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Article
Publication date: 9 August 2021

Xintong Zhao, Jane Greenberg, Vanessa Meschke, Eric Toberer and Xiaohua Hu

The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including…

Abstract

Purpose

The output of academic literature has increased significantly due to digital technology, presenting researchers with a challenge across every discipline, including materials science, as it is impossible to manually read and extract knowledge from millions of published literature. The purpose of this study is to address this challenge by exploring knowledge extraction in materials science, as applied to digital scholarship. An overriding goal is to help inform readers about the status knowledge extraction in materials science.

Design/methodology/approach

The authors conducted a two-part analysis, comparing knowledge extraction methods applied materials science scholarship, across a sample of 22 articles; followed by a comparison of HIVE-4-MAT, an ontology-based knowledge extraction and MatScholar, a named entity recognition (NER) application. This paper covers contextual background, and a review of three tiers of knowledge extraction (ontology-based, NER and relation extraction), followed by the research goals and approach.

Findings

The results indicate three key needs for researchers to consider for advancing knowledge extraction: the need for materials science focused corpora; the need for researchers to define the scope of the research being pursued, and the need to understand the tradeoffs among different knowledge extraction methods. This paper also points to future material science research potential with relation extraction and increased availability of ontologies.

Originality/value

To the best of the authors’ knowledge, there are very few studies examining knowledge extraction in materials science. This work makes an important contribution to this underexplored research area.

Details

The Electronic Library , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 3 August 2021

Chuanming Yu, Haodong Xue, Manyi Wang and Lu An

Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource…

Abstract

Purpose

Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages.

Design/methodology/approach

This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction.

Findings

The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages.

Originality/value

The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.

Details

The Electronic Library , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 13 April 2021

Mariana Souza Rocha, Luiz Célio Souza Rocha, Marcia Barreto da Silva Feijó, Paula Luiza Limongi dos Santos Marotta and Samanta Cardozo Mourão

The mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential…

Abstract

Purpose

The mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential applications in both food and pharmaceutical industries. To increase the yield and quality of linseed oil during its production process, it is necessary to previously extract its polysaccharides. Because of this, flax mucilage production can be made viable as a byproduct of oil extraction process, which is already a product of high commercial value consolidated in the market. Thus, the purpose of this work is to optimize the mucilage extraction process of L. usitatissimum L. using the normal-boundary intersection (NBI) multiobjective optimization method.

Design/methodology/approach

Currently, the variables of the process of polysaccharide extraction from different sources are optimized using the response surface methodology. However, when the optimal points of the responses are conflicting it is necessary to study the best conditions to achieve a balance between these conflicting objectives (trade-offs) and to explore the available options it is necessary to formulate an optimization problem with multiple objectives. The multiobjective optimization method used in this work was the NBI developed to find uniformly distributed and continuous Pareto optimal solutions for a nonlinear multiobjective problem.

Findings

The optimum extraction point to obtain the maximum fiber concentration in the extracted material was pH 3.81, temperature of 46°C, time of 13.46 h. The maximum extraction yield of flaxseed was pH 6.45, temperature of 65°C, time of 14.41 h. This result confirms the trade-off relationship between the objectives. NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows to analyze the behavior of the trade-off relationship. Thus, the decision-maker can set extraction conditions to achieve desired characteristics in mucilage.

Originality/value

The novelty of this paper is to confirm the existence of a trade-off relationship between the productivity parameter (yield) and the quality parameter (fiber concentration in the extracted material) during the flaxseed mucilage extraction process. The NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows us to analyze the behavior of the trade-off relationship. This allows the decision-making to the extraction conditions according to the desired characteristics of the final product, thus being able to direct the extraction for the best applicability of the mucilage.

Details

British Food Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 2 December 2020

Yohanes Sigit Purnomo W.P., Yogan Jaya Kumar and Nur Zareen Zulkarnain

Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is…

Abstract

Purpose

Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.

Design/methodology/approach

This paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research.

Findings

There are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed.

Originality/value

The paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.

Details

Global Knowledge, Memory and Communication, vol. 70 no. 6/7
Type: Research Article
ISSN: 2514-9342

Keywords

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Article
Publication date: 24 June 2020

Yilu Zhou and Yuan Xue

Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual…

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137

Abstract

Purpose

Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.

Design/methodology/approach

The proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.

Findings

In evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.

Originality/value

The research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.

Details

Information Technology & People, vol. 33 no. 5
Type: Research Article
ISSN: 0959-3845

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Article
Publication date: 27 January 2020

Anshu Sharma, Anju Kumari Dhiman and Surekha Attri

Internal fluffy portion along with fibrous strands of ripe pumpkin is considered as waste in processing industries though it contains sufficient amount of ß-carotene…

Abstract

Purpose

Internal fluffy portion along with fibrous strands of ripe pumpkin is considered as waste in processing industries though it contains sufficient amount of ß-carotene pigment. The purpose of this paper is to use the leftover fluffy portion of ripe pumpkin (Cucurbita maxima) after the use of its flesh for the purpose of processing.

Design/methodology/approach

The data were analyzed statistically by following a complete randomized design (CRD). All analysis were performed using the software OPSTAT.

Findings

One hour pre-enzymatic treatment before solvent extraction showed significant improvement in extraction yield in comparison to the isolation of ß-carotene pigment through solvent only. Temperature time combination was optimized as 40°C for 2 h during solvent extraction to obtain maximum yield irrespective of the type of extraction method used.

Practical implications

Extracted carotene pigment can further be used as a natural food colorant in processed food products not only to enhance the color appeal but also it improves the nutritional value of the product as ß-carotene acts as a precursor of vitamin A.

Social implications

Coloring agents of natural origin are becoming famous among society due to their health benefits. Consumers are becoming reluctant to use synthetic colors because of the undesirable allergic reactions caused by them, so carotene bio-pigment produced is a natural coloring compound with wide application in the food sector.

Originality/value

Even though few researchers have worked on the extraction of carotene pigment from pumpkin, but no researcher has reported the use of a waste fluffy portion of C. maxima for extraction of ß-carotene pigment.

Details

Pigment & Resin Technology, vol. 49 no. 2
Type: Research Article
ISSN: 0369-9420

Keywords

Content available
Article
Publication date: 14 August 2017

Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Anne H.H. Ngu and Yihong Zhang

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

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1746

Abstract

Purpose

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

Design/methodology/approach

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Findings

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

Originality/value

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Details

PSU Research Review, vol. 1 no. 2
Type: Research Article
ISSN: 2399-1747

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Article
Publication date: 3 April 2017

Adrián Rabadán, Manuel Álvarez-Ortí, José E. Pardo, Ricardo Gómez, Arturo Pardo-Giménez and Miguel Olmeda

The high content of unsaturated fatty acids and the elevated presence of bioactive compounds make pistachio oil a healthy product with great commercial potential. One of…

Abstract

Purpose

The high content of unsaturated fatty acids and the elevated presence of bioactive compounds make pistachio oil a healthy product with great commercial potential. One of the primary constraints for its production is the lack of information regarding oil extraction from an industrial perspective. The purpose of this paper is to ensure the success of pistachio oil production at a commercial scale, attention should be paid to the effect of the main extraction procedures on the characteristics of oil, the consumer acceptance of these oils and their production cost.

Design/methodology/approach

Comparison and evaluation of the physicochemical and sensory characteristics and production cost of oil extracted using two different production lines (hydraulic press and screw press) are considered here.

Findings

Slight differences were found in the physicochemical analysis, but significant differences were identified in the sensory analysis. Consumer judges preferred the oil extracted with the hydraulic press. According to production costs, the break-even value that makes screw press extraction sustainable is €70.4 per litre, while for the hydraulic press it is €91.0 per litre, mainly due to a lower extraction yield and the longer extraction time required. As production costs of both methods are high, pistachio oil quality should prevail, making the use of the hydraulic press more advisable.

Originality/value

Although significant research has been conducted to analyse pistachio oil composition and nutritional value, little attention has been paid to differences that appear regarding consumer preferences and production costs depending on the production method used. This paper provides a comprehensive approach to high-quality pistachio oil production from an industrial perspective.

Details

British Food Journal, vol. 119 no. 4
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 14 August 2017

Sudeep Thepade, Rik Das and Saurav Ghosh

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image…

Abstract

Purpose

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Design/methodology/approach

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

Findings

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

Originality/value

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 3
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 9 May 2019

Sawinder Kaur, Paramjit S. Panesar, Sushma Gurumayum, Prasad Rasane and Vikas Kumar

The extraction of bioactive compounds such as pigments from natural sources, using different solvents, is a vital downstream process. The present study aims to investigate…

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96

Abstract

Purpose

The extraction of bioactive compounds such as pigments from natural sources, using different solvents, is a vital downstream process. The present study aims to investigate the effect of different variables, namely, extraction temperature, mass of fermented rice and time on the extraction process of orevactaene and flavanoid pigment from Epicoccum nigrum fermented broken rice.

Design/methodology/approach

Central composite rotatable design under response surface methodology was used for deducing optimized conditions. The pigments were extracted under conditions of extraction temperature (40-70°C), mass of fermented rice (0.5-1.5 g) and time (30-90 min), using water as the extraction media. The experimental data obtained were studied by analysis of variance. Data were fitted to a second-order polynomial equation using multiple regression analysis.

Findings

The optimum conditions generated by the software for aqueous extraction process, i.e. extraction temperature of 55.7°C, 0.79 g of fermented matter and extraction time of 56.6 min, resulted in a pigment yield of 52.7AU/g orevactaene and 77.2 AU/g flavanoid.

Research limitations/implications

The developed polynomial empirical model for the optimal recovery of the orevactaene and flavanoid pigments could be used for further studies in prediction of yield under specified variable conditions.

Practical implications

The response surface methodology helped in optimizng the conditions for the eco-friendly low-cost aqueous extarction process for orevactaene and flavanoid pigments, produced by Epicoccum nigrum during solid state fermentation of broken rice. This optimization can provide the basis for scaling up for industrial extraction process.

Originality/value

This paper focuses on optimizing the extraction conditions to get the maximum yield of orevactaene and flavanoid pigments, using water as the extracting media. No literature is available on the optimization of the extraction process of Epicoccum nigrum pigments, to the best of the authors’ knowledge.

Details

Pigment & Resin Technology, vol. 48 no. 4
Type: Research Article
ISSN: 0369-9420

Keywords

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