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Implementation of graph database for OpenCog artificial general intelligence framework using Neo4j
Irawan H.a, Prihatmanto A.S.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Jawa Barat, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.We create a graph backing store[1l for OpenCog[2, 3, 26, 27, 28] using Neo4j graph database[4]. The GraphBackingStore API extends the current BackingStore C++ API. It can take special queries that map naturally into Cypher queries and simple manipulations of Neo4j graph traversals. The Neo4j node-relationship structures and custom indices are optimized for AtomSpace[5] usage and performance, with considerations for integration with Linked Data[17]. This makes it easier to integrate projects which use common Linked Data foundation, such as Lumen Robot Friend[29]. Data access is using Protocol Buffers-based[6] protocol over ZeroMQ[7] messaging transport. The initial work for Neo4j Graph Backing Store for OpenCog was implemented during Google Summer of Code 2015[8]. All source code is open source and available at https://github.com/ceefour/opencog-neo4j, https://github.com/opencog/atomspace, andhttps://github.com/opencog/opencog.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Graph database,Linked datum,Neo4j,OpenCog,Protocol buffers,YAGO,ZeroMQ[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]artificial intelligence,graph database,Knowledge representation,Linked Data,Neo4j,OpenCog,protocol buffers,semantic web,YAGO,ZeroMQ[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IDM.2015.7516355[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]