The spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.

An explainable data-driven approach to web directory taxonomy mapping / Daraio, Elena; Cagliero, Luca; Chiusano, SILVIA ANNA; Garza, Paolo; Ricupero, Giuseppe. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - ELETTRONICO. - 176:(2020), pp. 1101-1110. ((Intervento presentato al convegno 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems tenutosi a Verona (IT) nel 16-18 september 2020 [10.1016/j.procs.2020.09.106].

An explainable data-driven approach to web directory taxonomy mapping

Elena Daraio;Luca Cagliero;Silvia Anna Chiusano;Paolo Garza;Giuseppe Ricupero
2020

Abstract

The spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.
File in questo prodotto:
File Dimensione Formato  
k20gen-192.pdf

non disponibili

Descrizione: Articolo principale
Tipologia: 1. Preprint / submitted version [pre- review]
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 2.52 MB
Formato Adobe PDF
2.52 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
1-s2.0-S1877050920320068-main.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 1.94 MB
Formato Adobe PDF
1.94 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2844245