{"id":84124,"date":"2022-01-14T10:12:33","date_gmt":"2022-01-14T10:12:33","guid":{"rendered":"https:\/\/websitedesigns.com.au\/elankanew\/?p=84124"},"modified":"2022-01-14T10:13:12","modified_gmt":"2022-01-14T10:13:12","slug":"knowledge-graphs-a-graph-for-linking-entities-by-aditya-abeysinghe","status":"publish","type":"post","link":"https:\/\/websitedesigns.com.au\/elankanew\/knowledge-graphs-a-graph-for-linking-entities-by-aditya-abeysinghe\/","title":{"rendered":"Knowledge Graphs: A graph for linking entities? By Aditya Abeysinghe"},"content":{"rendered":"<h1 style=\"text-align: center;\"><span style=\"color: #008080; font-size: 24px;\"><strong>Knowledge Graphs: A graph for linking entities?<\/strong><\/span><\/h1>\n<h1 style=\"text-align: center;\"><span style=\"color: #008080; font-size: 24px;\"><strong>By Aditya Abeysinghe<\/strong><\/span><\/h1>\n<p><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-84125\" src=\"https:\/\/websitedesigns.com.au\/elankanew\/wp-content\/uploads\/2022\/01\/Knowledge-Graphs-A-graph-for-linking-entities.jpg\" alt=\"Knowledge Graphs: A graph for linking entities? By Aditya Abeysinghe\" width=\"275\" height=\"183\" \/><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\"><img loading=\"lazy\" loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-75845 alignleft\" src=\"https:\/\/websitedesigns.com.au\/elankanew\/wp-content\/uploads\/2021\/10\/Aditya-Abeysinghe160.jpg\" alt=\"Aditya-Abeysinghe\" width=\"160\" height=\"141\" \/>The use of graphs for retrieving relationships between data entities has caused a massive growth of a new method for search of similar data. Graph databases are used from simple search engine results of searched text to complex social media and big data analytics. A graph with its connections enables faster, efficient data processing while ensuring less need to be context-based data links. While knowledge graphs are not to be thought as same as knowledge stores, their usage has certainly changed how data is handled in current querying methods.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #800000;\"><strong>What is a knowledge graph?<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\">A knowledge graph, referred to as a graph hereafter, is a graph which connects real-world entities. These entities can be of different types: people, places, weather, etc. These entities are stored in a graph database and a graph represents connections between these entities. In a graph, there are three main nodes: nodes, edges and labels. For example, a node would be a person and another node would be a restaurant. An edge would connect these two nodes: the person and the restaurant. The edge would display the relationship: \u201clikes\u201d, that is \u201cthe person\u201d \u201clikes\u201d \u201cthe restaurant\u201d. Knowledge graphs are not simple relationships between nodes. A large collection of connections between nodes enable data to be linked and searched.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #800000;\"><strong>Why are knowledge graphs used?<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\">A knowledge graph is a combination of a database, a graph, and a knowledge base. As with a database, data can be retrieved using a knowledge graph using structured queries. Unlike a structured or unstructured database, a graph database has data linked to each other. Therefore, complex joins between data as those used by structured or unstructured databases are not needed. Therefore, graph databases offer a flexible design in contrast to structured databases. While structured databases offer high performance for database transactions such as reads, writes, and updates, they often do not offer high performance on data analytics. As data are connected in graph databases, they are useful for complex data analytics required by large data stores.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #800000;\"><strong>Key Characteristics<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\">A graph database is composed of data from different sources. For example, Google\u2019s Knowledge graph extracts data from databases of people, geographical places, businesses etc. They need to be connected when they need to be indexed for search. Connections between these entities are formed using the schema of a graph.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\">The schema of a graph is formed using ontologies. An ontology represents the connection between the user of a graph and its developers. Similar to HTML (Hyper Text Markup Language) which defines the language for website design, ontologies define how the graph is designed. Usually, an object in a graph is represented by a class: a Person, City, Food etc. Classes could be connected in a top-to-bottom hierarchy by defining which classes are super classes or sub classes of others. For example, a City is a Place which is a sub class of a Province which is a sub class of a Country. Relationships between these objects are specified by Types. For example, a Person likes a Singer, a Singer sings Jazz and a Song belongs to an Album. An object can be of different categories. For example, a Movie can be a \u201cRussian Movie\u201d, \u201c5 star Movie\u201d, and \u201cFree Movie to be viewed\u201d.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 16px; color: #000000;\"><strong>Image Courtesy: <\/strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" href=\"https:\/\/medium.com\/\"><strong>https:\/\/medium.com\/<\/strong><\/a><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Knowledge Graphs: A graph for linking entities? By Aditya Abeysinghe &nbsp; The use of graphs for retrieving relationships between data entities has caused a massive growth of a new method for search of similar data. Graph databases are used from simple search engine results of searched text to complex social media and big data analytics. [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":84125,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"aside","meta":{"footnotes":""},"categories":[33978,20],"tags":[41057,1214,41056,41059,41058],"class_list":{"0":"post-84124","1":"post","2":"type-post","3":"status-publish","4":"format-aside","5":"has-post-thumbnail","7":"category-aditya-abeysinghe","8":"category-articles","9":"tag-data-analytics","10":"tag-google","11":"tag-graph-databases","12":"tag-html","13":"tag-knowledge-graph","14":"post_format-post-format-aside"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.7.1 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Knowledge Graphs: A graph for linking entities? 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