"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 publisher of Kojien, the nation's most authoritative dictionary, has drawn complaints from advocates for sexual minorities for incorrectly defining the term, "LGBT," in its latest edition released Friday. "Lesbian," "gay" and "bisexual" are terms used to describe sexual orientations, while "transgender" is used to "describe people whose gender identity does not match the sex or gender they were identified as having at birth." But the seventh edition of Kojien failed to separate the meaning of "lesbian," "gay" and "bisexual," from "transgender," defining the meaning of "LGBT" collectively as "people whose sexual orientations are different from the majority." Following its release, many LGBT advocates took to Twitter and Facebook to point out the mistake, urging the publisher to make a correction. Iwanami Shoten, the publisher, admitted the inaccuracy, saying the explanation of the term was "insufficient."
Click to learn more about author Thomas Frisendal. Losing your way is easy. Much of Data Modeling in the search, analytics and reporting spaces have been focused on the fabulous five W-words. We have been throwing technologies at this for quite some years now: Plain old Data Modeling, Semantics, "hyperindexes", ontologies, topic maps, Data Warehouses, Operational Data Stores, multidimensional OLAP, mapping-intensive ETL, key/value pairs, Big Data and now also Data Catalogs, Data Lakes, and Knowledge Graphs. Even alerts and exception reporting have been baked into reporting and analytics solutions for quite some time now.
The publisher of Kojien, the most authoritative dictionary in Japan, has been stuck between a rock and a hard place over its definition of Taiwan as a province of China, prompting a request for a correction from the self-ruled island. Since its first publication in 1955, the dictionary has become a household name. The media and other organizations often use it to get the final say on a word's meaning. The seventh edition is slated to be released next month. On Friday, Iwanami Shoten, the publisher, said Kojien's entry on Taiwan is in line with the 1972 Japan-China Joint Communique, in which Japan recognized the People's Republic of China as the sole legal government of China and "fully understands and respects" the PRC's stance that Taiwan is an inalienable part of its territory.
The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining. Data mining over Web domains that are unusual is an even harder problem. There are several factors that make a domain unusual. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief.
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.