Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Lithuania, a small Baltic state bordering Belarus and Russia's Kaliningrad, is adapting to new tensions between NATO and Moscow. A member of the Lithuanian Riflemen's Union takes part in a military exercise in central Lithuania [Nils Adler/Al Jazeera] Two members of the Lithuanian Riflemen's Union take part in a military exercise in central Lithuania [Nils Adler/Al Jazeera] On a nearby building is an illuminated decorative Z, a symbol used to show support for the Russian military's full-scale invasion of Ukraine, which began in February 2022.

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.

Microsoft arrived on the graph-database scene last month. Already on that scene are Neo4J, MarkLogic, Oracle, SAP and Teradata - among others. Driving Microsoft, like those before, is the desire to connect - to establish connections between things and derive some kind of gain. Those "things" could be people, "likes", online sales – tech firms are almost literally trying connecting the dots or as they like them to be called "nodes." The new thing is Artificial Intelligence and the Machine Learning that gets us there.