EpiK Protocol Knowledge Graph Technology
What is Knowledge Graph Technology?
Voice assistants (such as Alexa, Siri, or Google Assistant), smart search results, and even personalized shopping experiences through online store recommenders have all introduced Knowledge Graphs into our daily lives. On a daily basis, we engage with Knowledge Graphs in a variety of ways. However, most people are still unfamiliar with Knowledge Graphs and the underlying graph databases, and because of the technology’s seamless integration into our lives, most of us aren’t even aware of how reliant we are on it — or, worse, how we have come to expect a certain level of quality and standard.
A knowledge graph is a knowledge base that integrates data using a graph-structured data model or topology. Knowledge networks are frequently used to contain interconnected descriptions of items having free-form semantics, such as objects, events, circumstances, or abstract concepts.
Knowledge Graph gives all of your data a structure and a common interface, and it allows you to create clever multilateral relationships across all of your databases. The Knowledge Graph is a virtual data layer that sits on top of your existing databases or data sets, allowing you to link all of your data at scale, whether structured or unstructured.
Characteristics of Knowledge Graph
Knowledge graphs combine characteristics of several data management paradigms:
- Database, where the data can be explored via structured queries.
- Graph, where they can be analyzed as any other network data structure;
- Knowledge base, where they bear formal semantics, which can be used to interpret the data and infer new facts.
Applications of Knowledge Graph Technology
Many companies are already utilizing Knowledge Graph technology to remain ahead of the competition. And graph databases and knowledge graphs have been used in a variety of industries, including banking, the auto industry, oil and gas, pharmaceutical and health, retail, publishing, and the media, among others.
Although these firms employ Knowledge Graphs for different purposes, the goal is the same to take enormous volumes of data from diverse data silos and add value to it so that it can be used (and eventually re-used) in a more meaningful and intelligent way. The major applications are explained below:
- Healthcare Industry
Knowledge graphs have proven to be an efficient tool for mapping linkages between the large diversity and structure of healthcare data in the healthcare services arena. Graphs have an unusual capacity to model latent links between data sources and capture connected data (i.e., entity relationships) that other data models miss. This makes it easier for doctors and service providers to find the data they need among a large number of variables and data sources.
One of the top 20 pharmaceutical corporations uses Enterprise Knowledge Graphs’ comprehensive capabilities to provide a single view of all their research operations.
2. IT & IT Services
Knowledge Graphs are used by large IT services company to link all unstructured (legal) papers to their structured data, allowing the company to intelligently evaluate hazards that are typically disguised in standard legal documents.
Knowledge Graphs are enabling multinational telecom business build chatbots based on semi-structured documents.
Several common industry Knowledge Graphs are used by a significant governmental body to give reliable health information to its citizens (such as MeSH and DBPedia etc.). The government’s health platform connects over 200 reliable medical information sources, enhancing search results and providing accurate answers.
Other case scenario includes
6. Public Security
7. Smart Constructions
8. General Manufacturing
9. Intelligent Risk Control
10. Smart Investment Advisor.
How does Knowledge Graph Work?
Although, Knowledge Graphs can take many different forms and be presented in a variety of ways, the following is a general architecture overview of how a Knowledge Graph works:
- Data Sources
Structured data in the form of relational databases, semi-structured data in the form of HTML, JSON, XML, and other formats, and unstructured data such as free text, and documents can all be utilized to build a knowledge graph.
2. Knowledge Extraction
The knowledge extraction procedure begins after the data has been received. This technique collects features from semi-structured and unstructured data, such as entities, relations, and attributes, from the incoming data. Natural Language Processing, text mining, and machine learning approaches are used to do this.
3. Knowledge Fusion
The goal of knowledge fusion is to bring together all of the information bases from various sources to create a comprehensive picture. Its specific objectives include entity alignment and ontology creation. Entity alignment is concerned with determining if different entities refer to the same real-world objects because it brings the data to a common ground, and data standardization is a key step in entity alignment.
Structure similarity functions, such as pattern recognition, are used to achieve collective alignment. All of this work results in the production of an ontology, which is then supplemented with a taxonomy, hierarchical structures, metadata, and other elements to improve the knowledge graph’s quality.
4. Knowledge Graph Storage, Retrieval and Visual Representation
A knowledge graph is saved in a NoSQL database, which can be either an RDF (resource description framework) or a graph database. Graph databases hold nodes, edges, and attributes of graphs, while RDF encodes knowledge graphs, the traditional query language for accessing large-scale knowledge graphs is SPARQL. Applications for browsing are used for a lot of knowledge graph visualization, and it’s still one of the most investigated subjects in this industry.
Application of Knowledge Graph to EpiK Protocol
EpiK Protocol will build a decentralized KG using blockchain technology to expand the horizons of today’s AI technology, tapping on the decentralized storage technology, uniquely designed Token Economy which ensures fair incentives, Decentralized Autonomous Organization (DAO ) to ensure trusted governance, and Decentralized Financial Technology (DeFi ) for reliable financial capabilities. Thus, creating a trusted, multi-party collaboration platform where all trusted contributors are rewarded fairly.
About EpiK Protocol
EpiK Protocol is a decentralized knowledge graph data sharing network based on the blockchain. Anyone can contribute their information to EpiK’s open source knowledge graph base, which allows users to act as both providers and consumers in the network.
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Medium: EpiK Protocol