Towards a distributional semantic web stack

André Freitas; Edward Curry; Siegfried Handschuh

Towards a distributional semantic web stack

André Freitas
, Edward Curry
, Siegfried Handschuh

Computer Science

University of Galway
University of Passau

Research output: Contribution to a Journal (Peer & Non Peer) › Conference article › peer-review

2 Citations (Scopus)

Abstract

The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.

Original language	English
Pages (from-to)	49-52
Number of pages	4
Journal	CEUR Workshop Proceedings
Volume	1259
Publication status	Published - 2014
Event	10th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2014, Collocated with the 13th International Semantic Web Conference, ISWC 2014 - Riva del Garda, Italy Duration: 19 Oct 2014 → 19 Oct 2014

Cite this

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title = "Towards a distributional semantic web stack",

abstract = "The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.",

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TY - JOUR

T1 - Towards a distributional semantic web stack

AU - Freitas, André

AU - Curry, Edward

AU - Handschuh, Siegfried

PY - 2014

Y1 - 2014

N2 - The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.

AB - The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.

UR - https://www.scopus.com/pages/publications/84918832776

M3 - Conference article

AN - SCOPUS:84918832776

SN - 1613-0073

VL - 1259

SP - 49

EP - 52

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 10th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2014, Collocated with the 13th International Semantic Web Conference, ISWC 2014

Y2 - 19 October 2014 through 19 October 2014

ER -

Towards a distributional semantic web stack

Abstract

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