A Meta-Learning Architecture based on Linked Data

Santos, Ricardo Dos; Aguilar, José; Puerto, Eduard

doi:10.1109/clei53233.2021.9640223

Cited by 3 publications

(2 citation statements)

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“…Linked data (LD) is a way of data publishing on the Web, so that they can interconnect between them, allowing them to identify, describe, connect and relate the different elements or concepts on the Web (Berners‐Lee et al, 2006; Dos Santos & Aguilar, 2018; Dos Santos et al, 2021; Rodríguez et al, 2017). At present, the linked open data (LOD) clouds cover many domains, from structured information about life sciences (diseases, symptoms, among others) and media (movies, books, etc.…”

Section: Introductionmentioning

confidence: 99%

“…), to geographic and government data (Ristoski, 2019). These clouds provide a huge interconnected and decentralized knowledge base, which is readable by people and machines, but requires services and applications that make intensive use of all this knowledge (Dos Santos et al, 2021). Recommender systems (RSs) can benefit from all this information contained in the LOD cloud, by exploiting the non‐trivial connections encoded in the cloud.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid recommender system based on description/dialetheic logic and linked data

Santos

Aguilar

2022

Expert Systems

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In this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%