Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.

Surprisingly, Amazon Alexa doesn't have a good way to search for Alexa skills by voice. You can't say that you want to play word games, need a skill to check airport security wait times, or feel like meditating. Alexa doesn't know what to tell you. Amazon released its own "Skill Finder" skill last year, but it's a bare-bones experience that can only read off the most popular apps in certain vague categories, or list the top or newest Alexa skills. You can't ask it for a skill with a specific use case or functionality.

Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at query time. In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities. mp-LSH supports L2, cosine, and inner product dissimilarities, and their corresponding weighted sums, where the weights can be adjusted at query time. It also allows us to modify the importance of pre-defined groups of features. Thus, mp-LSH enables us, for example, to retrieve similar items to a query with the user preference taken into account, to find a similar material to a query with some properties (stability, utility, etc.) optimized, and to turn on or off a part of multi-modal information (brightness, color, audio, text, etc.) in image/video retrieval. We theoretically and empirically analyze the performance of three variants of mp-LSH, and demonstrate their usefulness on real-world data sets.

Many AI applications rely on knowledge encoded in a locigal knowledge base (KB). The most essential benefit of such logical KBs is the opportunity to perform automatic reasoning which however requires a KB to meet some minimal quality criteria such as consistency. Without adequate tool assistance, the task of resolving such violated quality criteria in a KB can be extremely hard, especially when the problematic KB is large and complex. To this end, interactive KB debuggers have been introduced which ask a user queries whether certain statements must or must not hold in the intended domain. The given answers help to gradually restrict the search space for KB repairs. Existing interactive debuggers often rely on a pool-based strategy for query computation. A pool of query candidates is precomputed, from which the best candidate according to some query quality criterion is selected to be shown to the user. This often leads to the generation of many unnecessary query candidates and thus to a high number of expensive calls to logical reasoning services. We tackle this issue by an in-depth mathematical analysis of diverse real-valued active learning query selection measures in order to determine qualitative criteria that make a query favorable. These criteria are the key to devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation for interactive KB debugging while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability for the user, of the generated query that existing methods cannot realize. Further, we study different relations between active learning measures. The obtained picture gives a hint about which measures are more favorable in which situation or which measures always lead to the same outcomes, based on given types of queries.

Ukrainian authorities earlier this month blocked access to Yandex as well as to several major Russian social media websites. President Petro Poroshenko said the move was made in response to Russia's annexation of the Crimean peninsula and continuing interference in eastern Ukraine.

Francisco is the Founder and CEO of cortical.io, Francisco's medical background in genetics combined with over two decade's of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on the way the human neocortex processes information. Francisco founded Matrixware Information Services, a company that developed the first standardized database of patents. Francisco also initiated the Information Retrieval Facility, a non-profit research institute, with the goal to bridge the gap between science and industry in the information retrieval domain. Let me introduce you to Francisco Webber, Founder and CEO of cortical.io.

In this new episode of Real Smart Marketing, we've asked this big question to 4 of our favorite influencers. If you're like me, the first thing that comes to mind when you hear AI might be this: But as I've come to realize, what we're talking about is a lot less creepy. AI is changing the face of SEO, but not like that. We're talking about algorithms that enable machines to make connections and "learn" to process data and apply its learning in future tasks. Basically, improvements in artificial intelligence like deep learning and natural language processing mean that search engines are becoming smarter and more human-friendly.

Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.

An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of long text. These tools can help writers skim through a lot of articles and find relevant topics to write about. "Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people," Cuofano says. "The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding)," Cuofano says.

After Ava's betrayal of Caleb the actual purpose of the sessions became clear. Upon first meeting Caleb, Ava saw more than just another human; she saw a way out. While she seemed to show genuine affection for Caleb, especially during the power shutoff scenes, she was feigning affection in order to manipulate Caleb into releasing her. Like HAL and other robot characters discussed in my previous commentaries, Ava clearly has a mind of her own and uses her superior capabilities to outsmart the humans. However, Ava proves to have a level of pure hatred for humans in general. This is something that she was not programmed with but developed over her imprisonment in Nathan's basement.