Personal Knowledge Graphs

Balog, Krisztian; Kenter, Tom

doi:10.1145/3341981.3344241

Cited by 66 publications

(49 citation statements)

References 14 publications

(16 reference statements)

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“…Another source of bias in constructing PKGs is time [2]. Timebased events can greatly influence search behavior.…”

Section: Bias From Algorithmsmentioning

confidence: 99%

“…. $15.00 https://doi.org /10.1145/3409256.3409834 which is a structured form of information about "entities personally related to a user, their attributes and the relations between them" [2]. PKGs provide the system with (locally stored) rich information about the user, to tailor the answers as much to their liking as possible [2].…”

Section: Introductionmentioning

confidence: 99%

“…$15.00 https://doi.org /10.1145/3409256.3409834 which is a structured form of information about "entities personally related to a user, their attributes and the relations between them" [2]. PKGs provide the system with (locally stored) rich information about the user, to tailor the answers as much to their liking as possible [2]. However, introducing such a data resource and personalizing search results is not without risks: personalization can lead to echo-chamber effect and reinforce bias [4].…”

Section: Introductionmentioning

confidence: 99%

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Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Gerritse

Hasibi

Vries

2020

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

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Conversational AI systems are being used in personal devices, providing users with highly personalized content. Personalized knowledge graphs (PKGs) are one of the recently proposed methods to store users' information in a structured form and tailor answers to their liking. Personalization, however, is prone to amplifying bias and contributing to the echo-chamber phenomenon. In this paper, we discuss different types of biases in conversational search systems, with the emphasis on the biases that are related to PKGs. We review existing definitions of bias in the literature: people bias, algorithm bias, and a combination of the two, and further propose different strategies for tackling these biases for conversational search systems. Finally, we discuss methods for measuring bias and evaluating user satisfaction.

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“…Another source of bias in constructing PKGs is time [2]. Timebased events can greatly influence search behavior.…”

Section: Bias From Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Gerritse

Hasibi

Vries

2020

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

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“…Ambiguity is inherent in the way entities are mentioned in natural language text. Grounding such ambiguous mentions to their corresponding entities, the task of entity linking, is critical to many applications: automated knowledge base construction and completion (Riedel et al, 2013;Surdeanu et al, 2012), information retrieval (Meij et al, 2014), smart assistants (Balog and Kenter, 2019), question answering (Dhingra et al, 2020), text mining Murty et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Clustering-based Inference for Biomedical Entity Linking

Angell¹,

Monath²,

Mohan³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates entity linking models which are able to link mentions of unseen entities using learned representations of entities. Previous approaches link each mention independently, ignoring the relationships within and across documents between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a knowledge base entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments we improve the state-of-the-art entity linking accuracy on two biomedical entity linking datasets including on the largest publicly available dataset.

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“…First, they are particularly suited to graph-alignment and machine translation as downstream tasks which necessarily leads to tackle computational challenges in large-scale KBs. Second, these methods assume that each KB has access to all the entities and relations that are stored in the other KBs, while it may not be feasible neither relevant to the KBs to share unaligned information such as in personal KBs (Balog and Kenter, 2019).…”

Section: Introductionmentioning

confidence: 99%

Knowledge Base Embedding By Cooperative Knowledge Distillation

Sourty¹,

Moreno²,

Servant³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of knowledge distillation over neural networks. Given a set of KBs, our proposed approach KD-MKB, learns KB embeddings by mutually and jointly distilling knowledge within a dynamic teacher-student setting. Experimental results on two standard datasets show that knowledge distillation between KBs through entity and relation inference is actually observed. We also show that cooperative learning significantly outperforms the two proposed baselines, namely traditional and sequential distillation.

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Personal Knowledge Graphs

Cited by 66 publications

References 14 publications

Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Clustering-based Inference for Biomedical Entity Linking

Knowledge Base Embedding By Cooperative Knowledge Distillation

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