TiFi: Taxonomy Induction for Fictional Domains

Chu, Cuong Xuan; Razniewski, Simon; Weikum, Gerhard

doi:10.1145/3308558.3313519

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…Thus it is also possible to use them as a class replacement. [4] already clean those category trees to induce a class hierarchy. After this step, each KG has a class hierarchy which needs to be merged to create a huge consolidated hierarchy which does not need to be manually created (like in DBpedia).…”

Section: Discussionmentioning

confidence: 99%

“…As an example William Riker (the fictional character in the Star Trek universe) appears in Wikipedia because this character is famous enough to be added (see also the notability criterion for people 3 ). For other characters, like his mother Betty Riker, this notability is not given, so it only appears in special wikis like memory alpha 4 (a Star Trek wiki in Fandom).…”

Section: Introductionmentioning

confidence: 99%

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DBkWik++ -- Multi Source Matching of Knowledge Graphs

Hertling¹,

Paulheim²

2022

Preprint

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Large knowledge graphs like DBpedia and YAGO are always based on the same source, i.e., Wikipedia. But there are more wikis that contain information about long-tail entities such as wiki hosting platforms like Fandom. In this paper, we present the approach and analysis of DBkWik++, a fused Knowledge Graph from thousands of wikis. A modified version of the DBpedia framework is applied to each wiki which results in many isolated Knowledge Graphs. With an incremental merge based approach, we reuse one-to-one matching systems to solve the multi source KG matching task. Based on this alignment we create a consolidated knowledge graph with more than 15 million instances.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DBkWik++ -- Multi Source Matching of Knowledge Graphs

Hertling¹,

Paulheim²

2022

Preprint

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“…Thus, stories inside Mahabharata are always being attractive for creative people. This Epic is even inspiring for the technologist to create various taxonomies for the fictional domain (TiFI) [8] and launch 'ENTYFI' -the first technique for typing entities in fictional text. This 5-steps technique is useful to generate supervise fiction typing, supervised real-world typing and unsupervised typing [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Related Words Computationally Using Language Model from the Mahabharata an Indian Epic

Gadesha¹,

Joshi²,

Naik³

2022

Lecture Notes in Networks and Systems

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Mahabharata' is the most popular among many Indian pieces of literature referred to in many domains for completely different purposes. This text itself is having various dimension and aspects which is useful for the human being in their personal life and professional life. This Indian Epic is originally written in the Sanskrit Language. Now in the era of Natural Language Processing, Artificial Intelligence, Machine Learning, and Human-Computer interaction this text can be processed according to the domain requirement. It is interesting to process this text and get useful insights from Mahabharata. The limitation of the humans while analyzing Mahabharata is that they always have a sentiment aspect towards the story narrated by the author. Apart from that, the human cannot memorize statistical or computational details, like which two words are frequently coming in one sentence? What is the average length of the sentences across the whole literature? Which word is the most popular word across the text, what are the lemmas of the words used across the sentences? Thus, in this paper, we propose an NLP pipeline to get some statistical and computational insights along with the most relevant word searching method from the largest epic 'Mahabharata'. We stacked the different text-processing approaches to articulate the best results which can be further used in the various domain where Mahabharata needs to be referred.

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“…More exotic verticals to which the Wikipedia-inspired methodology has been carried over are fictional universes such as Game of Thrones, the Simpsons, etc., with input from rich category systems by fan-community wikis (https://www.fandom.com/). Recent research on this topic includes works by [231] and [97]. Finally, another non-standard theme for KB construction is how-to knowledge: organizing human tasks and procedures for solving them in a principled taxonomy.…”

Section: Beyond Wikipediamentioning

confidence: 99%

“…Wikia), which have collaboratively built extensive but noisy category and tagging systems for entertainment fiction like movie series or TV series (e.g., Lord of the Rings, Game of Thrones, The Simpsons etc.) [97,231].…”

Section: Folksonomies From Social Tagsmentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

Self Cite

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Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

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TiFi: Taxonomy Induction for Fictional Domains

Cited by 8 publications

References 34 publications

DBkWik++ -- Multi Source Matching of Knowledge Graphs

DBkWik++ -- Multi Source Matching of Knowledge Graphs

Estimating Related Words Computationally Using Language Model from the Mahabharata an Indian Epic

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

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