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Knowledge graph

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</style><div role="note" class="hatnote navigation-not-searchable">For other uses, see <a href="Knowledge_graph_(disambiguation)" class="mw-redirect mw-disambig" title="Knowledge graph (disambiguation)">Knowledge graph (disambiguation)</a>.</div>

In <a href="Knowledge_representation_and_reasoning" title="Knowledge representation and reasoning">knowledge representation and reasoning</a>, a knowledge graph is a <a href="Knowledge_base" title="Knowledge base">knowledge base</a> that uses a <a href="Graph_(discrete_mathematics)" title="Graph (discrete mathematics)">graph</a>-structured <a href="Data_model" title="Data model">data model</a> or <a href="Topology" title="Topology">topology</a> to represent and operate on <a href="Data" title="Data">data</a>. Knowledge graphs are often used to store interlinked descriptions of <a href="Named_entity" title="Named entity">entities</a> – objects, events, situations or abstract concepts – while also encoding the free-form <a href="Semantics" title="Semantics">semantics</a> or relationships underlying these entities.<a href="#cite_note-1">[1]</a><a href="#cite_note-2">[2]</a>

Since the development of the <a href="Semantic_Web" title="Semantic Web">Semantic Web</a>, knowledge graphs have often been associated with <a href="Linked_data" title="Linked data">linked open data</a> projects, focusing on the connections between <a href="Concept" title="Concept">concepts</a> and entities.<a href="#cite_note-Ref1-3">[3]</a><a href="#cite_note-4">[4]</a> They are also historically associated with and used by <a href="Search_engine" title="Search engine">search engines</a> such as <a href="Knowledge_Graph_(Google)" title="Knowledge Graph (Google)">Google</a>, <a href="Bing_(search_engine)" class="mw-redirect" title="Bing (search engine)">Bing</a>, <a href="Yext" title="Yext">Yext</a> and <a href="Yahoo" title="Yahoo">Yahoo</a>; <a href="Knowledge_engine" title="Knowledge engine">knowledge engines</a> and question-answering services such as <a href="WolframAlpha" title="WolframAlpha">WolframAlpha</a>, Apple's <a href="Siri" title="Siri">Siri</a>, and <a href="Amazon_Alexa" title="Amazon Alexa">Amazon Alexa</a>; and <a href="Social_network" title="Social network">social networks</a> such as <a href="LinkedIn" title="LinkedIn">LinkedIn</a> and <a href="Facebook" title="Facebook">Facebook</a>.

Recent developments in data science and machine learning, particularly in graph neural networks and representation learning and also in machine learning, have broadened the scope of knowledge graphs beyond their traditional use in search engines and recommender systems. They are increasingly used in scientific research, with notable applications in fields such as <a href="Genomics" title="Genomics">genomics</a>, <a href="Proteomics" title="Proteomics">proteomics</a>, and <a href="Systems_biology" title="Systems biology">systems biology</a>.<a href="#cite_note-5">[5]</a>

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

The term was coined as early as 1972 by the Austrian <a href="Linguistics" title="Linguistics">linguist</a> <a href="Edgar_W._Schneider" title="Edgar W. Schneider">Edgar W. Schneider</a>, in a discussion of how to build modular instructional systems for courses.<a href="#cite_note-6">[6]</a> In the late 1980s, the <a href="University_of_Groningen" title="University of Groningen">University of Groningen</a> and <a href="University_of_Twente" title="University of Twente">University of Twente</a> jointly began a project called Knowledge Graphs, focusing on the design of <a href="Semantic_network" title="Semantic network">semantic networks</a> with edges restricted to a limited set of relations, to facilitate <a href="Graph_algebra" title="Graph algebra">algebras on the graph</a>. In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.

Some early knowledge graphs were topic-specific. In 1985, <a href="Wordnet" class="mw-redirect" title="Wordnet">Wordnet</a> was founded, capturing semantic relationships between words and meanings – an application of this idea to language itself. In 2005, Marc Wirk founded <a href="Geonames" class="mw-redirect" title="Geonames">Geonames</a> to capture relationships between different geographic names and locales and associated entities. In 1998 Andrew Edmonds of Science in Finance Ltd in the UK created a system called ThinkBase that offered <a href="Fuzzy_logic" title="Fuzzy logic">fuzzy-logic</a> based reasoning in a graphical context.<a href="#cite_note-7">[7]</a> ThinkBase LLC<a href="#cite_note-8">[8]</a>

In 2007, both <a href="DBpedia" title="DBpedia">DBpedia</a> and <a href="Freebase_(database)" title="Freebase (database)">Freebase</a> were founded as graph-based knowledge <a href="Repository_(version_control)" title="Repository (version control)">repositories</a> for general-purpose knowledge. DBpedia focused exclusively on data extracted from Wikipedia, while Freebase also included a range of public datasets. Neither described themselves as a 'knowledge graph' but developed and described related concepts.

In 2012, Google introduced their <a href="Knowledge_Graph_(Google)" title="Knowledge Graph (Google)">Knowledge Graph</a>,<a href="#cite_note-Singhal-2012-9">[9]</a> building on DBpedia and Freebase among other sources. They later incorporated <a href="RDFa" title="RDFa">RDFa</a>, <a href="Microdata_(HTML)" title="Microdata (HTML)">Microdata</a>, <a href="JSON-LD" title="JSON-LD">JSON-LD</a> content extracted from indexed web pages, including the <a href="The_World_Factbook" title="The World Factbook">CIA World Factbook</a>, <a href="Wikidata" title="Wikidata">Wikidata</a>, and <a href="Wikipedia" title="Wikipedia">Wikipedia</a>.<a href="#cite_note-Singhal-2012-9">[9]</a><a href="#cite_note-10">[10]</a> Entity and relationship types associated with this knowledge graph have been further organized using terms from the <a href="Schema.org" title="Schema.org">schema.org</a><a href="#cite_note-McCusker-11">[11]</a> vocabulary. The Google Knowledge Graph became a successful complement to string-based search within Google, and its popularity online brought the term into more common use.<a href="#cite_note-McCusker-11">[11]</a>

Since then, several large multinationals have advertised their knowledge graphs use, further popularising the term. These include Facebook, LinkedIn, <a href="Airbnb" title="Airbnb">Airbnb</a>, <a href="Microsoft" title="Microsoft">Microsoft</a>, <a href="Amazon.com" class="mw-redirect" title="Amazon.com">Amazon</a>, <a href="Uber" title="Uber">Uber</a> and <a href="EBay" title="EBay">eBay</a>.<a href="#cite_note-12">[12]</a>

In 2019, <a href="Institute_of_Electrical_and_Electronics_Engineers" title="Institute of Electrical and Electronics Engineers">IEEE</a> combined its annual international conferences on "Big Knowledge" and "Data Mining and Intelligent Computing" into the International Conference on Knowledge Graph.<a href="#cite_note-13">[13]</a>

<div class="mw-heading mw-heading2"><h2 id="Definitions">Definitions</h2></div>

There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features:<a href="#cite_note-14">[14]</a>

<ul><li>Flexible relations among knowledge in topical domains: A knowledge graph (i) defines <a href="Abstract_class" class="mw-redirect" title="Abstract class">abstract classes</a> and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph, (iii) allows for potentially interrelating arbitrary entities with each other, and (iv) covers various topical domains.<a href="#cite_note-15">[15]</a></li>

<li>General structure: A network of entities, their semantic types, properties, and relationships.<a href="#cite_note-16">[16]</a><a href="#cite_note-17">[17]</a> To represent properties, categorical or numerical values are often used.</li>

<li>Supporting reasoning over inferred ontologies: A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.<a href="#cite_note-Ref1-3">[3]</a></li></ul>

There are, however, many knowledge graph representations for which some of these features are not relevant. For those knowledge graphs, this simpler definition may be more useful:

<ul><li>A digital structure that represents knowledge as concepts and the relationships between them (facts). A knowledge graph can include an ontology that allows both humans and machines to understand and reason about its contents.<a href="#cite_note-18">[18]</a><a href="#cite_note-19">[19]</a></li></ul>

<div class="mw-heading mw-heading3"><h3 id="Implementations">Implementations</h3></div>

In addition to the above examples, the term has been used to describe open knowledge projects such as <a href="YAGO_(database)" title="YAGO (database)">YAGO</a> and Wikidata; federations like the Linked Open Data cloud;<a href="#cite_note-20">[20]</a> a range of commercial search tools, including Yahoo's semantic search assistant Spark, Google's <a href="Knowledge_Graph_(Google)" title="Knowledge Graph (Google)">Knowledge Graph</a>, and Microsoft's Satori; and the LinkedIn and Facebook entity graphs.<a href="#cite_note-Ref1-3">[3]</a>

The term is also used in the context of <a href="Note-taking_software" class="mw-redirect" title="Note-taking software">note-taking software</a> applications that allow a user to build a <a href="Personal_knowledge_graph" class="mw-redirect" title="Personal knowledge graph">personal knowledge graph</a>.<a href="#cite_note-21">[21]</a>

The popularization of knowledge graphs and their accompanying methods have led to the development of graph databases such as Neo4j,<a href="#cite_note-22">[22]</a> GraphDB<a href="#cite_note-23">[23]</a> and AgensGraph.<a href="#cite_note-24">[24]</a> These graph databases allow users to easily store data as entities and their interrelationships, and facilitate operations such as data reasoning, node embedding, and ontology development on knowledge bases.

In contrast, virtual knowledge graphs do not store information in specialized databases. They rely on an underlying relational database or data lake to answer queries on the graph. Such a virtual knowledge graph system must be properly configured in order to answer the queries correctly. This specific configuration is done through a set of mappings that define the relationship between the elements of the data source and the structure and ontology of the virtual knowledge graph.<a href="#cite_note-25">[25]</a>

<div class="mw-heading mw-heading2"><h2 id="Using_a_knowledge_graph_for_reasoning_over_data">Using a knowledge graph for reasoning over data</h2></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Ontology_(information_science)" title="Ontology (information science)">Ontology (information science)</a></div>

A knowledge graph formally represents semantics by describing entities and their relationships.<a href="#cite_note-26">[26]</a> Knowledge graphs may make use of <a href="Ontology_(information_science)" title="Ontology (information science)">ontologies</a> as a schema layer. By doing this, they allow <a href="Inference" title="Inference">logical inference</a> for retrieving <a href="Implicit_knowledge" class="mw-redirect" title="Implicit knowledge">implicit knowledge</a> rather than only allowing queries requesting explicit knowledge.<a href="#cite_note-27">[27]</a>

In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like <a href="Word_embedding" title="Word embedding">word embeddings</a>. This can complement other estimates of conceptual similarity.<a href="#cite_note-28">[28]</a><a href="#cite_note-29">[29]</a>

Models for generating useful knowledge graph embeddings are commonly the domain of graph neural networks (GNNs).<a href="#cite_note-30">[30]</a> GNNs are deep learning architectures that comprise edges and nodes, which correspond well to the entities and relationships of knowledge graphs. The topology and data structures afforded by GNNs provides a convenient domain for semi-supervised learning, wherein the network is trained to predict the value of a node embedding (provided a group of adjacent nodes and their edges) or edge (provided a pair of nodes). These tasks serve as fundamental abstractions for more complex tasks such as knowledge graph reasoning and alignment.<a href="#cite_note-31">[31]</a>

<div class="mw-heading mw-heading3"><h3 id="Entity_alignment">Entity alignment</h3></div>

As new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as knowledge graph entity alignment, and is an active area of research.<a href="#cite_note-32">[32]</a>

Strategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs. Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes corresponds to the same entity.<a href="#cite_note-33">[33]</a>

The recent successes of large language models (LLMs), in particular their effectiveness at producing syntactically meaningful embeddings, has spurred the use of LLMs in the task of entity alignment.<a href="#cite_note-34">[34]</a>

As the amount of data stored in knowledge graphs grows, developing dependable methods for knowledge graph entity alignment becomes an increasingly crucial step in the integration and cohesion of knowledge graph data.

<ul><li><a href="Concept_map" title="Concept map">Concept map</a> – Diagram showing relationships among concepts</li>

<li><a href="Formal_semantics_(natural_language)" title="Formal semantics (natural language)">Formal semantics (natural language)</a> – Formal study of linguistic meaning</li>

<li><a href="Graph_database" title="Graph database">Graph database</a> – Database using graph structures for queries</li>

<li><a href="Knowledge_base" title="Knowledge base">Knowledge base</a> – Information repository with multiple applications</li>

<li><a href="Knowledge_graph_embedding" title="Knowledge graph embedding">Knowledge graph embedding</a> – Dimensionality reduction of graph-based semantic data objects [machine learning task]</li>

<li><a href="Logical_graph" class="mw-redirect" title="Logical graph">Logical graph</a> – Type of diagrammatic notation for propositional logicPages displaying short descriptions of redirect targets</li>

<li><a href="Semantic_integration" title="Semantic integration">Semantic integration</a> – Interrelating info from diverse sources</li>

<li><a href="Semantic_technology" title="Semantic technology">Semantic technology</a> – Technology to help machines understand data</li>

<li><a href="Topic_map" title="Topic map">Topic map</a> – Knowledge organization system</li>

<li><a href="Vadalog" title="Vadalog">Vadalog</a> – Type of Knowledge Graph Management System</li>

<li><a href="Wikibase" title="Wikibase">Wikibase</a>- Mediawiki Software extensions for creating knowledge bases</li>

<li><a href="Wikidata" title="Wikidata">Wikidata</a> - Free Knowledge Database Project</li>

<li><a href="YAGO_(database)" title="YAGO (database)">YAGO (database)</a> – Open-source information repository</li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

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<ul><li><cite id="CITEREFWill_Douglas_Heaven2020" class="citation news cs1">Will Douglas Heaven (4 September 2020). <a rel="nofollow" class="external text" href="https://www.technologyreview.com/2020/09/04/1008156/knowledge-graph-ai-reads-web-machine-learning-natural-language-processing/">"This know-it-all AI learns by reading the entire web nonstop"</a>. MIT Technology Review. Retrieved 5 September 2020. <q>Diffbot is building the biggest-ever knowledge graph by applying image recognition and natural-language processing to billions of web pages.</q></cite></li></ul>

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