Edge: Enriching Knowledge Graph Embeddings with External Text

Rezayi, Saed; Zhao, Handong; Kim, Sung‐Chul; Rossi, Ryan A.; Lipka, Nedim; Li, Sheng

doi:10.18653/v1/2021.naacl-main.221

Cited by 21 publications

(10 citation statements)

References 21 publications

(16 reference statements)

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“…The original and the augmented graphs are then aligned to suppress the noise and distil relevant information. In our work, we focus on adding extra edges to the KG rather than nodes as in Rezayi et al (2021) and Wang et al (2016).…”

Section: Kge Methodsmentioning

confidence: 99%

Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Hakami¹,

Hakami²,

Mandya³

et al. 2022

Preprint

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Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that cooccur in sentences in the corpus (i.e. with mention entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, Su-perBorrow, that learns to score the suitability of an LDP to represent a without-mention entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.

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Section: Kge Methodsmentioning

confidence: 99%

Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Hakami¹,

Hakami²,

Mandya³

et al. 2022

Preprint

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“…There are comprehensive survey articles building KGs from relational databases [35], semi-structued data [33], and unstructured text [1,8,11,28,34]. Rezayi et al [31] propose an approach to augment a KG with key phrases generated from textual content of entities. In our work, we augment our KG with semantically rich triples generated from textual content of each entity.…”

Section: Related Workmentioning

confidence: 99%

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

Mihindukulasooriya¹,

Mike²,

Rossiello³

et al. 2022

Preprint

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A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategies are increasingly becoming more challenging for both researchers and executives as the amount of information grows in both velocity and volume. In this paper we present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph from both structured and textual data obtained by integrating various applications used by the community related to research projects, academic papers, datasets, achievements and recognition. In order to make the Knowledge Graph more accessible to application developers, we identified a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated. We outline two distinct scenarios: recommendation and analytics for business use. We will discuss these scenarios in detail and provide an empirical evaluation on entity recommendation specifically. The methodology used and the lessons learned from this work can be applied to other organizations facing similar challenges.

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“…Models that integrate text information include DKRL [ 14 ] and Joint [ 32 ], which use a continuous bag of words and CNN for text encoding; STKRL [ 33 ], which uses an RNN to obtain text sequence information; EDGE [ 34 ] and AATE [ 35 ], which are based on the bidirectional long short-term memory network; TA-ConvKB [ 36 ], which uses bidirectional short and long term memory network with attention to encode the text. The text- and structure-based representations are generally combined when calculating the score functions.…”

Section: Related Workmentioning

confidence: 99%

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

Wang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.

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Edge: Enriching Knowledge Graph Embeddings with External Text

Cited by 21 publications

References 21 publications

Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion

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

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs

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