scholarly journals Keyword Query Expansion on Linked Data Using Linguistic and Semantic Features

2013 ◽  
Author(s):  
Saeedeh Shekarpour ◽  
Konrad Hoffner ◽  
Jens Lehmann ◽  
Soren Auer
Keyword(s):  
Linked Data ◽  
Query Expansion ◽  
Semantic Features ◽  
Keyword Query

scholarly journals Query Expansion for Slovak to Bulgarian Language Machine Translation using Parallel Search

2021 ◽  
pp. 351-357
Author(s):  
VELISLAVA STOYKOVA ◽  
DANIELA MAJCHRAKOVA
Keyword(s):  
Machine Translation ◽  
Query Expansion ◽  
Statistical Approach ◽  
Semantic Relations ◽  
Target Language ◽  
Parallel Search ◽  
Keyword Query ◽  
Source Language ◽  
Standard Presentation ◽  
Standard Semantic

The paper presents results of the application of a statistical approach for Slovak to Bulgarian language machine translation. It uses Information Retrieval inspired search techniques and employs sever alalgorithmic steps of parallel statistical search with query expansion in Slovak-Bulgarian EUROPARL 7 Corpus using the Sketch Engine software and its scoring. The search includes the generation of concordances,collocations, word sketch differences, word sketches, and thesauri of the studied keyword (query) by using a statistical scoring, which is regarded as intermediate (inter-lingual) semantic standard presentation by means of which the studied keyword (from the source language) is mapped together with its possible translation equivalents (onto the target language. The results present the study of adjectival collocabillity in both Slovak and Bulgarian language from the corpus of political speech texts outlining the standard semantic relations based on the evaluation of statistical scoring. Finally, the advantages and shortcomings of the approach are discussed.

The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features

2021 ◽  
pp. 563-579
Author(s):  
Evan W. Patton ◽  
William Van Woensel ◽  
Oshani Seneviratne ◽  
Giuseppe Loseto ◽  
Floriano Scioscia ◽  
...  
Keyword(s):  
Linked Data ◽  
Semantic Features

scholarly journals Mapping Keywords to Linked Data Resources for Automatic Query Expansion

2013 ◽  
pp. 101-112 ◽  
Author(s):  
Isabelle Augenstein ◽  
Anna Lisa Gentile ◽  
Barry Norton ◽  
Ziqi Zhang ◽  
Fabio Ciravegna
Keyword(s):  
Linked Data ◽  
Query Expansion

scholarly journals Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yingqi Wang ◽  
Nianbin Wang ◽  
Lianke Zhou
Keyword(s):  
Real World ◽  
Relational Databases ◽  
Query Expansion ◽  
Semantic Information ◽  
Experimental Results ◽  
Similarity Matrix ◽  
Recommendation Algorithm ◽  
Keyword Query ◽  
Query Recommendation ◽  
Term Similarity

Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.

scholarly journals Semantic and syntactic information for neural machine translation

2021 ◽  
Author(s):  
Jordi Armengol-Estapé ◽  
Marta R. Costa-jussà
Keyword(s):  
Machine Translation ◽  
Linked Data ◽  
Semantic Information ◽  
Semantic Features ◽  
Linguistic Features ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Syntactic Information ◽  
Attentional System

AbstractIntroducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.

scholarly journals Health Query Expansion based on Graph Matching between DBpedia and UMLS

2021 ◽  
Vol 17 (06) ◽  
pp. 110
Author(s):  
Sarah Dahir ◽  
Abderrahim El Qadi ◽  
Hamid Bennis
Keyword(s):  
Linked Data ◽  
Query Expansion ◽  
Graph Matching ◽  
Graph Connectivity ◽  
Sufficient Information ◽  
Retrieval Model ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Medical Domain ◽  
Technical Terms

<p class="0abstract">Information Retrieval (IR) in the medical domain is considered as a challenging task for many reasons. Short health queries tend to lack information on user's intent, and the target corpus may not have sufficient information for Relevance Feedbacks. And even, if the user obtains relevant documents to his/her queries, it is difficult for him/her to understand the technical terms.  In contrast, in this paper, we propose an approach for health queries reformulation based on graph matching between two external linked data sources: DBpedia and Unified Medical Language System (UMLS). DBpedia has a broad coverage of topics and less noise compared to Wikipedia articles, and UMLS is specific to the medical domain. We also introduced the degree centrality to measure the graph connectivity and to select the most efficient candidate terms for query expansion. Experimental results on MEDLINE collection using Okapi BM25 as a retrieval model showed that our approach outperformed related methods, and the two sources achieved very good retrieval results. They helped in the diversification of the retrieved documents and the improvement of the recall.</p>

Measure-driven keyword-query expansion

2009 ◽  
Vol 2 (1) ◽  
pp. 121-132 ◽  
Author(s):  
Nikos Sarkas ◽  
Nilesh Bansal ◽  
Gautam Das ◽  
Nick Koudas
Keyword(s):  
Query Expansion ◽  
Keyword Query
Sign in / Sign up

Export Citation Format

Share Document