Construction of Enterprise Knowledge Graphs (I)

Villazón-Terrazas, Boris; Garcia-Santa, Nuria; Ren, Yuan; Srinivas, Kavitha; Rodríguez-Muro, Mariano; Alexopoulos, Panos; Pan, Jeff Z.

doi:10.1007/978-3-319-45654-6_4

Cited by 8 publications

(4 citation statements)

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“…The current implementation of KGLiDS adopts the RDF model to manage the LiDS graph and uses Stardog as storage engine [11]. KGLiDS uses RDFbased knowledge graph technology because (i) it already includes the formalization of rules and metadata using a controlled vocabulary for the labels in the graphs ensuring interoperability [13,37], (ii) it has built-in notions of modularity in the form of named graphs, for instance, each pipeline is abstracted in its own named graph [10], and (iii) it is schema-agnostic, allowing the platform to support reasoning and semantic manipulation, e.g., adding new labeled edges between equivalent artifacts, as the platform evolves [9,14,39], and (iv) it has a powerful query language (SPARQL) to support federated query processing [4,27,35]. In extension, KGLiDS uses the RDF-star [17] model, which supports annotating edges between nodes with metadata, which enables us, for instance, to capture the similarity scores for similarity edges between column nodes of datasets.…”

Section: The Kglids Storage and Interfacesmentioning

confidence: 99%

Linked Data Science Powered by Knowledge Graphs

Helali¹,

Vashisth²,

Carrier³

et al. 2023

Preprint

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In recent years, we have witnessed a growing interest in data science not only from academia but particularly from companies investing in data science platforms to analyze large amounts of data. In this process, a myriad of data science artifacts, such as datasets and pipeline scripts, are created. Yet, there has so far been no systematic attempt to holistically exploit the collected knowledge and experiences that are implicitly contained in the specification of these pipelines, e.g., compatible datasets, cleansing steps, ML algorithms, parameters, etc. Instead, data scientists still spend a considerable amount of their time trying to recover relevant information and experiences from colleagues, trial and error, lengthy exploration, etc. In this paper, we, therefore, propose a scalable system (KGLiDS) that employs machine learning to extract the semantics of data science pipelines and captures them in a knowledge graph, which can then be exploited to assist data scientists in various ways. This abstraction is the key to enabling Linked Data Science since it allows us to share the essence of pipelines between platforms, companies, and institutions without revealing critical internal information and instead focusing on the semantics of what is being processed and how. Our comprehensive evaluation uses thousands of datasets and more than thirteen thousand pipeline scripts extracted from data discovery benchmarks and the Kaggle portal and shows that KGLiDS significantly outperforms state-of-the-art systems on related tasks, such as dataset recommendation and pipeline classification.

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Section: The Kglids Storage and Interfacesmentioning

confidence: 99%

Linked Data Science Powered by Knowledge Graphs

Helali¹,

Vashisth²,

Carrier³

et al. 2023

Preprint

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“…The Data Source of the EKG is usually centralized [32]. Some papers explain the implementation of Enterprise Knowledge Graphs [11,30,33]. Additionally, some tools allow to integrate database contents [1,31] or documents [9,14] into a Knowledge Graph or even directly from texts through Named Entity Resolution [12] and Thematic Scope Resolution [4,12].…”

Section: Related Workmentioning

confidence: 99%

Designing a Business View of Enterprise Data

Vidé

Marty²,

Ravat

et al. 2021

25th International Database Engineering &Amp; Applications Symposium

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Nowadays, companies manage a large volume of data usually organised in "silos". Each "data silo" contains data related to a specific Business Unit, or a project. This scattering of data does not facilitate decision-making requiring the use and cross-checking of data coming from different silos. So, a challenge remains: the construction of a Business View of all data in a company. In this paper, we introduce the concepts of Enterprise Knowledge Graph (EKG) and Decentralised EKG (DEKG). Our DEKG aims at generating a Business View corresponding to a synthetic view of data sources. We first define and model a DEKG with an original process to generate a Business View before presenting the possible implementation of a DEKG.

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“…Information representation in Linked Open Data format aids in the data's reuse and linkage with other KGs (Ehrlinger & Wöß, 2016;Villazon-Terrazas et al, 2021). For an individual in a disaster management situation it is also important to access geospatial data that contains information about the location that suffered the destruction.…”

Section: Introductionmentioning

confidence: 99%

“…Our focus in this work is on representing information for disaster management and media planning, more specifically, information about: (a) Observations and Events (for example, information from photos, and information from text messages); (b) Spatial and Temporal data, such as coordinates or labels of locations and timestamps; and (c) Tasks and Plans for disaster management and media planning. Information representation in Linked Open Data format aids in the data’s reuse and linkage with other KGs (Ehrlinger & Wöß, 2016; Villazon-Terrazas et al ., 2021). For an individual in a disaster management situation it is also important to access geospatial data that contains information about the location that suffered the destruction.…”

Section: Introductionmentioning

confidence: 99%

XR4DRAMA a knowledge-based system for disaster management and media planning

Vassiliades,

Stathopoulos-Kampilis,

Antzoulatos

et al. 2024

The Knowledge Engineering Review

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In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework 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). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).

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Construction of Enterprise Knowledge Graphs (I)

Cited by 8 publications

References 0 publications

Linked Data Science Powered by Knowledge Graphs

Linked Data Science Powered by Knowledge Graphs

Designing a Business View of Enterprise Data

XR4DRAMA a knowledge-based system for disaster management and media planning

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