"A semantic network or net is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. What is common to all semantic networks is a declarative graphic representation that can be used either to represent knowledge or to support automated systems for reasoning about knowledge. Some versions are highly informal, but other versions are formally defined systems of logic. ...The oldest known semantic network was drawn in the 3rd century AD by the Greek philosopher Porphyry in his commentary on Aristotle's categories."
– from John F. Sowa, Semantic Networks, revised and extended version of article originally written for the Encyclopedia of Artificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992.
The word2vec method based on skip-gram with negative sampling (Mikolov et al., 2013)  was published in 2013 and had a large impact on the field, mainly through its accompanying software package, which enabled efficient training of dense word representations and a straightforward integration into downstream models. In some respects, we have come far since then: Word embeddings have established themselves as an integral part of Natural Language Processing (NLP) models. In other aspects, we might as well be in 2013 as we have not found ways to pre-train word embeddings that have managed to supersede the original word2vec. This post will focus on the deficiencies of word embeddings and how recent approaches have tried to resolve them. If not otherwise stated, this post discusses pre-trained word embeddings, i.e. word representations that have been learned on a large corpus using word2vec and its variants.
Enterprise Knowledge Graph vendors are working hard to find their place in the heart of businesses, helping them do more with and get more out of their mountains of data. Recently, for example, Stardog has adopted its leading Knowledge Graph platform to be "FIBO-aware," mapping to the Financial Industry Business Ontology (FIBO) semantic standards out-of-the-box. GraphPath launched what it says is the first Knowledge-Graph-as-a-Service (KGaaS) platform. And Maana, with its Knowledge Graph-centered Knowledge Platform, has been talking up its partnerships with clients like Shell to drive digital transformation efforts. As part of these efforts, work is underway to make it easier for businesses to adopt these solutions – for experts like data engineers who will manage the graphs, of course, but also for the business users who will consume data from them via different applications that developers create.
Financial news giant Thomson Reuters has released its Knowledge Graph Feed, a way of instantly visualising the connections between lots of data sources, which it describes as "the first financial social network". The Knowledge Graph system is an open source, standardised data modelling system composed of Permanent Identifiers (PermID) which connect some two billion relationships. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. Geoffrey Horrell, director, Product Incubation Financial and Risk, Thomson Reuters, explained: "What we are delivering is like a social network but it's the first financial social network. So you can ask, what are the strategic relationships around the companies and people that you do business with; who are all the officers and directors, who are their suppliers, competitors, associates, affiliates.
Financial news giant Thomson Reuters has released its Knowledge Graph Feed, a way of instantly visualising the connections between lots of data sources, which it describes as "the first financial social network". The Knowledge Graph system is an open source, standardised data modelling system composed of Permanent Identifiers (PermID) which connect some two billion relationships. Geoffrey Horrell, director, Product Incubation Financial and Risk, Thomson Reuters, explained: "What we are delivering is like a social network but it's the first financial social network. So you can ask, what are the strategic relationships around the companies and people that you do business with; who are all the officers and directors, who are their suppliers, competitors, associates, affiliates. "People have talked about graphs but none of the content providers have really published all their data and all their taxonomies and definitions in this graph format before.
Cognitive applications have become constant companions at our places of work. We expect smart systems to reduce repetitive workloads and support us in uncovering new Knowledge. As a result, data scientists and software engineers are applying various machine learning algorithms to finetune results and increase processing capabilities. At the same time, critics are ever more loudly calling for more transparency about how these cognitive applications actually function. Companies are also advised to not to manage their AI-driven application environment solely on technical grounds.
The network, through iterated adjustment of the elements of the vector based on errors detected on comparison with the text corpora, produces the values in continuous space that best reflect the contextual data given. Most dictionaries will offer a direct or indirect connection through "king" to "ruler" or "sovereign" and "male" and through "queen" to "ruler" or "sovereign" and "female," as: These definitions2 show gender can be "factored out," and in common usage the gender aspect of sovereigns is notable. As we understand the high degree of contextual dependency of word meanings in a language, any representation of word meaning to a significant degree will reflect context, where context is its interassociation with other words. The word vectors produced by the method of training on a huge natural text dataset, in which words are given distributed vector representations refined through associations present in the input context, reflect the cross-referential semantic compositionality of a dictionary.
To show the word in action, Merriam-Webster included two example sentences, including one that takes a shot at the folks who prefer Apple's computers and mobile devices over the alternatives. "Apple's debuted a battery case for the juice-sucking iPhone--an ungainly lumpy case the sheeple will happily shell out $99 for," the sentence reads. According to Merriam-Webster's history for the word, sheeple was first used in 1945--more than 30 years before Steve Jobs and Steve Wozniak founded Apple and 62 years before the company would introduce the device that would apparently help herd the sheeple. The case referenced in the sentence is the Smart Battery Case Apple introduced in 2015 for the iPhone 6 and 6s.