Knowledge extraction using semantic similarity of concepts from Web of Things knowledge bases

The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.

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Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

Applied Sciences ◽

10.3390/app9163318 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3318

Author(s):

Azmat Anwar ◽

Xiao Li ◽

Yating Yang ◽

Yajuan Wang

Keyword(s):

Semantic Similarity ◽

Knowledge Bases ◽

Considerable Effort ◽

Commonsense Knowledge ◽

Scoring Model ◽

Bilingual Dictionary ◽

Short Period ◽

Translation Ambiguity ◽

Back Translation ◽

Cross Lingual

Although considerable effort has been devoted to building commonsense knowledge bases (CKB), it is still not available for many low-resource languages such as Uyghur because of expensive construction cost. Focusing on this issue, we proposed a cross-lingual knowledge-projection method to construct an Uyghur CKB by projecting ConceptNet’s Chinese facts into Uyghur. We used a Chinese–Uyghur bilingual dictionary to get high-quality entity translation in facts and employed a back-translation method to eliminate the entity-translation ambiguity. Moreover, to tackle the inner relation ambiguity in translated facts, we made a hand-crafted rule to convert the structured facts into natural-language phrases and built the Chinese–Uyghur lingual phrases based on the similarity of phrases that corresponded to the bilingual semantic similarity scoring model. Experimental results show that the accuracy of our semantic similarity scoring model reached 94.75% for our task, and they successfully project 55,872 Chinese facts into Uyghur as well as obtain 67,375 Uyghur facts within a very short period.

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How to Understand Better “Smart Vehicle”? Knowledge Extraction for the Automotive Sector Using Web of Things

Semantic IoT: Theory and Applications - Studies in Computational Intelligence ◽

10.1007/978-3-030-64619-6_13 ◽

2021 ◽

pp. 303-321

Author(s):

Mahda Noura ◽

Amélie Gyrard ◽

Benjamin Klotz ◽

Raphael Troncy ◽

Soumya Kanti Datta ◽

...

Keyword(s):

Knowledge Extraction ◽

Automotive Sector ◽

Web Of Things ◽

Smart Vehicle

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Geographic knowledge extraction and semantic similarity in OpenStreetMap

Knowledge and Information Systems ◽

10.1007/s10115-012-0571-0 ◽

2012 ◽

Vol 37 (1) ◽

pp. 61-81 ◽

Cited By ~ 72

Author(s):

Andrea Ballatore ◽

Michela Bertolotto ◽

David C. Wilson

Keyword(s):

Semantic Similarity ◽

Knowledge Extraction ◽

Geographic Knowledge

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Automatic Knowledge Extraction to Build Semantic Web of Things Applications

IEEE Internet of Things Journal ◽

10.1109/jiot.2019.2918327 ◽

2019 ◽

Vol 6 (5) ◽

pp. 8447-8454 ◽

Cited By ~ 6

Author(s):

Mahda Noura ◽

Amelie Gyrard ◽

Sebastian Heil ◽

Martin Gaedke

Keyword(s):

Semantic Web ◽

Knowledge Extraction ◽

Web Of Things

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Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Natural Language Engineering ◽

10.1017/s1351324921000279 ◽

2021 ◽

pp. 1-29

Author(s):

Dongqiang Yang ◽

Yanqin Yin

Keyword(s):

Semantic Similarity ◽

Word Frequency ◽

Similarity Measures ◽

Semantic Networks ◽

Knowledge Bases ◽

Fine Tuning ◽

Language Models ◽

Word Similarity ◽

Uniform Distance ◽

Measure Word

Abstract Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

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Using part-of-speech tags as deep-syntax indicators in determining short-text semantic similarity

Computer Science and Information Systems ◽

10.2298/csis131127082b ◽

2015 ◽

Vol 12 (1) ◽

pp. 1-31 ◽

Cited By ~ 6

Author(s):

Vuk Batanovic ◽

Dragan Bojic

Keyword(s):

Semantic Similarity ◽

Language Processing ◽

Knowledge Bases ◽

Bag Of Words ◽

Semantic Role ◽

Short Text ◽

Part Of Speech ◽

Syntactic Information ◽

Processing Resources ◽

Statistical Similarity

This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.

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