{"id":29598,"date":"2022-06-15T06:00:00","date_gmt":"2022-06-15T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=29598"},"modified":"2022-06-16T09:16:43","modified_gmt":"2022-06-16T16:16:43","slug":"the-worlds-most-ambitious-knowledge-graph","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/06\/15\/the-worlds-most-ambitious-knowledge-graph\/","title":{"rendered":"The World\u2019s Most Ambitious Knowledge Graph\u00a0"},"content":{"rendered":"\n

Knowledge graphs have become one of the foremost expressions of Artificial Intelligence today. Almost everyone\u2014from vendors to organizations, analysts to regulators\u2014relies on these applications at some point to compile, harmonize, and scrutinize specialized business information.<\/p>\n\n\n\n

Use cases range the gamut of data management requisites from data quality solutions to internal applications of employees\u2019 aptitudes<\/a>. The ubiquity of knowledge graphs isn\u2019t just because of their unmatched relationship discernment, innate reasoning capabilities, or standardization of divers data sources.  According to expert.ai<\/a> CTO Marco Varone, it\u2019s for something much simpler and, perhaps, more universal.<\/p>\n\n\n\n

\u201cAny knowledge is added value for any use case,\u201d Varone observed. \u201cIt\u2019s always better to have more knowledge than less. If you\u2019ve got more than you need you can discard it, but if you don\u2019t have knowledge you can\u2019t create it out of thin air.\u201d<\/p>\n\n\n\n

Of all the knowledge graphs wrought, the most extensive, heavily populated, and nuanced are those pertaining to Natural Language Understanding (NLU) which, arguably, is the most arduous of AI tasks. As Varone pointed out, the context of computer vision deployments is relatively the same across the globe. Language, however, has far more distinctions pertaining to accents, regions, dialects, use cases, and other cardinal points of differentiation of what words mean in varying contexts.<\/p>\n\n\n\n

Devising a knowledge graph then, to facilitate language understanding for each of these intricacies\u2014in an assortment of languages\u2014is surely one of the most ambitious undertakings of these applications ever completed. Success is critical for accurate computer understanding of language for enterprise AI.<\/p>\n\n\n\n

\u201cFor simple use cases for language understanding, you can do well without knowledge graphs,\u201d Varone commented. \u201cBut as soon as you move from super basic ones to the really complex, knowledge is something you need.\u201d<\/p>\n\n\n\n

Subject Area Models<\/strong><\/p>\n\n\n\n

The underpinnings of any true knowledge graph will always be the subject area models (sometimes termed ontologies) upon which enterprise knowledge is based. This fact is particularly prominent for a knowledge graph focused on NLU<\/a>, which expert.AI built across a sundry of language and domains that, Varone estimated, required hundreds of \u201cman years of work\u201d. Consequently, \u201cit\u2019s not separate knowledge graphs: one for chemistry, one for sports, one for finance,\u201d Varone indicated. \u201cAs much as possible, we put everything into one knowledge graph.\u201d<\/p>\n\n\n\n

The means of doing so lies in a binary approach in which the resulting graph was, conceptually, split into two parts. The first is comprised of the concepts represented by language itself, which Varone characterized as \u201clanguage independent.\u201d Since language is the very substrate of knowledge, the exhaustive nature of such subject area models is readily apparent. The second focuses on the linguistic application of these concepts, which is naturally codified according to respective languages.<\/p>\n\n\n\n

Vocabularies, Taxonomies<\/strong><\/p>\n\n\n\n

Whereas ontologies are necessary for representing knowledge in a unified manner that Varone denoted \u201cminimizes the entropy\u201d not uncommon to knowledge graphs, taxonomies are necessary for the specific applications of those concepts. \u201cThat\u2019s where you have the vocabulary, the terminology,\u201d Varone explained. Naturally, there are different taxonomies for different languages and distinctions, like business units.<\/p>\n\n\n\n

Taxonomies also include synonyms and hierarchies of definitions for the varying terms that relate to concepts in ontologies. In this respect, this second aspect of a NLU knowledge graph \u201cis the thesaurus or vocabulary on top of the language independent part,\u201d Varone revealed.<\/p>\n\n\n\n

Modeling Language<\/strong><\/p>\n\n\n\n

Although it may conceptually help to think of the knowledge graph according to these two halves, the difference between taxonomies and ontologies isn\u2019t always clear. Some ontologies involve taxonomies; arguably, all taxonomies are founded in some way in the concepts of these subject area models. \u201cOntologies can be very complex,\u201d Varone mentioned. \u201cThey can have any type of relation, attributes, and number of nodes.\u201d<\/p>\n\n\n\n

Frameworks like Cyc<\/a>\u2014an expert system specializing in language, for which there is now an open source variety\u2014were integral to fine-tuning the complexities of expert.ai<\/a>\u2019s knowledge graph so that it was applicable to the real world. Thus, for the first part of Varone\u2019s knowledge graph, the subject area model component, \u201cthe secret was making a pragmatic compromise between things that are too generic, to abstract for real users and Cyc,\u201d Varone disclosed.<\/p>\n\n\n\n

Knowledge Enrichment <\/strong><\/p>\n\n\n\n

The final aspect of constructing an exhaustive knowledge graph for NLU across traditional barriers like languages and domains was to actually put the knowledge into the graph. Varone articulated a variegated method that began with knowledge engineers manually inputting rules and definitions before eventually involving statistical AI models<\/a>. It may be surprising that for what Varone called this \u201cknowledge enrichment\u201d facet of building the graph, the CTO eschewed neural network approaches popularized by BERT and transformers.<\/p>\n\n\n\n

\u201cThis is absolutely not the way to do it because, with the deep learning and neural network approach, what you can do is only create a sort of implicit knowledge,\u201d Varone specified. \u201cIt is a black box. But our knowledge graph is explicit. So, all of its information is explicit so you can see, modify, or link and enrich it.\u201d<\/p>\n\n\n\n

The explicit nature of the knowledge contained in knowledge graphs is attributed to their self-declarative nature, in which anyone can see what terms mean, look up their definitions, and understand them in relation to the subject area model. As such, the self-populating element of the knowledge enrichment phase involves what Varone termed \u201cproprietary\u201d algorithms, in addition to traditional machine learning approaches utilizing both supervised and unsupervised learning.<\/p>\n\n\n\n

Untold Advantages <\/strong><\/p>\n\n\n\n

The boons of devising a NLU knowledge graph with the methodology Varone advocated are manifold. It effectively gives one a single knowledge graph that\u2019s applicable to almost any use for language understanding. Moreover, it\u2019s highly extensible and adapts to the particular lexicon of any organization or its business units. \u201cIf you need to add new concepts, first you have to add them in the ontology part, and then you add the word in the particular language,\u201d Varone clarified.<\/p>\n\n\n\n

This AI application is also primed for translations between languages, as well as the myriad use cases throughout the world in which information must be exposed to customers in both English and a country\u2019s native language\u2014such as French, for example. Finally, this knowledge graph supports the burgeoning array of enterprise NLU use cases, including everything from Cognitive Process Automation to intelligent chatbots and text analytics.<\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance and analytics.<\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, editorial consultant Jelani Harper highlights how knowledge graphs have become one of the foremost expressions of Artificial Intelligence today. Almost everyone\u2014from vendors to organizations, analysts to regulators\u2014relies on these applications at some point to compile, harmonize, and scrutinize specialized business information.<\/p>\n","protected":false},"author":10513,"featured_media":22407,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[65,115,87,180,56,1,70],"tags":[437,106,1154,96],"acf":[],"yoast_head":"\nThe World\u2019s Most Ambitious Knowledge Graph\u00a0 - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/06\/15\/the-worlds-most-ambitious-knowledge-graph\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The World\u2019s Most Ambitious Knowledge Graph\u00a0 - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, editorial consultant Jelani Harper highlights how knowledge graphs have become one of the foremost expressions of Artificial Intelligence today. 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