Learning to Map Wikidata Entities To Predefined Topics

Bhargava, Preeti; Spasojevic, Nemanja; Ellinger, Sarah; Rao, Adithya; Menon, Abhinand; Fuhrmann, Saul; Hu, Guoning

doi:10.1145/3308560.3316749

Cited by 6 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, statistical entity-topic models [22], [23] have been studied to map named-entities to topics for document topic analysis. Bhargava et al [24] proposed multiple methods to learn to map wikidata entities to pre-defined topics. The challenge, in our case, was the lack of a labeled set of incidents based on pre-defined topics.…”

Section: Mapping Entities To Incident Titlesmentioning

confidence: 99%

SoftNER: Mining Knowledge Graphs From Cloud Incidents

Shetty¹,

Bansal²,

Kumar³

et al. 2021

Preprint

View full text Add to dashboard Cite

Incident management is a critical part of the DevOps processes for developing and operating large-scale services in the cloud. Incident reports filed by customers are largely unstructured making any automated diagnosis or mitigation non-trivial. It requires on-call engineers to parse verbose reports to understand the issue and locate key information. Prior work has looked into extraction of key attributes or entities like error codes, tenant Ids, stack traces, etc. from incident and bug reports. Although a flat list of entities is informative, to unlock the full potential of knowledge extraction, it is necessary to provide context to these entities. For instance, the relations between the real-world concepts or objects that these entities represent in otherwise unstructured data is useful for downstream tasks like incident linking, triaging and mitigation. With this additional context, entities are transformed from "Strings" to "Things". In this work, we present an approach to mine and score binary entity relations from co-occurring entity pairs. We evaluate binary relations extracted and show that our approach has a high precision of 0.9. Further, we construct knowledge graphs automatically and show that the implicit knowledge in the graph can be used to mine and rank relevant entities for distinct incidents, by mapping entities to clusters of incident titles.

show abstract

Section: Mapping Entities To Incident Titlesmentioning

confidence: 99%

SoftNER: Mining Knowledge Graphs From Cloud Incidents

Shetty¹,

Bansal²,

Kumar³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Altszyler et al found that LSA more effective than Word2Vec in a small corpus, less than 1 million words [12]. A latest research is a kind of multimodal when using GloVe, Wikidata, Wikipedia, and entity-topic co-occurrences all together to define topics [27]. In this research, we focus only on LDA to see how well it can work with quotations and no need to pre-define topics.…”

Section: Literature Reviewsmentioning

confidence: 99%

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

Rahman

Sidorov

et al. 2020

Advances in Computational Intelligence

View full text Add to dashboard Cite

In this paper, we extract quotations from Al Jazeera's news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to see how coronavirus impacts human beings. By combining noun phrases as inputs before the training and C v measure for coherence values, we obtain an average coherence value of 0.66 with a least average number of topics of 24.8. The result covers some of the top issues that our world has been facing against the COVID-19 pandemic.

show abstract

“…This leads us to a secondary research question: What are effective designs for collecting large-scale annotations for conversational data? Although a large body of research exists on effective designs for collecting large-scale entity annotations [1,6,29,41], to the best of our knowledge, there is no work on conversational data. We run a number of pilot experiments using Amazon Mechanical Turk (MTurk) to select the best design and instruction.…”

Section: Introductionmentioning

confidence: 99%

Conversational Entity Linking: Problem Definition and Datasets

Joko

Hasibi

Balog

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

Machine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.

show abstract

Learning to Map Wikidata Entities To Predefined Topics

Cited by 6 publications

References 13 publications

SoftNER: Mining Knowledge Graphs From Cloud Incidents

SoftNER: Mining Knowledge Graphs From Cloud Incidents

Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

Conversational Entity Linking: Problem Definition and Datasets

Contact Info

Product

Resources

About