{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-semantic-knowledge-graph-a-compact-auto","title":"The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain","arxiv_id":"1609.00464","date":"2016-09-02","proceeding":null,"authors":["Trey Grainger","Khalifeh Aljadda","Mohammed Korayem","Andries Smith"],"abstract":"This paper describes a new kind of knowledge representation and mining system\nwhich we are calling the Semantic Knowledge Graph. At its heart, the Semantic\nKnowledge Graph leverages an inverted index, along with a complementary\nuninverted index, to represent nodes (terms) and edges (the documents within\nintersecting postings lists for multiple terms/nodes). This provides a layer of\nindirection between each pair of nodes and their corresponding edge, enabling\nedges to materialize dynamically from underlying corpus statistics. As a\nresult, any combination of nodes can have edges to any other nodes materialize\nand be scored to reveal latent relationships between the nodes. This provides\nnumerous benefits: the knowledge graph can be built automatically from a\nreal-world corpus of data, new nodes - along with their combined edges - can be\ninstantly materialized from any arbitrary combination of preexisting nodes\n(using set operations), and a full model of the semantic relationships between\nall entities within a domain can be represented and dynamically traversed using\na highly compact representation of the graph. Such a system has widespread\napplications in areas as diverse as knowledge modeling and reasoning, natural\nlanguage processing, anomaly detection, data cleansing, semantic search,\nanalytics, data classification, root cause analysis, and recommendations\nsystems. The main contribution of this paper is the introduction of a novel\nsystem - the Semantic Knowledge Graph - which is able to dynamically discover\nand score interesting relationships between any arbitrary combination of\nentities (words, phrases, or extracted concepts) through dynamically\nmaterializing nodes and edges from a compact graphical representation built\nautomatically from a corpus of data representative of a knowledge domain.","url_abs":"http://arxiv.org/abs/1609.00464v2","url_pdf":"http://arxiv.org/pdf/1609.00464v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-semantic-knowledge-graph-a-compact-auto","repo_url":"https://github.com/careerbuilder/semantic-knowledge-graph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-semantic-knowledge-graph-a-compact-auto","repo_url":"https://github.com/giridharm0505/SKG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-semantic-knowledge-graph-a-compact-auto","repo_url":"https://github.com/jzwerling/SemanticKnowledgeGraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-semantic-knowledge-graph-a-compact-auto","repo_url":"https://github.com/jzwerling/semantic-knowledge-graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-semantic-knowledge-graph-a-compact-auto","repo_url":"https://github.com/shalder/knowledge_graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}