Your machine learning models need training data to make sure that your algorithms are continually optimized. But not just any data – they need large, high-quality, human-annotated datasets. Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity. Appen's global, curated crowd allows you to collect the amount of quality data samples needed to optimize your algorithm, in your desired timeframe. Leverage the Appen Global platform to gain efficiencies in your data collection efforts and gather large volumes of high-quality datasets to quickly train your machine learning model.

Entity recommendation, providing search users with an improved experience via assisting them in finding related entities for a given query, has become an indispensable feature of today's search engines. Existing studies typically only consider the queries with explicit entities. They usually fail to handle complex queries that without entities, such as "what food is good for cold weather", because their models could not infer the underlying meaning of the input text. In this work, we believe that contexts convey valuable evidence that could facilitate the semantic modeling of queries, and take them into consideration for entity recommendation. In order to better model the semantics of queries and entities, we learn the representation of queries and entities jointly with attentive deep neural networks. We evaluate our approach using large-scale, real-world search logs from a widely used commercial Chinese search engine. Our system has been deployed in ShenMa Search Engine and you can fetch it in UC Browser of Alibaba. Results from online A/B test suggest that the impression efficiency of click-through rate increased by 5.1% and page view increased by 5.5%.

Google entering verticals and competing directly against publishers, advancements in machine learning and AI and zero-click searches are the trends most likely to affect SEO in the next three years, according to a SparkToro survey of over 1,500 SEOs. Trends that are here to stay? Respondents were presented with a list of choices and asked, "How much of an impact do you believe the following trends will have on SEO in the next 3 years?" Options were ranked on a zero-to-four scale; zero meaning "no impact" and four meaning "huge impact." The trend that professionals responded were least likely to affect SEO included outcomes from US Congressional and Department of Justice investigations, visual search advances and "content-nudging" products such as Google Discover.

Google's goal has always been to organize the world's information, and its first target was the commercial web. Now, it wants to do the same for the scientific community with a new search engine for datasets. The service, called Dataset Search, launches today, and will be a companion of sorts to Google Scholar, the company's popular search engine for academic studies and reports. Institutions that publish their data online, like universities and governments, will need to include metadata tags in their webpages that describe their data, including who created it, when it was published, how it was collected, and so on. This information will then be indexed by Dataset Search and combined with input from Google's Knowledge Graph.

We present improvements in maximum a-posteriori inference for Markov Logic, a widely used SRL formalism. Inferring the most probable world for Markov Logic is NP-hard in general. Several approaches, including Cutting Plane Aggregation (CPA), perform inference through translation to Integer Linear Programs. Aggregation exploits context-specific symmetries independently of evidence and reduces the size of the program. We illustrate much more symmetries occurring in long ground clauses that are ignored by CPA and can be exploited by higher-order aggregations. We propose Full-Constraint-Aggregation, a superior algorithm to CPA which exploits the ignored symmetries via a lifted translation method and some constraint relaxations. RDBMS and heuristic techniques are involved to improve the overall performance. We introduce Xeggora as an evolutionary extension of RockIt, the query engine that uses CPA. Xeggora evaluation on real-world benchmarks shows progress in efficiency compared to RockIt especially for models with long formulas.

Business firms who sell their products and services online, want to enhance the visibility of their website so that they can get top ranking in SERP(Search Engine Result Page; Google, Yahoo, Bing..). SEO is the process of optimizing the website to get organic or natural traffic and ranking of your targeted keywords. Ranking of the website is based on what the search engine considers most relevant to users. All search engines provide the best info for their users. So if you have a website and you want to increase the visibility of your website and want to see your website at the top rank of search engine's results, you will definitely need SEO software that helps find keywords you can rank for, links you can get and On-Page optimization guaranteed to increase rankings on search engines.

Google My Business is now providing insights to business owners about what their business is known for, according to customers. A new addition to the Google My Business insights section for cafes and restaurants offers data about subjective attributes, which are based on customers' opinions. We've launched subjective attributes to provide more information in your insights tab! Customers of restaurants and cafes can submit subjective attributes to help you and their fellow customers. When submitting a Google review, customers can add a variety of subjective attributes. If a particular attribute is submitted frequently enough, it will appear on the listing for others to see.

With the huge amount of information inflowing the World Wide Web every second, it becomes more difficult and more difficult to retrieve information from the Web. This explains why the existence of a search engine is as important as the existence of the web itself. Since the appearance of the web, there has been a fundamental talk in the web research communit y to develop the rapid, effective, and precise search engines. This paper will be chiefly discussing about the most common search engine nowadays which is Google. The mathematical theory behind the Google search engine is the PageRank algorithm, which was presented by Sergey Brin and Lawrence Page [1].

Welcome to the era of Big Data where data-driven insights have the power to transform your business. You're about to discover the solution: a powerful, innovative and adaptive platform power packed with every feature you need for Search, Discovery & Analytics of your data. We have named it 3RDi "Third Eye". It's the semantic search engine your enterprise needs to help you take action, boost revenues and cut costs! Powered by NLP and semantic search, it is designed for multidimensional information analysis and easy search relevancy management.

In this tutorial, you will learn how to build a scalable image hashing search engine using OpenCV, Python, and VP-Trees. Back in 2017, I wrote a tutorial on image hashing with OpenCV and Python (which is required reading for this tutorial). That guide showed you how to find identical/duplicate images in a given dataset. However, there was a scalability problem with that original tutorial -- namely that it did not scale! To find near-duplicate images, our original image hashing method would require us to perform a linear search, comparing the query hash to each individual image hash in our dataset. In a practical, real-world application that's far too slow -- we need to find a way to reduce that search to sub-linear time complexity. But how can we reduce search time so dramatically?