PANDA: A platform for academic knowledge discovery and acquisition

Dong, Zhaoan; Lu, Jiaheng; Ling, Tok Wang

doi:10.1109/bigcomp.2016.7425795

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Ambros und Biberhofer [26], on the other hand, investigate the use of a knowledge platform for sustainability-driven entrepreneurship. Dong et al [27] present a platform for academic knowledge discovery.…”

Section: Resultsmentioning

confidence: 99%

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Mapping the Platforms in Higher Education – A Systematic Literature Review

Reiche

2022

2022 IEEE European Technology and Engineering Management Summit (E-Tems)

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Platforms have determined the interaction and actions of many players for years. They have established themselves as leading business models in both the business-tobusiness and business-to-customer sectors. But not only companies use platforms. Increasingly, universities are also using platform models to generate value. This article uses a systematic literature review to examine 1289 publications in this area and classifies them in a concept matrix of the use of platforms in higher education to date. It shows that some areas of universities use platforms and platform ecosystems. The area of teaching benefits greatly from Massive Open Online Courses, which are provided on well-known platforms. Other areas, such as transfer or research, are underrepresented.

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Section: Resultsmentioning

confidence: 99%

“…It can be seen that research related to platforms is also gradually entering the field of higher education without only highlighting e-learning platforms. Research in the areas of transfer, research and other areas in the context of higher education is also gaining increasing attention [26]- [27], [45] - [48], [51], [53]- [54].…”

Section: Discussionmentioning

confidence: 99%

Mapping the Platforms in Higher Education – A Systematic Literature Review

Reiche

2022

2022 IEEE European Technology and Engineering Management Summit (E-Tems)

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“…Effectiveness. To evaluate the effectiveness of similarity measures, we applied a crowd-sourcing platform [17][18][19] to obtain 268 (MED dataset) and 961 (WIKI dataset) string pairs as ground truths. In MED, the inconsistencies are mainly due to synonyms (e.g., "mitral valve insufficiency" vs "bicuspid valves incompetence"), while in WIKI, we find typos and taxonomic-similar strings (e.g., "computer network, massively parallel computers" vs "computer networks, supercomputers").…”

Section: Evaluation Of Similarity Measuresmentioning

confidence: 99%

Towards a unified framework for string similarity joins

2019

Proc. VLDB Endow.

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A similarity join aims to find all similar pairs between two collections of records. Established algorithms utilise different similarity measures, either syntactic or semantic, to quantify the similarity between two records. However, when records are similar in forms of a mixture of syntactic and semantic relations, utilising a single measure becomes inadequate to disclose the real similarity between records, and hence unable to obtain high-quality join results. In this paper, we study a unified framework to find similar records by combining multiple similarity measures. To achieve this goal, we first develop a new similarity framework that unifies the existing three kinds of similarity measures simultaneously, including syntactic (typographic) similarity, synonym-based similarity, and taxonomy-based similarity. We then theoretically prove that finding the maximum unified similarity between two strings is generally NP-hard, and furthermore develop an approximate algorithm which runs in polynomial time with a non-trivial approximation guarantee. To support efficient string joins based on our unified similarity measure, we adopt the filter-and-verification framework and propose a new signature structure, called pebble, which can be simultaneously adapted to handle multiple similarity measures. The salient feature of our approach is that, it can judiciously select the best pebble signatures and the overlap thresholds to maximise the filtering power. Extensive experiments show that our methods are capable of finding similar records having mixed types of similarity relations, while exhibiting high efficiency and scalability for similarity joins. The implementation can be downloaded at https://github.com/HY-UDBMS/AU-Join.

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Using Crowdsourcing for Fine-Grained Entity Type Completion in Knowledge Bases

Dong

Fan

et al. 2018

Web and Big Data

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Recent years have witnessed the proliferation of large-scale Knowledge Bases (KBs). However, many entities in KBs have incomplete type information, and some are totally untyped. Even worse, fine-grained types (e.g., BasketballPlayer) containing rich semantic meanings are more likely to be incomplete, as they are more difficult to be obtained. Existing machine-based algorithms use predicates (e.g., birthPlace) of entities to infer their missing types, and they have limitations that the predicates may be insufficient to infer fine-grained types. In this paper, we utilize crowdsourcing to solve the problem, and address the challenge of controlling crowdsourcing cost. To this end, we propose a hybrid machine-crowdsourcing approach for fine-grained entity type completion. It firstly determines the types of some "representative" entities via crowdsourcing and then infers the types for remaining entities based on the crowdsourcing results. To support this approach, we first propose an embedding-based influence for type inference which considers not only the distance between entity embeddings but also the distances between entity and type embeddings. Second, we propose a new difficulty model for entity selection which can better capture the uncertainty of the machine algorithm when identifying the entity types. We demonstrate the effectiveness of our approach through experiments on real crowdsourcing platforms. The results show that our method outperforms the state-ofthe-art algorithms by improving the effectiveness of fine-grained type completion at affordable crowdsourcing cost.

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PANDA: A platform for academic knowledge discovery and acquisition

Cited by 3 publications

References 22 publications

Mapping the Platforms in Higher Education – A Systematic Literature Review

Mapping the Platforms in Higher Education – A Systematic Literature Review

Towards a unified framework for string similarity joins

Using Crowdsourcing for Fine-Grained Entity Type Completion in Knowledge Bases

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