What is Knowledge Graph Technology?
A knowledge graph is a knowledge base that uses a graph-structured dataframe or structure to collaborate closely. Interconnected descriptions of elements with free-form semantics, such as objects, events, circumstances, or abstract concepts, are typically stored in knowledge networks.
Knowledge Graphs are used in a variety of ways. Most people, however, are still unfamiliar with Knowledge Graphs and the underlying graph databases, and because of the technology’s seamless integration into our lives, Most people are not even aware of how reliant humans are on it or, and how they have come to expect a certain level of quality and standard from it.
Knowledge Graph Technology has different characteristics ranging from 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.
Knowledge Graph Technology Applications
In recent years, knowledge graphs (KGs) have become the base of many information systems which require access to structured knowledge. The concept of Semantic Web can be traced back to Berners-Lee’s research in 2001. In his work, Berners-Lee suggested that the technical standards such as Uniform Resource Identifier (URI), Resource Description Framework (RDF) and Web Ontology Language (OWL) should be promoted and developed. Some researches contributed to promote the graph-based representation of knowledge by employing the RDF standard in early days. Nodes in such graphs represent entities and they are connected by edges which represent relations. The sets of relations can be organized in a schema or ontology which defines their correlativity and restrictions of their usage.
The concept of Linked Data came out in 2009. It is proposed to link different datasets to each other in the Semantic Web to make them be treated as one large, global knowledge graph. Until 2014, approximately 1,000 datasets are linked with each other in the Linked Open Data cloud, most links between them connect identical entities.
To stay ahead of the competition, several organizations are already using Knowledge Graph technology. Banking, the auto sector, oil and gas, pharmaceutical and health, retail, publishing, and the media are just a few of the businesses that have used graph databases and knowledge graphs.
Although these companies use Knowledge Graphs for different objectives, the goal is the same; to add value to massive amounts of data from various data silos so that it may be used (and eventually re-used) in a more meaningful and intelligent manner. The following are the most important applications:
- IT Services:
Large IT services companies employ Knowledge Graphs to connect all unstructured (legal) papers to their data structure, allowing them to effectively enhance capacity that are often hidden in ordinary legal paperwork.
- Medical Field:
In the healthcare services arena, knowledge graphs have proven to be an effective tool for mapping links between the huge diversity and structure of healthcare data. Graphs offer a unique ability to model latent linkages between data sources and collect related data (i.e., entity relationships) that other data models fail to capture. This makes it easier for doctors and service providers to find the information they need among many variables and data sources.
- Govermental Bodies:
A prominent governmental organization uses several common industry Knowledge Graphs to provide credible health information to its citizens (such as MeSH and DBPedia etc.). The government’s health platform connects over 200 reputable medical information sources to improve search results and provide correct answers.
- Deep Learning Based:
With the rapid development of deep learning in the field of natural language processing, many researches started to improve the performance of traditional methods by using deep learning method and achieved good results. Multi-column convolutional neural networks (MCCNNs) was used for information retrieving without relying on hand-crafted features and rules. They employ a score layer to rank candidate answers according to the representations of questions and candidate answers. An end-to-end neural network model was provided with cross-attention mechanism which considers various candidate answer aspects to represent the questions and their corresponding scores.
Traditional approaches for semantic parsing are largely decoupled from the knowledge base. Inspired by information retrieval method and embedding method, they reduce semantic parsing to query graph generation and formulate it as a staged search problem to make full use of the knowledge in knowledge bases. They also apply a deep convolutional neural network (CNN) model to leverage the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem. An attention based bidirectional long short-term memory (BiLSTM) was provided to learn the representations of the questions when using embedding approach. The experimental results show that their approach is effective and has a better ability of expressing the proper information of questions.
EpiK Protocol Knowledge Graph Collaboration
EpiK Protocol will create a decentralized KG based on blockchain technology to broaden the horizons of today’s AI technology by leveraging decentralized 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. As a result, a trusted, multi-party cooperation platform is created, with all trusted contributors being adequately compensated.
About EpiK Protocol
EpiK Protocol is a blockchain-based decentralized knowledge graph data sharing network. Anyone can add their knowledge to EpiK’s open source knowledge graph database, allowing users to function as both providers and consumers in the network.
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