What is Knowledge Graph Technology, and how does it work?
A knowledge graph is a knowledge base that collaborates closely using a graph-structured dataframe or structure. Knowledge networks are typically used to store interconnected descriptions of elements with free-form semantics, such as objects, events, circumstances, or abstract concepts.
Knowledge Graphs are useful in a number of situations. Most people, on the other hand, are still unfamiliar with Knowledge Graphs and the underlying graph databases, and because of the technology’s seamless integration into our lives, they are unaware of how reliant humans have become on it or how they have come to expect a certain level of quality and standard from it.
Knowledge Graph Technology has a variety of properties, including database, where data can be examined using structured queries, graph, where data can be analysed like any other network data structure, and knowledge base, where data can be interpreted and new facts inferred using formal semantics.
Applications of Knowledge Graph Technology
Knowledge graphs (KGs) have been the foundation of many information systems that require access to organised knowledge in recent years. Berners-research Lee’s in 2001 gave birth to the Semantic Web notion.
Berners-Lee advocated for the promotion and development of technical standards such as the Uniform Resource Identifier (URI), Resource Description Framework (RDF), and Web Ontology Language (OWL) in his work. Using the RDF standard in the early days, some researches helped to advance the graph-based representation of knowledge. In such graphs, nodes represent entities, and edges represent relationships between them.
A schema or ontology can be used to structure the sets of relations, defining their correlativity and usage limits.
In 2009, the concept of Linked Data was introduced. It is proposed that diverse datasets in the Semantic Web be linked together so that they can be viewed as one single, global knowledge graph. Until 2014, the Linked Open Data cloud had roughly 1,000 datasets linked to each other, with the majority of the links connecting identical entities.
Several firms are already utilising Knowledge Graph technology to remain ahead of the competition. Graph databases and knowledge graphs have been utilised in banking, the auto industry, oil and gas, pharmaceutical and health care, retail, publishing, and the media, to name a few industries.
Although these firms have different goals in mind when they employ Knowledge Graphs, the end goal is the same: to add value to large volumes of data from diverse data silos so that it may be used (and eventually re-used) in a more meaningful and intelligent way.
The most important applications are listed below.
Knowledge Graphs are used by large IT services firms to connect all unstructured (legal) files to their data structure, allowing them to improve capacity that is often hidden in typical legal documentation.
To give credible health information to its citizens, a renowned governmental agency employs numerous common industry Knowledge Graphs (such as MeSH and DBPedia etc.). To improve search results and deliver reliable answers, the government’s health platform connects over 200 credible medical information sources.
Knowledge graphs have shown to be an efficient tool for mapping linkages between the vast diversity and structure of healthcare data in the healthcare industry. Other data models fail to capture latent links between data sources and collect related data (i.e., entity relationships), but graphs do.
This makes it easier for doctors and service providers to identify the data they require among the many variables and data sources.
Deep Learning Based
With the rapid development of deep learning in the field of natural language processing, numerous studies have begun to use deep learning to improve the performance of classical methods, with promising results.
Without depending on hand-crafted features and rules, multi-column convolutional neural networks (MCCNNs) were utilised to retrieve information.
They rank candidate responses using a score layer based on question and candidate answer representations. An end-to-end neural network model that assesses multiple potential answer features to represent the questions and their associated scores incorporated a cross-attention mechanism.
The knowledge base is considerably separated from traditional techniques to semantic parsing. They compress semantic parsing to query graph generation and present it as a staged search issue to make full use of the knowledge in knowledge bases, inspired by information retrieval and embedding methods.
They also use a deep convolutional neural network (CNN) model to exploit the knowledge base early on to trim the search space, making the semantic matching problem easier to solve.
When employing the embedding strategy, an attention-based bidirectional long short-term memory (BiLSTM) was used to learn the representations of the questions. The results of the experiments suggest that their method is effective and capable of expressing the correct information of inquiries.
Collaboration on the EpiK Protocol Knowledge Graph
By leveraging decentralised storage technology, a uniquely designed Token Economy that ensures fair rewards, a Decentralized Autonomous Organization (DAO ) for trusted governance, and Decentralized Financial Technology (DeFi ) for dependable financial capabilities, EpiK Protocol will create a decentralised KG based on blockchain technology to broaden the horizons of today’s AI technology.
As a result, a secure, multi-party collaboration platform is established, with all trusted contributors being fairly compensated.
About EpiK Protocol
EpiK Protocol is a decentralised knowledge graph data sharing network based on blockchain technology. EpiK’s open source knowledge graph database allows anybody to contribute their knowledge, allowing users to act as both providers and consumers in the network.