"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 next time you search for and tap on an image on Google, you may see some helpful information related to what's on your screen. The company is now more deeply integrating its Knowledge Graph with pictures that it finds online. Say you're paging through photos of famous buildings as in the GIF above, you'll see a new element of the interface that highlights people, places or things related to the current picture. You can then tap on these to find out more information about them. As usual, you'll also see prompts for related searches. If you've ever searched for something and seen a panel to the side of the main interface that displays some facts related to your query, then you've seen the Knowledge Graph in action.
The number of studies about COVID-19 has risen exponentially from the start of the pandemic, from around 20,000 in early March to over 30,000 as of late June. In an effort to help clinicians digest the vast amount of biomedical knowledge in the literature, researchers affiliated with Columbia, Brandeis, Darpa, UCLA, and UIUC developed a framework -- COVID-KG -- that draws on papers to answer natural language questions about drug purposing and more. The sheer volume of COVID-19 research makes it difficult to sort the wheat from the chaff. Some false information has been promoted on social media and in publication venues like journals. And many results about the virus from different labs and sources are redundant, complementary, or would appear to conflict.
Google has been accused of a conspiracy and a cover-up over a disappearing image of Winston Churchill – but the affair appears to have been both more complicated and innocent than it first appeared. Outcry was prompted among some specific people on social media over the weekend when it emerged that searching for Winston Churchill no longer showed an image of the former prime minister, and instead just text responses to the query. The search company was attacked by people including culture secretary Oliver Dowden, who expressed his "concern" that the image had been removed for sinister reasons. It disappeared amid ongoing debate about the place of statues in public life, racial inequality, and Churchill's legacy, leading some to suggest the decision was a political move. But Google said it was in fact the result of a bug that occurred when Google tried to change rather than remove the image.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An email from a Missouri woman has prompted Merriam-Webster to update its definition of "racism" to include the systemic aspects that have contributed to discrimination, according to a report. Kennedy Mitchum, 22, of Florissant, told KMOV-TV that she was inspired to email the dictionary publisher after getting into arguments with others about the definition of racism. Merriam-Webster defines racism as "a belief that race is the primary determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race."
One of the most significant developments about the current resurgence of statistical Artificial Intelligence is the emphasis it places on knowledge graphs. These repositories have paralleled the contemporary pervasiveness of machine learning for numerous reasons, from their aptitude for preparing training datasets for this technology to pairing it with AI's knowledge base for consummate AI. Consequently, graph technologies are becoming fairly ubiquitous in a broadening array of solutions from Business Intelligence mechanisms to Digital Asset Management platforms. With tools like GraphQL gaining credence across the data landscape as well, it's not surprising many consider knowledge graphs one of the core technologies shaping modern AI deployments. As such, it's imperative to understand that all graphs are not equal; there are different types and functions ascribed to the various graphs vying for one another for the knowledge graph title.
A "knowledge graph" of the COVID-19 disease's many "strains" created by startup Graphen.ai. Each dot is a strain of COVID-19 or a family of COVID-19, the lines show how one strain descends from another. Everyone who has tried to figure out something has experienced the pleasure of seeing how things fit together -- connecting the dots, or following the money, as they say. One of the most fascinating technologies in vogue is a tool that can automate the process of making connections. Called a knowledge graph, it gathers up all the data trapped in various databases and in emails and digital repositories of all sorts, and draws conclusions about how they fit together.
A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends.
It's great to see more research and more datasets on complex QA and reasoning tasks. Whereas last year we saw a surge of multi-hop reading comprehension datasets (e.g., HotpotQA), this year at ICLR there is a strong line-up of papers dedicated to studying compositionality and logical complexity: and here KGs are of big help! Keysers et al study how to measure compositional generalization of QA models, i.e., when train and test splits operate on the same set of entities (broadly, logical atoms), but the composition of such atoms is different. The authors design a new large KGQA dataset CFQ (Compositional Freebase Questions) comprised of about 240K questions of 35K SPARQL query patterns. Several fascinating points 1) the questions are annotated with EL Description Logic (yes, those were the times around 2005 when DL meant mostly Description Logic, not Deep Learning); 2) as the dataset is positioned towards semantic parsing, all questions already have linked Freebase IDs (URIs), so you don't need to plug in your favourite Entity Linking system (like ElasticSearch).