Google is reportedly going to launch a censored version of its search engine in China. The tech giant has been secretly planning to launch the product since last year, as part of a project referred to inside the company as'Dragonfly,' according to The Intercept, which was given internal documents from a whistleblower. It comes as Google has tried and failed to make inroads in the Chinese market over the past several years. Google has been planning to launch the product since last year, as part of a project referred to inside the company as'Dragonfly.' While China is home to the world's largest number of internet users, a 2015 report by US think tank Freedom House found that the country had the most restrictive online use policies of 65 nations it studied, ranking below Iran and Syria.

SAN FRANCISCO -- Google may launch a censored version of its search engine in China in a move that would largely reverse its 2010 decision to withdraw from the country, The Intercept reported. The news outlet says Google has been working on a project called Dragonfly since last year and has demonstrated a version of the censored search engine to Chinese officials. The Android app could launch in six to nine months as part of a joint venture with an unnamed partner company, likely in China. It would automatically block websites blacklisted by Beijing as well as search terms on human rights, democracy, religion, and peaceful protest, The Intercept said. "We provide a number of mobile apps in China, such as Google Translate and Files Go, help Chinese developers, and have made significant investments in Chinese companies like JD.com. But we don't comment on speculation about future plans," Google said in a statement.

China's relationship with Google is fractious at best, but it's no secret that the search giant wants to make inroads in what is a largely untapped market. However, its latest alleged plan could send tech's political sphere into a tailspin. According to The Intercept, Google is working on a censored version of its search engine for the country -- one which will blacklist websites and search terms about human rights, democracy, religion and protest. The project, codenamed Dragonfly, has been in the works since spring 2017 and could be ready to launch within the next six to nine months, according to unnamed sources familiar with the plan. Apparently the project was given the go-ahead during a meeting between Google's CEO Sundar Pichai and a top Chinese government official, although it still needs final approval from China.

Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation. NTRODUCTION With rapid development of the Word Wide Web and e-societies, information retrieval (IR) has many challenges in exploiting those rich and huge information resources. Whereas, the keyword based IR has many limitations in finding suitable documents for user's queries. Semantic search improves search precision and recall by understanding user's intent and the contextual meaning of terms in documents and queries.

It was supposed to happen on August 29th, 1997. That was the date that Skynet, an artificial intelligence, would become self-aware and declares war on humanity. Of course, Skynet only exists in the Terminator movie franchise universe. It's an overused, yet still popular, Hollywood trope: an artificial intelligence (AI) becomes self-aware and seeks only to destroy its human creators. From Blade Runner to Terminator to Maximum Overdrive to the more recent Westworld, our fiction is filled with humans fighting AIs who have morphed into murderous, would-be despots.

Duplicates in data management are common and problematic. In this work, we present a translation of Datalog under bag semantics into a well-behaved extension of Datalog (the so-called warded Datalog+-) under set semantics. From a theoretical point of view, this allows us to reason on bag semantics by making use of the well-established theoretical foundations of set semantics. From a practical point of view, this allows us to handle the bag semantics of Datalog by powerful, existing query engines for the required extension of Datalog. Moreover, this translation has the potential for further extensions -- above all to capture the bag semantics of the semantic web query language SPARQL.

As human beings, we use our voices for conversation. When we interact with voice interfaces, therefore, our natural instinct is to apply the same rules that we would to a human conversation. We expect to be understood, but more than this, we expect the entity we're conversing with to remember the history of our conversation and understand the context of any following remarks. For some time, major search companies like Google and Bing have worked to teach their search engines to understand queries in natural language. Natural language search queries are queries that sound natural spoken aloud, such as, "How high is the Empire State building?" They often begin with question words ("When…?" "How…?" "Why…?"), contain stop words ("a", "the", "of", "for") and full sentences.

For many people, Google is the internet. It now dominates almost all aspects of our online lives, from how we search for information, to how we navigate from one place to another. But the route Google has taken to achieve this supremacy has been ruthless, illegal and occasionally unconventional. For 85 per cent of smartphone users that have Google's Android mobile operating system, the slew of apps that come pre-installed on the device are often owned by Google. This includes the popular Chrome web browser and Google search engine, meaning users are forced to download competing apps through the Google Play Store if they want to use them.

As mentioned, advertisers need to successfully bid for their keywords in order to have an effective digital marketing strategy. With the vast number of keywords available, it can be incredibly difficult to choose the most strategic ones for your campaign. With AI's Smart Bidding, its machine learning processes can optimize your strategy and use performance goals like target cost per acquisition (CPA), enhanced cost per click (CPC) and maximize conversions to dwindle down the number of keywords into the most efficient and cost-effective ones. Artificial intelligence would be able to use industry performance data, historical data, and user behavior patterns to make good use of the results of their keyword searches. Not everyone who uses your keywords are ready for conversion.

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching.