![]() “My definition is more around its purpose and driver of adoption than its tech composition. Karinna Nobbs (Co-CEO of The Dematerialised) “The Metaverse: a persistent, live digital universe that affords individuals a sense of agency, social presence, and shared spatial awareness, along with the ability to participate in an extensive virtual economy with profound societal impact.” Piers Kicks (Investment Team at BITKRAFT Ventures) It is the connective tissue between humanity that we have always literally lucid dreamed of but until recently haven’t had the infrastructure to make it real.” “Well, the ideal definition is “full interactive reality” layered across every segment of our lives. “A nebulous, digitally mixed reality with both non-fungible and infinite items and personas not bound by conventional physics and limitations.”Įmma-Jane MacKinnon-Lee (CEO & Founder of Digitalax) Luke Shabro (Futurist & Deputy Director of the Mad Scientist Initiative - Army Futures Command) And if that sounds ludicrously naive and optimistic about it - I am and I’m not sorry!” “I hope it will be like the Oasis from ReadyPlayerOne where in the end it’s owned by young people who care more about community than profit and use it for the good of the real and virtual world. “My general description: The Metaverse crosses the physical/digital divide between actual and virtual realities.”Įsther O’Callaghan OBE (Cofounder Hundo.careers) “I think the Metaverse is the all-encompassing space in which all digital experience sits the observable digital universe made up of millions of digital galaxies”Įric Redmond (Global Director, Technology Innovation, Nike) In NIPS, 2004.Claire Kimber (Group Innovation Director at Posterscope) Learning with local and global consistency. Context sensitive synonym discovery for web search queries. Mining broad latent query aspects from search sessions. Targeted disambiguation of ad-hoc, homogeneous sets of named entities. Mining the web for synonyms: Pmi-ir versus lsa on toefl. Entity linking with a knowledge base: Issues, techniques, and solutions. Clustering query refinements by user intent. Heterogeneous graph-based intent learning with queries, web pages and wikipedia concepts. Joint unsupervised coreference resolution with markov logic. Web-scale distributional similarity and entity set expansion. Learning latent semantic relations from clickthrough data for query suggestion. Identifying synonyms among distributionally similar words. Mining entity attribute synonyms via compact clustering. Semantic frame-based document representation for comparable corpora. Grias: an entity-relation graph based framework for discovering entity aliases. Graph regularized transductive classification on heterogeneous information networks. Mining query subtopics from search log data. Large-scale learning of word relatedness with constraints. Entity resolution: theory, practice & open challenges. Entity extraction, linking, classification, and tagging for social media: a wikipedia-based approach. Scalable topical phrase mining from text corpora. Kert: Automatic extraction and ranking of topical keyphrases from content-representative document titles. Robust query rewriting using anchor data. Entity synonyms for structured web search. Exploiting web search to generate synonyms for entities. A framework for robust discovery of entity synonyms. ![]() Swoosh: a generic approach to entity resolution. Effective query formulation with multiple information sources. Using cooccurrence statistics and the web to discover synonyms in a technical language. ![]() Behavior-driven clustering of queries into topics. Experiments on several real-life domains demonstrate the effectiveness of our proposed method. We cast the synonym discovery problem into a graph-based ranking problem and demonstrate the existence of a closed-form optimal solution for outputting entity synonym scores. A general, heterogeneous graph-based data model which encodes our problem insights is designed by capturing three key concepts (synonym candidate, web page and keyword) and different types of interactions between them. Unlike existing query log-based methods, we delve deeper to explore sub-queries, and exploit tailed synonyms and tailed web pages for harvesting more synonyms. In this work, we propose adopting a "structured" view of each entity by considering not only its string name, but also other important structured attributes. Previous works often take a "literal" view of the entity, i.e., its string name. With the increasing use of entities in serving people's daily information needs, recognizing synonyms-different ways people refer to the same entity-has become a crucial task for many entity-leveraging applications.
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