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.

IBM Almaden Research Center, 650 Harry Road, San Jose, California 95136 Abstract We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database of compoundtarget associations augmented with molecular ontological labels. It also contains two computational tools for prediction of new associations. We describe two drug-repurposing systems: one, Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based on an information retrieval strategy, and another, based on nonnegative matrix factorization together with compound similarity, that was inspired by recommender systems. We report the performance of both tools on a drug-repurposing task. 1 Introduction Drug repurposing is an efficient strategy for drug discovery, where new targets or activities are found for known drugs [1-5]. Drug repurposing requires the efficient representation of existing information about the activity of chemical compounds as drugs, and the development of algorithms that leverage such information and propose new indications.

With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.

Yandex spokesman Ilya Grabovsky said Thursday that some Internet users contacted the company Wednesday to say that its public search engine was yielding what looked like personal Google files. Grabovsky said the company has alerted Google.

As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools enabled by machine learning. With this in mind, a team of researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at Berkeley Lab's Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments. A beta version of the platform has been made available to Foundry researchers.

NPR-Marist poll finds that almost half of online shoppers go to Amazon first when they look for an item. Other search engines know what customers look for but Amazon knows what they ultimately buy.

Note: By submitting this form, you agree to Third Door Media's terms. In this week's Search In Pictures, here are the latest images culled from the web, showing what people eat at the search engine companies, how they play, who they meet, where they speak, what toys they have and more. Note: By submitting this form, you agree to Third Door Media's terms. Have something to say about this article?

French startup Doctrine is raising a $11.6 million funding round (€10 million) from existing investors Otium Venture and Xavier Niel. Doctrine is building a search engine for court decisions and other legal texts. This is a key tool if you're a lawyer or you're working in the legal industry in general. There are now a thousand companies using the service. It currently costs around €129 per user per month.