triple storage is usually implemented using a relational database or NoSQL database. Common triplet storage includes Apache Jena, OpenLink Virtuoso, etc.
4.3 Knowledge graph storage framework
The knowledge map storage framework is a storage method that combines graph database and triple storage. It uses graph database to store entities and relationships, and uses triple storage to store attribute information of entities. The knowledge graph storage framework aims to provide more flexible and efficient knowledge graph storage and query capabilities. Common knowledge graph storage frameworks include Tinkerpop Gremlin, Apache Jena Fuseki, etc.
When managing native knowledge graph storage, the following aspects need to be considered:
Storage structure
The storage structure of the knowledge map should be reasonable to support efficient query and analysis. It is necessary to select an appropriate storage structure according to the actual application scenario and data characteristics.
Data import
The construction of a knowledge map requires a lot of data import work, and it is necessary to provide efficient data import and processing tools to support the import and conversion of various data sources.
Data query and analysis
The application of knowledge graph needs to support efficient data query and analysis. It is necessary to provide various query and analysis tools and optimize query and analysis performance to improve user experience.
Security and Reliability
The data of the knowledge graph is very important, and it is necessary to provide a safe and reliable storage and management method. Various security measures, such as access control, backup and recovery, etc., need to be adopted to protect the data security of the knowledge graph.
5. Knowledge Graph Query Language
Knowledge Graph Query Language is a language for querying data in knowledge graphs. Common knowledge graph query languages include SPARQL and Gremlin.
5.1 SPARQL
SPARQL (SPARQL Protocol and RDF Query Language) is a query language for RDF data. It can query knowledge map data stored in RDF format, support querying information such as entities, relationships, attributes, etc., and can perform complex queries and group aggregation operations with multiple conditions.
A SPARQL query usually consists of three parts: query body, selection set, and constraints. Among them, the query body indicates the entities, relationships and attributes of the query, the selection set indicates the entities, relationships and attributes that need to be retained in the query results, and the restriction conditions are used to limit the quantity and content of the query results. SPARQL queries can interact with the knowledge graph storage system through the standardized SPARQL protocol.
Here is an example of a SPARQL query, assuming we want to query for a person's name, age, and place of residence:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name ?age ?location
WHERE {
?person foaf:name ?name .
?person foaf:age ?age .
?person foaf:location ?location .
FILTER (?name = "John Smith" && ?age > 30)
}
The query first defines a namespace foaf through the prefix declaration, and then uses the SELECT statement to specify the variables to be queried, namely ?name, ?age, and ?location. In the WHERE block, we use three triplet patterns to describe the patterns we want to query. Each triple pattern includes a subject, predicate, and object, representing a relationship between an entity, an attribute, and an attribute value. In this example, we use ?person as the entity variable, and obtain the corresponding attribute values through the attributes of foaf:name, foaf:age and foaf:location. Finally, we use the FILTER keyword to limit the query results to people whose first name is "John Smith" and whose age is greater than 30.
5.2 Gremlin
Gremlin is a general-purpose graph traversal language that can be used to query various types of graph data, including knowledge graphs. Gremlin uses a graph-like way to represent data, and supports multiple traversal methods such as depth-first traversal and breadth-first traversal.
Gremlin queries usually consist of traversers, steps, filters, etc. The traverser is used to specify the starting point and traversal direction, the step is used to specify the specific traversal method and conditions, and the filter is used to filter the query results. The Gremlin query language can interact with the knowledge graph storage system through the open source TinkerPop graph computing framework.
SPARQL and Gremlin are two common knowledge graph query languages, which are suitable for querying RDF data and graph data respectively. In practical applications, it is necessary to select the appropriate query language and tools according to specific requirements and data models to achieve the best results.