Graph-Embedding Empowered Entity Retrieval

Gerritse, Emma J.; Hasibi, Faegheh; Vries, Arjen P. de

doi:10.1007/978-3-030-45439-5_7

Cited by 22 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The entity and word embeddings used for selecting candidate entities are trained on a Wikipedia 2019-07 dump using the Wikipedia2Vec package. 6 Following [9], we set the min-entitycount parameter to zero and used the Wikipedia link graph during training. For the entity disambiguation model, we used GloVe embeddings [20] as suggested in [15].…”

Section: Inputmentioning

confidence: 99%

REL: An Entity Linker Standing on the Shoulders of Giants

Hulst

Hasibi

Dercksen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

Article 25fa pilot End User AgreementThis publication is distributed under the terms of Article 25fa of the Dutch Copyright Act (Auteurswet) with explicit consent by the author. Dutch law entitles the maker of a short scientific work funded either wholly or partially by Dutch public funds to make that work publicly available for no consideration following a reasonable period of time after the work was first published, provided that clear reference is made to the source of the first publication of the work.This publication is distributed under The Association of Universities in the Netherlands (VSNU) 'Article 25fa implementation' pilot project. In this pilot research outputs of researchers employed by Dutch Universities that comply with the legal requirements of Article 25fa of the Dutch Copyright Act are distributed online and free of cost or other barriers in institutional repositories. Research outputs are distributed six months after their first online publication in the original published version and with proper attribution to the source of the original publication.You are permitted to download and use the publication for personal purposes. All rights remain with the author(s) and/or copyrights owner(s) of this work. Any use of the publication other than authorised under this licence or copyright law is prohibited.If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons.

show abstract

Section: Inputmentioning

confidence: 99%

REL: An Entity Linker Standing on the Shoulders of Giants

Hulst

Hasibi

Dercksen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

show abstract

“…This threshold allows for filtering personal entity mentions that do not have the corresponding entities in the conversation history. We used Wikipedia2Vec [60] word and entity embeddings released by Gerritse et al [31]. The threshold τ was set empirically by performing a sweep (on the range [0, 1] in steps of 0.1) using 5-fold cross-validation.…”

Section: Annotation Resultsmentioning

confidence: 99%

Conversational Entity Linking: Problem Definition and Datasets

Joko

Hasibi

Balog

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

Machine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.

show abstract

“…Experimental setup. Similarly as in previous work on embeddings for information retrieval [13], we implement a re-ranking procedure, by updating a list of document scores assigned by an existing IR system (e.g. BM25).…”

Section: Methodsmentioning

confidence: 99%

“…Knowledge graphs provide a structured way to represent information in the form of entities and relations between them [12]. They have become central to a variety of tasks in the Web, including information retrieval [6,13], question answering [19,43], and information extraction [4,14,26]. Many of these tasks can benefit from distributed representations of entities and relations, also known as embeddings.…”

Section: Introductionmentioning

confidence: 99%

Inductive Entity Representations from Text via Link Prediction

Daza

Cochez

Groth

2021

Proceedings of the Web Conference 2021

View full text Add to dashboard Cite

Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation.Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. To address this problem, there is a large amount of work that proposes using machine learning to complete these graphs by predicting new links. Recent work has begun to explore the use of textual descriptions available in knowledge graphs to learn vector representations of entities in order to preform link prediction. However, the extent to which these representations learned for link prediction generalize to other tasks is unclear. This is important given the cost of learning such representations. Ideally, we would prefer representations that do not need to be trained again when transferring to a different task, while retaining reasonable performance.Therefore, in this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. We consider the inductive link prediction and entity classification tasks, which involve entities not seen during training. We also consider an information retrieval task for entity-oriented search. We evaluate an architecture based on a pretrained language model, that exhibits strong generalization to entities not observed during training, and outperforms related state-of-the-art methods (22% MRR improvement in link prediction on average). We further provide evidence that the learned representations transfer well to other tasks without fine-tuning. In the entity classification task we obtain an average improvement of 16% in accuracy compared with baselines that also employ pre-trained models. In the information retrieval task, we obtain significant improvements of up to 8.8% in NDCG@10 for natural language queries. We thus show that the learned representations are not limited KG-specific tasks, and have greater generalization properties than evaluated in previous work. CCS CONCEPTS• Computing methodologies → Statistical relational learning; • Information systems → Information retrieval.This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

show abstract

Graph-Embedding Empowered Entity Retrieval

Cited by 22 publications

References 25 publications

REL: An Entity Linker Standing on the Shoulders of Giants

REL: An Entity Linker Standing on the Shoulders of Giants

Conversational Entity Linking: Problem Definition and Datasets

Inductive Entity Representations from Text via Link Prediction

Contact Info

Product

Resources

About