Freebase was a large collaborative knowledge base consisting of data composed mainly by its community members. It was an online collection of structured data harvested from many sources, including individual, user-submitted wiki contributions. Freebase aimed to create a global resource that allowed people (and machines) to access common information more effectively. It was developed by the American software company Metaweb and run publicly beginning in March 2007. Metaweb was acquired by Google in a private sale announced on 16 July 2010. Google's Knowledge Graph is powered in part by Freebase.
During its existence, Freebase data was available for commercial and non-commercial use under a Creative Commons Attribution License, and an open API, RDF endpoint, and a database dump is provided for programmers.
On 16 December 2014, Google announced that it would shut down Freebase over the succeeding six months and help with the move of the data from Freebase to Wikidata.
On 16 December 2015, Google officially announced the Knowledge Graph API, which is meant to be a replacement to the Freebase API. Freebase.com was officially shut down on 2 May 2016.
Both Graphd and MQL, the graph database and JSON-based query language developed by Metaweb for Freebase, are open-sourced by Google under the Apache 2.0 license, and are available on GitHub. Graphd is open-sourced on September 8, 2018. MQL is open-sourced on August 4, 2020.
On 3 March 2007 Metaweb announced Freebase, describing it as "an open shared database of the world's knowledge", and "a massive, collaboratively edited database of cross-linked data". Often understood as a database model using Wikipedia-turned-database or entity-relationship model, Freebase provided an interface that allowed non-programmers to fill in structured data, or metadata, of general information and to categorize or connect data items in meaningful, semantic ways.
Described by Tim O'Reilly upon the launch, "Freebase is the bridge between the bottom up vision of Web 2.
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In knowledge representation and reasoning, knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities - objects, events, situations or abstract concepts - while also encoding the semantics underlying the used terminology. Since the development of the Semantic Web, knowledge graphs are often associated with linked open data projects, focusing on the connections between concepts and entities.
YAGO (Yet Another Great Ontology) is an open source knowledge base developed at the Max Planck Institute for Informatics in Saarbrücken. It is automatically extracted from Wikipedia and other sources. As of 2019, YAGO3 has knowledge of more than 10 million entities and contains more than 120 million facts about these entities. The information in YAGO is extracted from Wikipedia (e.g., categories, redirects, infoboxes), WordNet (e.g., synsets, hyponymy), and GeoNames. The accuracy of YAGO was manually evaluated to be above 95% on a sample of facts.
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets. In 2008, Tim Berners-Lee described DBpedia as one of the most famous parts of the decentralized Linked Data effort.
The discovery of web documents about certain topics is an important task for web-based applications including web document retrieval, opinion mining and knowledge extraction. In this paper, we propose an agent-based focused crawling framework able to retri ...
IEEE2012
The current information landscape is characterised by a vast amount of relatively semantically homogeneous, when observed in isolation, data silos that are, however, drastically semantically fragmented when considered as a whole. Within each data silo, inf ...