A DISTRIBUTIONAL STRUCTURED SEMANTIC SPACE FOR QUERYING RDF GRAPH DATA

2011 ◽  
Vol 05 (04) ◽  
pp. 433-462 ◽  
Author(s):  
ANDRÉ FREITAS ◽  
EDWARD CURRY ◽  
JOÃO GABRIEL OLIVEIRA ◽  
SEÁN O'RIAIN
Keyword(s):  
Vector Space ◽  
Data Model ◽  
Linked Data ◽  
Fundamental Problem ◽  
Semantic Space ◽  
Semantic Interpretation ◽  
Rdf Graph ◽  
Data Consumption ◽  
Model Independent ◽  
Rdf Data

The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a distributional structured semantic space which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The article analyzes the geometric aspects of the proposed space, providing its description as a distributional structured vector space, which is built upon the Generalized Vector Space Model (GVSM). The final semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.

scholarly journals Efficiently Processing and Storing Library Linked Data using Apache Spark and Parquet

2018 ◽  
Vol 37 (3) ◽  
pp. 29-49
Author(s):  
Kumar Sharma ◽  
Ujjal Marjit ◽  
Utpal Biswas
Keyword(s):  
Large Volume ◽  
Data Model ◽  
Experimental Evaluation ◽  
Linked Data ◽  
Storage Systems ◽  
Storage System ◽  
File Systems ◽  
Large Data ◽  
Apache Spark ◽  
Rdf Data

Resource Description Framework (RDF) is a commonly used data model in the Semantic Web environment. Libraries and various other communities have been using the RDF data model to store valuable data after it is extracted from traditional storage systems. However, because of the large volume of the data, processing and storing it is becoming a nightmare for traditional data-management tools. This challenge demands a scalable and distributed system that can manage data in parallel. In this article, a distributed solution is proposed for efficiently processing and storing the large volume of library linked data stored in traditional storage systems. Apache Spark is used for parallel processing of large data sets and a column-oriented schema is proposed for storing RDF data. The storage system is built on top of Hadoop Distributed File Systems (HDFS) and uses the Apache Parquet format to store data in a compressed form. The experimental evaluation showed that storage requirements were reduced significantly as compared to Jena TDB, Sesame, RDF/XML, and N-Triples file formats. SPARQL queries are processed using Spark SQL to query the compressed data. The experimental evaluation showed a good query response time, which significantly reduces as the number of worker nodes increases.

scholarly journals Towards the Representation of Etymological Data on the Semantic Web

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 304 ◽  
Author(s):  
Anas Khan
Keyword(s):  
Semantic Web ◽  
Data Model ◽  
Linked Data ◽  
New Model ◽  
Data Framework ◽  
Preceding Discussion ◽  
Rdf Data ◽  
Resource Data

In this article, we look at the potential for a wide-coverage modelling of etymological information as linked data using the Resource Data Framework (RDF) data model. We begin with a discussion of some of the most typical features of etymological data and the challenges that these might pose to an RDF-based modelling. We then propose a new vocabulary for representing etymological data, the Ontolex-lemon Etymological Extension (lemonETY), based on the ontolex-lemon model. Each of the main elements of our new model is motivated with reference to the preceding discussion.

scholarly journals Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1372
Author(s):  
Sanjanasri JP ◽  
Vijay Krishna Menon ◽  
Soman KP ◽  
Rajendran S ◽  
Agnieszka Wolk
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Semantic Space ◽  
Semantic Interpretation ◽  
Learning Approaches ◽  
Qualitative Comparison ◽  
Bilingual Dictionary ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Cross Lingual

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

System II: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

2008 ◽  
Author(s):  
Gang Wu ◽  
Juanzi Li ◽  
Jianqiang Hu ◽  
Kehong Wang
Keyword(s):  
Data Model ◽  
Rdf Data

scholarly journals Clustering of Authors’ Texts of English Fiction in the Vector Space of Semantic Fields

2014 ◽  
Vol 14 (3) ◽  
pp. 25-36
Author(s):  
Bohdan Pavlyshenko
Keyword(s):  
Cluster Analysis ◽  
Vector Space ◽  
Cluster Structure ◽  
Vector Space Model ◽  
Semantic Space ◽  
Field Vector ◽  
Semantic Field ◽  
Text Documents ◽  
Space Model ◽  
Semantic Fields

Abstract This paper describes the analysis of possible differentiation of the author’s idiolect in the space of semantic fields; it also analyzes the clustering of text documents in the vector space of semantic fields and in the semantic space with orthogonal basis. The analysis showed that using the vector space model on the basis of semantic fields is efficient in cluster analysis algorithms of author’s texts in English fiction. The study of the distribution of authors' texts in the cluster structure showed the presence of the areas of semantic space that represent the idiolects of individual authors. Such areas are described by the clusters where only one author dominates. The clusters, where the texts of several authors dominate, can be considered as areas of semantic similarity of author’s styles. SVD factorization of the semantic fields matrix makes it possible to reduce significantly the dimension of the semantic space in the cluster analysis of author’s texts. Using the clustering of the semantic field vector space can be efficient in a comparative analysis of author's styles and idiolects. The clusters of some authors' idiolects are semantically invariant and do not depend on any changes in the basis of the semantic space and clustering method.

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