Yucong Duan, Lixu Shao, Gongzhu Hu · International Journal of Software Innovation · 2018
Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. The authors propose to clarify the expression of knowledge graph as a whole. They clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. The authors also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.
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Taufiqurrahman Taufiqurrahman, Kemas Rahmat Saleh Wiharja, Gia Septiana Wulandari · Bulletin of Social Informatics Theory and Application · 2024
Scholarly knowledge graph is a knowledge graph that is used to represent knowledge contained in scientific publication documents. The information we can find in a scientific publication document is as follows: author, institution, name of journal/conference, and research topic. A knowledge graph that has been built is usually still not perfect. Some incomplete information may be found. To add the missing information, we can use knowledge graph completion, which is a method for finding missing or incorrect relationships to improve the quality of a knowledge graph. Knowledge graph completion can be carried out on a scholarly knowledge graph by adding new entities and relationships to produce further information in the scholarly knowledge graph. The data added to the scholarly knowledge graph are only other papers of first author entity, the research field of first author entity, and a description of the conference/journal entity. The result shows that the scholarly knowledge graph was completed by adding 81% correct data for other papers of first author entity, 80.3% correct data for the research field of first author entity, and 53.9% correct data for the description of the conference/journal.
Cited by 2Open access
Weedon, Daniel B., Olsen, Daniel · VTechWorks (Virginia Tech) · 2021
The knowledge graph project is a two-component project: the first component is concerned with the back end Grakn.AI while the second component deals with the front end service registration. The goal of the project is to build a knowledge graph that represents how user information goals are connected to one another. The knowledge graph is connected to a workflow management system that allows developers to register their services and add them to the knowledge graph. \n \nA knowledge graph is a directed graph data model that stores interlinked entities. Storing data into the knowledge graph allows you to see how this data is connected with other entities in the graph as well as how they are connected. Through this we see the power of a fully fleshed-out knowledge graph. A user may wish to complete a task but has no knowledge about how to complete this task or the tools needed to do so. They can use the knowledge graph and query for this task and thus retrieve the workflow necessary to perform this task including the input files, output files, libraries, functions, and environments. \n \nDuring this project, research was conducted on both the back end and the front end. On the back end, our team researched how to search through the knowledge graph with Grakn. The front end searched for a suitable method to visualize the knowledge graph. As a result, the Grakn database is able to query the knowledge graph and a Python API is connected to Grakn to allow the front end to display an update to date version of the knowledge graph.
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Daniel B. Weedon, Daniel Olsen · VTechWorks (Virginia Tech) · 2021
The knowledge graph project is a two-component project: the first component is concerned with the back end Grakn.AI while the second component deals with the front end service registration. The goal of the project is to build a knowledge graph that represents how user information goals are connected to one another. The knowledge graph is connected to a workflow management system that allows developers to register their services and add them to the knowledge graph. A knowledge graph is a directed graph data model that stores interlinked entities. Storing data into the knowledge graph allows you to see how this data is connected with other entities in the graph as well as how they are connected. Through this we see the power of a fully fleshed-out knowledge graph. A user may wish to complete a task but has no knowledge about how to complete this task or the tools needed to do so. They can use the knowledge graph and query for this task and thus retrieve the workflow necessary to perform this task including the input files, output files, libraries, functions, and environments. During this project, research was conducted on both the back end and the front end. On the back end, our team researched how to search through the knowledge graph with Grakn. The front end searched for a suitable method to visualize the knowledge graph. As a result, the Grakn database is able to query the knowledge graph and a Python API is connected to Grakn to allow the front end to display an update to date version of the knowledge graph.
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Wikipedia · Zenodo (CERN European Organization for Nuclear Research) · 2018
Wikipedia article about the Knowlegde Graph (30/06/2018).
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BRIERE, Galadriel · Zenodo (CERN European Organization for Nuclear Research) · 2024
This Knowledge Graph is derived directly from the Shepherd Knowledge Graph (see https://zitniklab.hms.harvard.edu/projects/SHEPHERD/) , with updated node identifiers to include node type.
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Jie Zheng · 2025
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Wikipedia · DANS Data Station Physical and Technical Sciences · 2018
This Wikipedia entry describes the Knowledge Graph as a knowledge base by Google. It enhances the search engine's results by gathering information from a variety of sources. The entry consists of the history, description, a criticism, references and links to external sources.
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athreya001 · Zenodo (CERN European Organization for Nuclear Research) · 2026
Full Changelog: https://github.com/athreya001/knowledge-graph-explorer/compare/v0.1.0...v1.0.0
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patdmc · Zenodo (CERN European Organization for Nuclear Research) · 2026
No description provided.
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athreya001 · Zenodo (CERN European Organization for Nuclear Research) · 2026
Full Changelog: https://github.com/athreya001/knowledge-graph-explorer/compare/v0.1.0...v1.0.0
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patdmc · Zenodo (CERN European Organization for Nuclear Research) · 2026
No description provided.
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Alexandros Vassiliades, Spyridon, Sotiris et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
In the previous two decades, knowledge graphs have evolved, inspiring developers to build even more context related Knowledge Graphs. Because of this development, artificial intelligence applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this paper, we introduce the XR4DRAMA Knowledge Graph, which can serve as a representation of media planning information. The XR4DRAMA knowledge graph can specifically represent data about the following: (a) Observations and Events (for example, data information from photos and text messages); (b) Spatial and Temporal data, such as coordinates or labels of locations and timestamps; and (c) Tasks and Plans for media planning. In addition, we provide a mechanism that allows Points of Interest to be created or updated based on videos, photos, and text messages sent by users. For improved media coverage of a remote location, Points of Interest serve as markers to journalists.
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Vassiliades, Alexandros, Symeonidis, Spyridon, Diplaris, Sotiris et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
The evolution of Knowledge Graphs (KGs), during the last two decades, has encouraged developers to create more and more context related KGs. This advance is extremely important because Artificial Intelligence (AI) applications can access open domain specific information in a semantically rich, machine understandable format. In this paper, we present the XR4DRAMA KG which can represent information for disaster management. More specifically, the XR4DRAMA KG can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). Moreover, we offer a mechanism that can create or update Points-Of-Interest (POIs), based on a visual or textual messages received from users.
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Alexandros Vassiliades, Spyridon Symeonidis, Sotiris Diplaris et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
The evolution of Knowledge Graphs (KGs), during the last two decades, has encouraged developers to create more and more context related KGs. This advance is extremely important because Artificial Intelligence (AI) applications can access open domain specific information in a semantically rich, machine understandable format. In this paper, we present the XR4DRAMA KG which can represent information for disaster management. More specifically, the XR4DRAMA KG can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). Moreover, we offer a mechanism that can create or update Points-Of-Interest (POIs), based on a visual or textual messages received from users.
Cited by 0Open access
Vassiliades, Alexandros, , Spyridon, , Sotiris et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
In the previous two decades, knowledge graphs have evolved, inspiring developers to build even more context related Knowledge Graphs. Because of this development, artificial intelligence applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this paper, we introduce the XR4DRAMA Knowledge Graph, which can serve as a representation of media planning information. The XR4DRAMA knowledge graph can specifically represent data about the following: (a) Observations and Events (for example, data information from photos and text messages); (b) Spatial and Temporal data, such as coordinates or labels of locations and timestamps; and (c) Tasks and Plans for media planning. In addition, we provide a mechanism that allows Points of Interest to be created or updated based on videos, photos, and text messages sent by users. For improved media coverage of a remote location, Points of Interest serve as markers to journalists.
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Alexandros Vassiliades, Spyridon Symeonidis, Sotiris Diplaris et al. · 2023
Cited by 1Open access
Mikko Koho, Erkki Heino, Petri Leskinen et al. · Figshare · 2019
WarSampo Knowledge Graph includes harmonized data of different kinds concerning the Second World War in Finland, separated in different subgraphs representing events, actors, places, photographs, and other aspects and documentation of the war. The data covers the Winter War 1939-1940 against the Soviet attack, the Continuation War 1941-1944 where the occupied areas of the Winter War were temporarily regained, and the Lapland War 1944-1945, where the Finns pushed the German troops away from Lapland. To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal WarSampo, explained in more detail in the project page. Example SPARQL queries for the data: Events, photographs and articles that are situated in Vyborg Casualties of the 1st Division and its subunits in the time interval 13.2.-13.3.1940 by place and date WarSampo knowledge graph version history: 1.0.0, November 2015: Initial public release 1.1.0, November 2017: War cemeteries addition 2.0.0, May 2018: Backwards-incompatible URI changes 2.1.0, November 2019: Prisoners of war addition (to be published) Version 2.0.0 contains 13,838,765 triples. To combine the files into a single Turtle file on a Linux system: <pre><code class="language-bash">find . -mindepth 2 -name "*.ttl" | xargs cat >> warsampo.ttl</code></pre>
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Mikko Koho, Erkki Heino, Petri Leskinen et al. · Zenodo (CERN European Organization for Nuclear Research) · 2019
WarSampo Knowledge Graph includes harmonized data of different kinds concerning the Second World War in Finland, separated in different subgraphs representing events, actors, places, photographs, and other aspects and documentation of the war. The data covers the Winter War 1939-1940 against the Soviet attack, the Continuation War 1941-1944 where the occupied areas of the Winter War were temporarily regained, and the Lapland War 1944-1945, where the Finns pushed the German troops away from Lapland. To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal WarSampo, explained in more detail in the project page. Example SPARQL queries for the data: Events, photographs and articles that are situated in Vyborg Casualties of the 1st Division and its subunits in the time interval 13.2.-13.3.1940 by place and date WarSampo knowledge graph version history: 1.0.0, November 2015: Initial public release 1.1.0, November 2017: War cemeteries addition 2.0.0, May 2018: Backwards-incompatible URI changes 2.0.1, November 2019: Updated schema and VoiD descriptions 2.1.0, November 2019: Prisoners of war addition Version 2.1.0 contains 14,322,426 triples. To combine the files into a single Turtle file on a Linux system: <pre><code class="language-bash">find . -mindepth 2 -name "*.ttl" | xargs cat >> warsampo.ttl</code></pre>
Cited by 2Open access
Paul Groth · Zenodo (CERN European Organization for Nuclear Research) · 2020
Knowledge graphs are increasingly built using complex multifaceted machine learning-based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. Here work is presented on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. The challenges of ongoing knowledge graph maintenance and the role of people in that process are reflected on. Finally, we look to need for a knowledge scientist in these processes.
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