کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
557706 1451608 2016 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs
ترجمه فارسی عنوان
نمودارهای Sar: یک منبع زبان اتصال دهنده دانش زبانی با روابط معنایی از نمودارهای دانش
کلمات کلیدی
نمودارهای دانش؛ منابع زبان؛ الگوهای زبانی؛ استخراج رابطه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی

Recent years have seen a significant growth and increased usage of large-scale knowledge resources in both academic research and industry. We can distinguish two main types of knowledge resources: those that store factual information about entities in the form of semantic relations (e.g., Freebase), namely so-called knowledge graphs, and those that represent general linguistic knowledge (e.g., WordNet or UWN). In this article, we present a third type of knowledge resource which completes the picture by connecting the two first types. Instances of this resource are graphs of semantically-associated relations (sar-graphs), whose purpose is to link semantic relations from factual knowledge graphs with their linguistic representations in human language.We present a general method for constructing sar-graphs using a language- and relation-independent, distantly supervised approach which, apart from generic language processing tools, relies solely on the availability of a lexical semantic resource, providing sense information for words, as well as a knowledge base containing seed relation instances. Using these seeds, our method extracts, validates and merges relation-specific linguistic patterns from text to create sar-graphs. To cope with the noisily labeled data arising in a distantly supervised setting, we propose several automatic pattern confidence estimation strategies, and also show how manual supervision can be used to improve the quality of sar-graph instances. We demonstrate the applicability of our method by constructing sar-graphs for 25 semantic relations, of which we make a subset publicly available at http://sargraph.dfki.de.We believe sar-graphs will prove to be useful linguistic resources for a wide variety of natural language processing tasks, and in particular for information extraction and knowledge base population. We illustrate their usefulness with experiments in relation extraction and in computer assisted language learning.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Web Semantics: Science, Services and Agents on the World Wide Web - Volumes 37–38, March 2016, Pages 112–131
نویسندگان
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