KNITTIR: Syntactical text indexing for analytics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AWWSYK7YA" target="_blank" >RIV/00216208:11320/26:WWSYK7YA - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3677389.3702486" target="_blank" >http://dx.doi.org/10.1145/3677389.3702486</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3677389.3702486" target="_blank" >10.1145/3677389.3702486</a>

Jazyk výsledku

angličtina

Název v původním jazyce

KNITTIR: Syntactical text indexing for analytics

Popis výsledku v původním jazyce

Scalable text analytics requires retrieval of similar text regions spread across millions of documents. It also requires that we can reason about entities by categorizing them; contrasting them to other entities; and ranking them using time and numbers. We present knittir that assists in such complex text analytical tasks. knittir uses semantic annotations such as parts-of-speech, named entities, and their syntactical relationships to words in text. To simplify text analytics, knittir uses a new search framework wherein a vertical partitioning of semantically annotated text is used. This allows users to aggregate and manipulate evidences to their queries from multiple text regions spread across millions of documents. To scale analytical queries, knittir creates indexes using the syntactical modeling of annotated text. Our experiments over 22 million documents show that knittir obtains speedups of up to 60× for performing similarity search and up to 69K× for reasoning tasks. © 2024 Copyright held by the owner/author(s).

Název v anglickém jazyce

KNITTIR: Syntactical text indexing for analytics

Popis výsledku anglicky

Scalable text analytics requires retrieval of similar text regions spread across millions of documents. It also requires that we can reason about entities by categorizing them; contrasting them to other entities; and ranking them using time and numbers. We present knittir that assists in such complex text analytical tasks. knittir uses semantic annotations such as parts-of-speech, named entities, and their syntactical relationships to words in text. To simplify text analytics, knittir uses a new search framework wherein a vertical partitioning of semantically annotated text is used. This allows users to aggregate and manipulate evidences to their queries from multiple text regions spread across millions of documents. To scale analytical queries, knittir creates indexes using the syntactical modeling of annotated text. Our experiments over 22 million documents show that knittir obtains speedups of up to 60× for performing similarity search and up to 69K× for reasoning tasks. © 2024 Copyright held by the owner/author(s).

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proc. ACM IEEE Joint Conf. Digit. Libr.

ISBN

979-8-4007-1093-3

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

1-12

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

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Místo konání akce

Hong Kong

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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