Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 |
| Seiten | 1313-1316 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781450356572 |
| Publikationsstatus | Veröffentlicht - Juni 2018 |
| Veranstaltung | 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, USA / Vereinigte Staaten Dauer: 8 Juli 2018 → 12 Juli 2018 |
Publikationsreihe
| Name | ACM Digital Library |
|---|
Abstract
Question answering (QA) systems provide user-friendly interfaces for retrieving answers from structured and unstructured data given natural language questions. Several QA systems, as well as related components, have been contributed by the industry and research community in recent years. However, most of these efforts have been performed independently from each other and with different focuses, and their synergies in the scope of QA have not been addressed adequately. FRANKENSTEIN is a novel framework for developing QA systems over knowledge bases by integrating existing state-of-the-art QA components performing different tasks. It incorporates several reusable QA components, employs machine learning techniques to predict best performing components and QA pipelines for a given question, and generates static and dynamic executable QA pipelines. In this paper, we illustrate different functionalities of FRANKENSTEIN for performing independent QA component execution, QA component prediction, given an input question as well as the static and dynamic composition of different QA pipelines.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Information systems
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- BibTex
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41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018. 2018. S. 1313-1316 (ACM Digital Library).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Dynamic composition of question answering pipelines with Frankenstein
AU - Singh, Kuldeep
AU - Lytra, Ioanna
AU - Radhakrishna, Arun Sethupat
AU - Vyas, Akhilesh
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2018 Authors.
PY - 2018/6
Y1 - 2018/6
N2 - Question answering (QA) systems provide user-friendly interfaces for retrieving answers from structured and unstructured data given natural language questions. Several QA systems, as well as related components, have been contributed by the industry and research community in recent years. However, most of these efforts have been performed independently from each other and with different focuses, and their synergies in the scope of QA have not been addressed adequately. FRANKENSTEIN is a novel framework for developing QA systems over knowledge bases by integrating existing state-of-the-art QA components performing different tasks. It incorporates several reusable QA components, employs machine learning techniques to predict best performing components and QA pipelines for a given question, and generates static and dynamic executable QA pipelines. In this paper, we illustrate different functionalities of FRANKENSTEIN for performing independent QA component execution, QA component prediction, given an input question as well as the static and dynamic composition of different QA pipelines.
AB - Question answering (QA) systems provide user-friendly interfaces for retrieving answers from structured and unstructured data given natural language questions. Several QA systems, as well as related components, have been contributed by the industry and research community in recent years. However, most of these efforts have been performed independently from each other and with different focuses, and their synergies in the scope of QA have not been addressed adequately. FRANKENSTEIN is a novel framework for developing QA systems over knowledge bases by integrating existing state-of-the-art QA components performing different tasks. It incorporates several reusable QA components, employs machine learning techniques to predict best performing components and QA pipelines for a given question, and generates static and dynamic executable QA pipelines. In this paper, we illustrate different functionalities of FRANKENSTEIN for performing independent QA component execution, QA component prediction, given an input question as well as the static and dynamic composition of different QA pipelines.
KW - Qa framework
KW - Question answering
KW - Semantic search
KW - Semantic web
KW - Software reusability
UR - http://www.scopus.com/inward/record.url?scp=85051535657&partnerID=8YFLogxK
U2 - 10.1145/3209978.3210175
DO - 10.1145/3209978.3210175
M3 - Conference contribution
AN - SCOPUS:85051535657
T3 - ACM Digital Library
SP - 1313
EP - 1316
BT - 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
T2 - 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
Y2 - 8 July 2018 through 12 July 2018
ER -