Knowledge Graph (KG) Database
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A Knowledge Graph (KG) Database is a semantic network that is a knowledge base.
- AKA: Formal Semantic Network.
- Context:
- It can (typically) be composed of KG Concepts and KG Edges.
- It can (often) be represented as a Graph Database.
- It can range from being a Structured KG (e.g. semi-structured KG) to being an Embedded KG.
- It can range from being a Small Knowledge Graph to being a Medium-Sized Knowledge Graph to being a Large Knowledge Graph.
- It can be produced by a KG Construction Task.
- It can be associated to a Embedded Knowledge Graph.
- …
- Example(s):
- a Freebase Knowledge Graph Database.
- a yago Database.
- a DBpedia KB.
- a NELL KB.
- a ConceptNet KG.
- a FrameNet KG.
- a Biomedical KG, such as: BioKG.
- an Open Research Knowledge Graph (ORKG).
- a Proprietary KG, such as: LinkedIn's KG, and Google's KG (or Google Knowledge Vault).
- …
- Counter-Example(s):
- See: Lightweight Ontology, Semantic Web, Linked Data.
References
2023
- (Pan, Luo et al., 2023) ⇒ Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. (2023). “Unifying Large Language Models and Knowledge Graphs: A Roadmap.” In: arXiv preprint arXiv:2306.08302. doi:10.48550/arXiv.2306.08302
- QUOTE: Knowledge graphs (KGs) store structured knowledge as a collection of triples KG = {(h, r, t) ⊆ E × R × E}, where E and R respectively denote the set of entities and relations. Existing knowledge graphs (KGs) can be classified into four groups based on the stored information: 1) encyclopedic KGs, 2) commonsense KGs, 3) domain-specific KGs, and 4) multimodal KGs. We illustrate the examples of KGs of different categories in Fig. 5.
2022
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/knowledge_graph Retrieved:2022-10-7.
- In knowledge representation and reasoning, knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entitiesobjects, events, situations or abstract conceptswhile also encoding the semantics underlying the used terminology. Since the development of the Semantic Web, knowledge graphs are often associated with linked open data projects, focusing on the connections between concepts and entities. They are also prominently associated with and used by search engines such as Google, Bing, and Yahoo; knowledge-engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook.
2020
- (Yu et al., 2020) ⇒ Jay Yu, Kevin McCluskey, and Saikat Mukherjee. (2020). “Tax Knowledge Graph for a Smarter and More Personalized TurboTax.” arXiv preprint arXiv:2009.06103. doi::10.48550/arXiv.2009.06103.
- ABSTRACT: Most knowledge graph use cases are data-centric, focusing on representing data entities and their semantic relationships. There are no published success stories to represent large-scale complicated business logic with knowledge graph technologies. In this paper, we will share our innovative and practical approach to representing complicated U.S. and Canadian income tax compliance logic (calculations and rules) via a large-scale knowledge graph. We will cover how the Tax Knowledge Graph is constructed and automated, how it is used to calculate tax refunds, reasoned to find missing info, and navigated to explain the calculated results. The Tax Knowledge Graph has helped transform Intuit's flagship TurboTax product into a smart and personalized experience, accelerating and automating the tax preparation process while instilling confidence for millions of customers.
2017
- (Wang et al., 2017) ⇒ Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. (2017). “Knowledge Graph Embedding: A Survey of Approaches and Applications.” IEEE Transactions on Knowledge and Data Engineering 29, no. 12
- ABSTRACT: Knowledge graph (KG) embedding is to embed components of a KG including entities and relations into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. It can benefit a variety of downstream tasks such as KG completion and relation extraction, and hence has quickly gained massive attention. ...
2015
- (Nickel et al., 2015) ⇒ Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. (2015). “A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction.” In: arXiv:1503.00759 Journal.
- QUOTE: Recently, a large number of knowledge graphs have been created, including YAGO [4], DBpedia [5], NELL [6], Freebase [ 7], and the Google Knowledge Graph (8). As we discuss in Section II, these graphs contain millions of nodes and billions of edges.
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Knowledge_Graph Retrieved:2014-8-24.
- The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources. Knowledge Graph display was added to Google's search engine in 2012, starting in the United States, having been announced on May 16, 2012. ...