Multiple Models for Recommending Temporal Aspects of Entities

Nguyen, Tu N.; Kanhabua, Nattiya; Nejdl, Wolfgang

doi:10.1007/978-3-319-93417-4_30

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Recently, Zhang et al (2016b); Tran et al (2017) proposed time-aware probabilistic approaches that combine 'static' entity relatedness with temporal factors from different sources. Nguyen et al (2018) studied the task of time-aware ranking for entity aspects and propose an ensemble model to address the sub-features competing problem.…”

Section: Entity Relatedness and Recommendationmentioning

confidence: 99%

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Nguyen¹,

Nejdl²

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time.In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.

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Section: Entity Relatedness and Recommendationmentioning

confidence: 99%

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Nguyen¹,

Nejdl²

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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“…Such control over similarity is helpful for example in knowledge exploration applications, which we investigated in previous work [3]. Other potential applications can be found in time-aware entity recommendations [18,28] (e.g., find "related contemporary entities" vs. "related entities in the past" vs. "time-independent related entities") and in temporal information retrieval, where it is important to keep track of the time factor.…”

Section: Introductionmentioning

confidence: 99%

Towards Encoding Time in Text-Based Entity Embeddings

Bianchi

Palmonari

Nozza

2018

Lecture Notes in Computer Science

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Knowledge Graphs (KG) are widely used abstractions to represent entity-centric knowledge. Approaches to embed entities, entity types and relations represented in the graph into vector spaces -often referred to as KG embeddings -have become increasingly popular for their ability to capture the similarity between entities and support other reasoning tasks. However, representation of time has received little attention in these approaches. In this work, we make a first step to encode time into vector-based entity representations using a text-based KG embedding model named Typed Entity Embeddings (TEEs). In TEEs, each entity is represented by a vector that represents the entity and its type, which is learned from entity mentions found in a text corpus. Inspired by evidence from cognitive sciences and application-oriented concerns, we propose an approach to encode representations of years into TEEs by aggregating the representations of the entities that occur in event-based descriptions of the years. These representations are used to define two time-aware similarity measures to control the implicit effect of time on entity similarity. Experimental results show that the linear order of years obtained using our model is highly correlated with natural time flow and the effectiveness of the time-aware similarity measure proposed to flatten the time effect on entity similarity.

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