PageRank and Generic Entity Summarization for RDF Knowledge Bases

Diefenbach, Dennis; Thalhammer, Andreas

doi:10.1007/978-3-319-93417-4_10

Cited by 20 publications

(12 citation statements)

References 16 publications

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“…Furthermore, end-to-end approaches suffer from the lack of explainability, which makes it challenging for users to validate the correctness of the result. Explainability in this context has therefore become an active area of research, with solutions proposed including translating back structured queries into natural language sentences [8,20,29] or summarizing the entities in the results [11].…”

Section: Related Workmentioning

confidence: 99%

Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

Sima¹,

Farias²,

Anisimova³

et al. 2021

Preprint

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The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available.In this paper, we focus on the challenges of natural language processing over knowledge graphs of scientific datasets. In particular, we introduce Bio-SODA, a natural language processing engine that does not require training data in the form of question-answer pairs for generating SPARQL queries. Bio-SODA uses a generic graph-based approach for translating user questions to a ranked list of SPARQL candidate queries. Furthermore, Bio-SODA uses a novel ranking algorithm that includes node centrality as a measure of relevance for selecting the best SPARQL candidate query. Our

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Section: Related Workmentioning

confidence: 99%

Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

Sima¹,

Farias²,

Anisimova³

et al. 2021

Preprint

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show abstract

“…In a similar setting, traditional entity summarisation (e.g. [2]) aims at the identification of relevant facts given a query concept. While entity summarisation approaches also utilise semantic information given in knowledge graphs, they are not considering temporal information.…”

Section: Related Workmentioning

confidence: 99%

EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph

Gottschalk

Demidova

2020

The Semantic Web: ESWC 2020 Satellite Events

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Research on notable accomplishments and important events in the life of people of public interest usually requires close reading of long encyclopedic or biographical sources, which is a tedious and time-consuming task. Whereas semantic reference sources, such as the EventKG knowledge graph, provide structured representations of relevant facts, they often include hundreds of events and temporal relations for particular entities. In this paper, we present EventKG+BTa timeline generation system that creates concise and interactive spatiotemporal representations of biographies from a knowledge graph using distant supervision. 1 http://eventkg-biographies.l3s.uni-hannover.de

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“…(i) Rank-based: The first strategy is to find all entities which have a label corresponding to the text span. These are then ordered based on the page rank of the KB (Diefenbach and Thalhammer, 2018). Finally, we return the top-ranked entity.…”

Section: Entity Linkingmentioning

confidence: 99%

IntKB: A Verifiable Interactive Framework for Knowledge Base Completion

Kratzwald¹,

Guo²,

Feuerriegel³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete. State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision. As a remedy, we propose IntKB: a novel interactive framework for KB completion from text based on a question answering pipeline. Our framework is tailored to the specific needs of a human-in-the-loop paradigm: (i) We generate facts that are aligned with text snippets and are thus immediately verifiable by humans. (ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero-and few-shot relations over time. (iii) We only trigger human interactions when there is enough information for a correct prediction. Therefore, we train our system with negative examples and a fold-option if there is no answer. Our framework yields a favorable performance: it achieves a hit@1 ratio of 29.7% for initially unseen relations, upon which it gradually improves to 46.2%.

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PageRank and Generic Entity Summarization for RDF Knowledge Bases

Cited by 20 publications

References 16 publications

Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

EventKG+BT: Generation of Interactive Biography Timelines from a Knowledge Graph

IntKB: A Verifiable Interactive Framework for Knowledge Base Completion

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