Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues. However, because of the huge search space of potential enrichment, predictions from KG completion (KGC) methods suffer from low precision, making them unreliable for real-world catalogues. Moreover, candidate facts for enrichment have varied relevance to users. While making correct predictions for incomplete triplets in KGs has been the main focus of KGC method, the relevance of when to apply such predictions has been neglected. Motivated by the product search use case, we address the angle of generating relevant completion for a catalogue using user search behaviour and the users property association with a product. In this paper, we present our intuition for identifying enrichable data points and use general-purpose KGs to show-case the performance benefits. In particular, we extract entity-predicate pairs from user queries, which are more likely to be correct and relevant, and use these pairs to guide the prediction of KGC methods. We assess our method on two popular encyclopedia KGs, DBPedia and YAGO 4. Our results from both automatic and human evaluations show that query guidance can significantly improve the correctness and relevance of prediction.

General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from Web sources, and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete in the first place, and to which degree. In this survey we discuss how knowledge about completeness, recall, and negation in KBs can be expressed, extracted, and inferred. We cover (i) the logical foundations of knowledge representation and querying under partial closed-world semantics; (ii) the estimation of this information via statistical patterns; (iii) the extraction of information about recall from KBs and text; (iv) the identification of interesting negative statements; and (v) relaxed notions of relative recall. This survey is targeted at two types of audiences: (1) practitioners who are interested in tracking KB quality, focusing extraction efforts, and building quality-aware downstream applications; and (2) data management, knowledge base and semantic web researchers who wish to understand the state of the art of knowledge bases beyond the open-world assumption. Consequently, our survey presents both fundamental methodologies and their working, and gives practice-oriented recommendations on how to choose between different approaches for a problem at hand.

The goal of Question Answering over Knowledge Graphs (KGQA) is to find answers for natural language questions over a knowledge graph. Recent KGQA approaches adopt a neural machine translation (NMT) approach, where the natural language question is translated into a structured query language. However, NMT suffers from the out-of-vocabulary problem, where terms in a question may not have been seen during training, impeding their translation. This issue is particularly problematic for the millions of entities that large knowledge graphs describe. We rather propose a KGQA approach that delegates the processing of entities to entity linking (EL) systems. NMT is then used to create a query template with placeholders that are filled by entities identified in an EL phase. Slot filling is used to decide which entity fills which placeholder. Experiments for QA over Wikidata show that our approach outperforms pure NMT: while there remains a strong dependence on having seen similar query templates during training, errors relating to entities are greatly reduced.

Bhagat Singh, Marie Curie, Charles Darwin, and other historical figures were momentarily'brought back to life' via Deep Nostalgia – an AI tool released by the genealogy website, MyHeritage. Kind of surreal to take a photo of the singularly inspiring Bhagat Singh -- a revolutionary voice in 1920s India, who was hung by the British in 1931, at the age of 24 -- run it through the Heritage AI algorithm, and see him reanimated. When Ken Burns meets Deep Fake: MyHeritage is offering a tool dubbed #DeepNostalgia, meant to animate old family pictures. Holy Darwin this #deepfake is so scary, Mr. Darwin!!#DeepNostalgia pic.twitter.com/vxWP5LnO9L Deep Nostalgia created quite a furore of late, with animated pictures of historical figures running rife in social media.

Artificial intelligence is turning old pictures of people into short, animated clips that show them moving and blinking. The feature, called Deep Nostalgia, comes from genealogy company MyHeritage. It uses machine learning to create facial expressions and movements that look super realistic, Tom's Guide reported Tuesday. In a blog post, MyHeritage shared social media posts from users who were thrilled to see their loved ones who'd passed come to life, if only for a few moments. The clips show the people in black-and-white or faded photos tilting their heads and looking around.

Artificial intelligence (AI) can now transform photos of people into short, highly realistic animations, much like the moving pictures in the newspapers and posters of Harry Potter's magical world. In these AI-animated clips, faces that were once frozen in time blink, turn their heads and even smile, their movements wavering between astonishingly lifelike and deeply unsettling (and yes, downright creepy). Genealogy website MyHeritage introduced the animation engine on Feb. 25. Developed by technology company D-ID and known as Deep Nostalgia, it enables users to animate photos via the MyHeritage website, representatives said in a blog post. D-ID designed custom algorithms that recreate the naturalistic movement of human faces digitally, applying those subtle movements to photographs and modifying facial expressions that move as human faces normally do, according to the D-ID website.