Automated conversion from natural language query to SPARQL query

Jung, Haemin; Kim, Wooju

doi:10.1007/s10844-019-00589-2

Cited by 21 publications

(12 citation statements)

References 4 publications

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“…Retrieval is then executed similarly to the retrieval of video feature information. • Audio: for audio processing, we apply NLP algorithms as illustrated in [41], which produce a textual representation of spoken words. This textual representation can be processed similar to documents in terms of MMFGs and Graph Codes.…”

Section: Discussionmentioning

confidence: 99%

“…A manual construction of a MMFG Query by users can result in a GC Query Graph Code, which then is employed for querying. This manual construction could be performed by entering keywords, structured queries (e.g., in a query language like SPARQL [40]), or also natural language based commands [41] into a MMIR application's query user interface. The MMFG Query and corresponding GC Query in this case is created completely from scratch.…”

Section: Querying With Graph Codesmentioning

confidence: 99%

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Fast and Effective Retrieval for Large Multimedia Collections

Wagenpfeil

Kevitt³

et al. 2021

BDCC

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The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.

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Section: Discussionmentioning

confidence: 99%

Section: Querying With Graph Codesmentioning

confidence: 99%

Fast and Effective Retrieval for Large Multimedia Collections

Wagenpfeil

Kevitt³

et al. 2021

BDCC

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“…NLP can facilitate processing the types of queries discussed in the previous section. In general, we follow the approach given in [82] to generate SPARQL-Queries based on NLP, but incorporate an additional component to generate a simple Graph Code based on the keywords of a SPARQL-query, which then facilitates fast Graph-Code-Retrieval.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

“…(The prototype is designed to support natural language input via speech-to-text and translate it into RDF. [82] gives an approach towards a corresponding solution, but is currently not yet implemented in the prototype.) The prototype has uncovered the following findings:…”

Section: Swift/ui5-prototypementioning

confidence: 99%

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

Wagenpfeil

Engel

Kevitt³

et al. 2021

Information

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To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

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“…A study explaining how deep learning models can be used for the classification of a question in the Turkish language was made, in which the structure of a word is obtained by appending suffixes to root [4]. Jung and Kim [5] demonstrated a method for SPARQL queries generation from natural language questions present in Korean. Song et al [6] proposed text matching models like triple CNN and two attention based triple CNN models for improving IR based QA system for e-commerce-AliMe.…”

Section: Related Workmentioning

confidence: 99%

Query-Based Retrieval Using Universal Sentence Encoder

Deepthi¹,

Sowjanya²

2021

RIA

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

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Automated conversion from natural language query to SPARQL query

Cited by 21 publications

References 4 publications

Fast and Effective Retrieval for Large Multimedia Collections

Fast and Effective Retrieval for Large Multimedia Collections

AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

Query-Based Retrieval Using Universal Sentence Encoder

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