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Anthony Colas

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2 papers

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AAAI Conference 2021 Conference Paper

ChronoR: Rotation Based Temporal Knowledge Graph Embedding

Ali Sadeghian
Mohammadreza Armandpour
Anthony Colas
Daisy Zhe Wang

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a kdimensional rotation transformation parametrized by relation and time, such that after each fact’s head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.

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NeurIPS Conference 2021 Conference Paper

EventNarrative: A Large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation

Anthony Colas
Ali Sadeghian
Yue Wang
Daisy Zhe Wang

We introduce EventNarrative, a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. Given the recent advances in event-driven Information Extraction (IE), and that prior research on graph-to-text only focused on entity-driven KGs, this paper focuses on event-centric data. However, our data generation system can still be adapted to other types of KG data. Existing large-scale datasets in the graph-to-text area are non-parallel, meaning there is a large disconnect between the KGs and text. The datasets that have a paired KG and text, are small scale and manually generated or generated without a rich ontology, making the corresponding graphs sparse. Furthermore, these datasets contain many unlinked entities between their KG and text pairs. EventNarrative consists of approximately 230, 000 graphs and their corresponding natural language text, six times larger than the current largest parallel dataset. It makes use of a rich ontology, all the KGs entities are linked to the text, and our manual annotations confirm a high data quality. Our aim is two-fold: to help break new ground in event-centric research where data is lacking and to give researchers a well-defined, large-scale dataset in order to better evaluate existing and future knowledge graph-to-text models. We also evaluate two types of baselines on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.

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