Beyond Time: Dynamic Context-Aware Entity Recommendation

Tran, Nam Khanh; Niederée, Claudia

doi:10.1007/978-3-319-58068-5_22

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…The Wikipedia clickstream has been used as a ground-truth to evaluate entity recommendation and relatedness in several examples, as it reveals the navigationâl behaviour of users and their preferences while exploring Wikipedia pages. Existing work, however, has not considered language-specific differences and mainly focused on the English Wikipedia clickstream: For example, Tran et al used the English Wikipedia clickstream as ground truth for constructing entity-context queries [16] and Bhatia et al constructed their query dataset based on the English Wikipedia clickstream [1]. Nguan et al evaluated their relatedness ranking method by using the raw number of navigations in Wikipedia clickstream [13].…”

Section: Related Workmentioning

confidence: 99%

EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

Abdollahi,

Gottschalk,

Demidova

2020

Preprint

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An increasing need to analyse event-centric cross-lingual information calls for innovative user interaction models that assist users in crossing the language barrier. However, datasets that reflect user interaction traces in cross-lingual settings required to train and evaluate the user interaction models are mostly missing. In this paper, we present the EventKG+Click dataset that aims to facilitate the creation and evaluation of such interaction models. EventKG+Click builds upon the eventcentric EventKG knowledge graph and language-specific information on user interactions with events, entities, and their relations derived from the Wikipedia clickstream.

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Section: Related Workmentioning

confidence: 99%

EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

Abdollahi,

Gottschalk,

Demidova

2020

Preprint

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“…However, these approaches are optimized to user information needs, and also does not target the global and temporal dimension. 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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“…Recent works have shown that the co-occurrence of entities in documents of a specific time period is a strong indicator of their relatedness during that period [38,40]. We also consider that entities that are co-mentioned frequently with the query entities in important time periods are probably important for them.…”

Section: Relatednessmentioning

confidence: 99%

Ranking Archived Documents for Structured Queries on Semantic Layers

Fafalios

Kasturia

Nejdl

2018

Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

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Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitations.

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Beyond Time: Dynamic Context-Aware Entity Recommendation

Cited by 14 publications

References 13 publications

EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

EventKG+Click: A Dataset of Language-specific Event-centric User Interaction Traces

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Ranking Archived Documents for Structured Queries on Semantic Layers

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