It's the Same Old Story! Enriching Event-Centric Knowledge Graphs by Narrative Aspects

• URL: http://arxiv.org/abs/2205.03876v1
• Date: Sun, 8 May 2022 14:00:41 GMT
• Title: It's the Same Old Story! Enriching Event-Centric Knowledge Graphs by Narrative Aspects
• Authors: Florian Pl\"otzky and Wolf-Tilo Balke
• Abstract summary: We introduce a novel and lightweight structure for event-centric knowledge graphs, which for the first time allows for queries incorporating viewpoint-dependent and narrative aspects. Our experiments prove the effective incorporation of subjective attributions for event participants and show the benefits of specifically tailored indexes for narrative query processing.
• Score: 0.3655021726150368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our lives are ruled by events of varying importance ranging from simple everyday occurrences to incidents of societal dimension. And a lot of effort is taken to exchange information and discuss about such events: generally speaking, stringent narratives are formed to reduce complexity. But when considering complex events like the current conflict between Russia and Ukraine it is easy to see that those events cannot be grasped by objective facts alone, like the start of the conflict or respective troop sizes. There are different viewpoints and assessments to consider, a different understanding of the roles taken by individual participants, etc. So how can such subjective and viewpoint-dependent information be effectively represented together with all objective information? Recently event-centric knowledge graphs have been proposed for objective event representation in the otherwise primarily entity-centric domain of knowledge graphs. In this paper we introduce a novel and lightweight structure for event-centric knowledge graphs, which for the first time allows for queries incorporating viewpoint-dependent and narrative aspects. Our experiments prove the effective incorporation of subjective attributions for event participants and show the benefits of specifically tailored indexes for narrative query processing.

Related papers

• SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events [13.894639630989563]
  We argue that such knowledge can be elicited through a participant achievement lens. We analyze a complex event in a narrative according to the intended achievements of the participants. We show that smaller models fine-tuned on our dataset can achieve performance surpassing larger models.
  arXiv  Detail & Related papers  (2024-08-11T14:52:40Z)
• Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs [2.657233098224094]
  We show how narratives concerning complex events can be constructed and utilized. We provide an algorithm that mines such narratives from texts to account for different perspectives on complex events.
  arXiv  Detail & Related papers  (2024-04-25T08:33:08Z)
• Open-Domain Event Graph Induction for Mitigating Framing Bias [89.46744219887005]
  We argue that studying and identifying framing bias is a crucial step towards trustworthy event understanding. We propose a novel task, neutral event graph induction, to address this problem. Our task aims to induce such structural knowledge with minimal framing bias in an open domain.
  arXiv  Detail & Related papers  (2023-05-22T08:57:42Z)
• Whats New? Identifying the Unfolding of New Events in Narratives [11.058053956455545]
  We study the Information Status (IS) of the events and propose a novel challenging task: the automatic identification of new events in a narrative. We define an event as a triplet of subject, predicate, and object. The event is categorized as new with respect to the discourse context. We annotated a publicly available corpus of narratives with the new events at sentence level using human annotators.
  arXiv  Detail & Related papers  (2023-02-15T15:54:01Z)
• Are All Steps Equally Important? Benchmarking Essentiality Detection of Events [92.92425231146433]
  This paper examines the extent to which current models comprehend the essentiality of step events in relation to a goal event. We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow. The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality.
  arXiv  Detail & Related papers  (2022-10-08T18:00:22Z)
• Computational Lens on Cognition: Study Of Autobiographical Versus Imagined Stories With Large-Scale Language Models [95.88620740809004]
  We study differences in the narrative flow of events in autobiographical versus imagined stories using GPT-3. We found that imagined stories have higher sequentiality than autobiographical stories. In comparison to imagined stories, autobiographical stories contain more concrete words and words related to the first person.
  arXiv  Detail & Related papers  (2022-01-07T20:10:47Z)
• ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning [49.795767003586235]
  We introduce ESTER, a comprehensive machine reading comprehension dataset for Event Semantic Relation Reasoning. We study five most commonly used event semantic relations and formulate them as question answering tasks. Experimental results show that the current SOTA systems achieve 60.5%, 57.8%, and 76.3% for event-based F1, token based F1 and HIT@1 scores respectively.
  arXiv  Detail & Related papers  (2021-04-16T19:59:26Z)
• Paragraph-level Commonsense Transformers with Recurrent Memory [77.4133779538797]
  We train a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. Our results show that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
  arXiv  Detail & Related papers  (2020-10-04T05:24:12Z)
• Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality [21.67542584041709]
  Causality visualization can help people understand temporal chains of events. But as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use. We propose the use of textual narratives as a data-driven storytelling method to augment causality visualization.
  arXiv  Detail & Related papers  (2020-09-06T05:46:24Z)
• Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
  We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays. Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
  arXiv  Detail & Related papers  (2020-04-27T11:54:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.