Deriving Validity Time in Knowledge Graph

Leblay, Julien; Chekol, Melisachew Wudage

doi:10.1145/3184558.3191639

Cited by 283 publications

(167 citation statements)

References 13 publications

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“…1) three event-based TKGs: ICEWS18 (Boschee et al, 2015), ICEWS14 (Trivedi et al, 2017), and GDELT (Leetaru and Schrodt, 2013); and 2) two knowledge graphs where temporally associated facts have meta-facts as (s, r, o, [t s , t e ]) where t s is the starting time point and t e is the ending time point: WIKI (Leblay and Chekol, 2018) and YAGO (Mahdisoltani et al, 2014).…”

Section: Datasetsmentioning

confidence: 99%

“…Given a temporal knowledge graph with timestamps varying from t 0 to t T , TKG reasoning primarily has two settings -interpolation and extrapolation. In the interpolation setting, new facts are predicted for time t such that t 0 ≤ t ≤ t T (García-Durán et al, 2018;Leblay and Chekol, 2018;Dasgupta et al, 2018). In contrast, extrapolation reasoning, as a less studied setting, focuses on predicting new facts (e.g., unseen events) over timestamps t that are greater than t T (i.e., t > t T ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs

Jin

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

220

187

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Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This paper proposes Recurrent Event Network (RE-NET), a novel autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. Specifically, our RE-NET employs a recurrent event encoder to encode past facts, and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules. We evaluate our proposed method via link prediction at future times on five public datasets. Through extensive experiments, we demonstrate the strength of RE-NET, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets 1 .

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs

Jin

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

220

187

View full text Add to dashboard Cite

show abstract

“…Reference [48] regards timestamps as a sequence of digits (from 0 to 9), then uses LSTMs to encode the relation vectors and the time digits. [49] models the interactions between relations and time, and studies various ways to combine the time embedding vector with relation embedding vector, such as concatenate, sum or dot product operations. Recently, HyTE [24] proposes a KGE method based on projected-time translation.…”

Section: Dynamic Knowledge Graph Embeddingmentioning

confidence: 99%

“…By doing so, less frequent year mentions are grouped into the same time bin but years with high frequency forms individual bins. For example, in KG Wikidata12k [49] which is extracted from a preprocessed dataset of Wikidata, there are bins like 1596-1777, 1791-1815 with a large span as the events occurring on those points of time are quite less in the KG. The years like 2013, 2014 being highly frequent are self-contained.…”

Section: Timespan-aware Temporal Evolution Embedding Modelmentioning

confidence: 99%

Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution

Tang

Yuan

et al. 2020

IEEE Access

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Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are twofold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate. INDEX TERMS Knowledge graph embedding, gated recurrent units, time unbalance.

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“…However, they restrict their domain to event-based interac- tion type of datasets which are fairly dense in nature. Leblay and Chekol (2018) propose a method for temporal embedding learning using side information from the atemporal part of the graph. However, we use purely temporal KG to learn the temporally aware embedding.…”

Section: Related Workmentioning

confidence: 99%

HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

Dasgupta

Ray

Talukdar

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

326

101

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Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano Ronaldo, playsFor, Manchester United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.

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Deriving Validity Time in Knowledge Graph

Cited by 283 publications

References 13 publications

Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs

Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs

Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution

HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

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