scholarly journals Multi-goal Pathfinding in Cyber-Physical-Social Environments: Multi-layer Search over a Semantic Knowledge Graph

2017 ◽  
Vol 112 ◽  
pp. 741-750 ◽  
Author(s):  
Oudom Kem ◽  
Flavien Balbo ◽  
Antoine Zimmermann ◽  
Pierre Nagellen
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Graph ◽  
Social Environments ◽  
Semantic Knowledge Graph

scholarly journals Knowledge-infused Learning for Entity Prediction in Driving Scenes

2021 ◽  
Vol 4 ◽  
Author(s):  
Ruwan Wickramarachchi ◽  
Cory Henson ◽  
Amit Sheth
Keyword(s):  
Scene Understanding ◽  
Autonomous Driving ◽  
Semantic Knowledge ◽  
Quality Data ◽  
Knowledge Graph ◽  
Social Environments ◽  
High Quality Data ◽  
Knowledge Based ◽  
Standard Mapping ◽  
High Level

Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.

Bringing Semantic Knowledge Graph Technology to Your Data

IEEE Software ◽  
2020 ◽  
Vol 37 (2) ◽  
pp. 89-94
Author(s):  
Bob van Luijt ◽  
Micha Verhagen
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Graph ◽  
Semantic Knowledge Graph

scholarly journals FAIR.ReD: Semantic knowledge graph infrastructure for the life sciences

2019 ◽  
Vol 3 ◽  
Author(s):  
Lars Vogt ◽  
Sören Auer ◽  
Thomas Bartolomaeus ◽  
Pier Luigi Buttigieg ◽  
Peter Grobe ◽  
...  
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Additional Data ◽  
Source Code ◽  
Life Sciences ◽  
Data Access ◽  
Semantic Knowledge ◽  
Knowledge Graph ◽  
Machine Readable ◽  
Semantic Knowledge Graph

We would like to present FAIR Research Data: Semantic Knowledge Graph Infrastructure for the Life Sciences (in short, FAIR.ReD), a project initiative that is currently being evaluated for funding. FAIR.ReD is a software environment for developing data management solutions according to the FAIR (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016) data principles. It utilizes what we call a Data Sea Storage, which employs the idea of Data Lakes to decouple data storage from data access but modifies it by storing data in a semantically structured format as either semantic graphs or semantic tables, instead of storing them in their native form. Storage follows a top-down approach, resulting in a standardized storage model, which allows sharing data across all FAIR.ReD Knowledge Graph Applications (KGAs) connected to the same Sea, with newly developed KGAs having automatically access to all contents in the Sea. In contrast access and export of data follows a bottom-up approach that allows the specification of additional data models to meet the varying domain-specific and programmatic needs for accessing structured data. The FAIR.ReD engine enables bidirectional data conversion between the two storage models and any additional data model, which will substantially reduce conversion workload for data-rich institutes (Fig. 1). Moreover, with the possibility to store data in semantic tables, FAIR.ReD provides high performance storage for incoming data streams such as sensory data. FAIR.ReD KGAs are modularly organized. Modules can be edited using the FAIR.ReD editor and combined to form coherent KGAs. The editor allows domain experts to develop their own modules and KGAs without any programming experience required, thus also allowing smaller projects and individual researchers to build their own FAIR data management solution. Contents from FAIR.ReD KGAs can be published under a Creative Commons license as documents, micropublications, or nanopublications, each receiving their own DOI. A publication-life-cycle is implemented in FAIR.ReD and allows updating published contents for corrections or additions without overwriting the originally published version. Together with the fact that data and metadata are semantically structured and machine-readable, all contents from FAIR.ReD KGAs will comply with the FAIR Guiding Principles. Due to all FAIR.Red KGAs providing access to semantic knowledge graphs in both a human-readable and a machine-readable version, FAIR.ReD seamlessly integrates the complex RDF (Resource Description Framework) world with a more intuitively comprehensible presentation of data in form of data entry forms, charts, and tables. Guided by use cases, the FAIR.ReD environment will be developed using semantic programming where the source code of an application is stored in its own ontology. The set of source code ontologies of a KGA and its modules provides the steering logic for running the KGA. With this clear separation of steering logic from interpretation logic, semantic programming follows the idea of separating main layers of an application, analog to the separation of interpretation logic and presentation logic. Each KGA and module is specified exactly in this way and their source code ontologies stored in the Data Sea. Thus, all data and metadata are semantically transparent and so is the data management application itself, which substantially improves their sustainability on all levels of data processing and storing.

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