Knowledge Graph Induction Enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

Mihindukulasooriya, Nandana; Mike, Sava,; Rossiello, Gaetano; Chowdhury, Md. Faisal Mahbub; Yachbes, I.; Gidh, Aditya; Duckwitz, Jillian; Nisar, Kovit; Santos, M. B.; Gliozzo, Alfio

doi:10.1007/978-3-031-19433-7_47

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…We use cross-entropy loss as standard in machine translation whereas, in teacher forcing, the model regards the translation problem as a one-to-one mapping process and maximizes the log-likelihood of generating the linearized facts given the input text. As reported in (Mihindukulasooriya et al 2022), our KnowGL model outperforms (F1 = 70.74) both a standard IE pipeline system (F1 = 42.50) and the current state-of-the-art generative IE model (F1 = 68.93) (Josifoski et al 2022). For the evaluation, we use the test set released with the REBEL dataset.…”

Section: Knowledge Generationmentioning

confidence: 77%

“…(SUBJECT, RELATION, OB-JECT), grounded with a given well-defined ontology (Hogan et al 2021). The usage of formal languages to represent KGs enables unambiguous access to data and facilitates automatic reasoning capabilities that enhance downstream applications, such as analytics, knowledge discovery or recommendations (Mihindukulasooriya et al 2022).…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…For this purpose, we propose a framework able to convert text into a set of Wikidata statements. As shown in (Mihindukulasooriya et al 2022), KnowGL parser can be used to automatically extract KGs from collections of documents, with the purpose to help users with semantic content exploration, to create trend analysis, to extract entity infoboxes from text, or to enhance content-based recommendation systems with semantic features.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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KnowGL: Knowledge Generation and Linking from Text

Rossiello¹,

Chowdhury²,

Mihindukulasooriya³

et al. 2023

AAAI

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We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART. Given a sentence, we fine-tune such models to detect pairs of entity mentions and jointly generate a set of facts consisting of the full set of semantic annotations for a KG, such as entity labels, entity types, and their relationships. To showcase the capabilities of our tool, we build a web application consisting of a set of UI widgets that help users to navigate through the semantic data extracted from a given input text. We make the KnowGL model available at https://huggingface.co/ibm/knowgl-large.

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Section: Knowledge Generationmentioning

confidence: 77%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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KnowGL: Knowledge Generation and Linking from Text

Rossiello¹,

Chowdhury²,

Mihindukulasooriya³

et al. 2023

AAAI

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“…Some of these requirements are of common nature for semantic knowledge graphs like R1, R2, R5 and therefore support the applicability of this technology. We consider the requirements R3, R4, R5, R7, R8, R9 more specific for Digital Twins and do not see them in other applications [22,24].…”

Section: Requirements For Semantic Digital Threadsmentioning

confidence: 99%

Scaling Knowledge Graphs for Automating AI of Digital Twins

Ploennigs

Semertzidis

Lorenzi

et al. 2022

The Semantic Web – ISWC 2022

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Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems. Semantic models are used increasingly to link these datasets from different stages of the IoT systems life-cycle together and to automatically configure the AI modelling pipelines. This combination of semantic models with AI pipelines running on external datasets raises unique challenges particular if rolled out at scale. Within this paper we will discuss the unique requirements of applying semantic graphs to automate Digital Twins in different practical use cases. We will introduce the benchmark dataset DTBM that reflects these characteristics and look into the scaling challenges of different knowledge graph technologies. Based on these insights we will propose a reference architecture that is in-use in multiple products in IBM and derive lessons learned for scaling knowledge graphs for configuring AI models for Digital Twins.

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Knowledge Graph Induction Enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

Cited by 2 publications

References 32 publications

KnowGL: Knowledge Generation and Linking from Text

KnowGL: Knowledge Generation and Linking from Text

Scaling Knowledge Graphs for Automating AI of Digital Twins

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