3cixty: Building Comprehensive Knowledge Bases for City Exploration

Troncy, Raphaël; Rizzo, Giuseppe; Jameson, Anthony; Corcho, Óscar; Plu, Julien; Palumbo, Enrico; Hermida, Juan Carlos Ballesteros; Spirescu, Adrian; Barbu, Catalin; Kuhn, Kai-Dominik; Rossi, Matteo; Celino, Irene; Agarwal, Rachit; Scanu, Christian; Valla, Massimo; Haaker, Timber

doi:10.2139/ssrn.3199308

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…Differently from the current articles that directly approach objects of the real world (Farrokhnia, 2013;Schulz et al, 2015;Troncy et al, 2017), the MFSM explicitly differentiates the objects based on reality and their representation through data instances, which is consistent with the semiotic view of Ellestrõm (2014) that differentiates objects of their representation. Referring to the classes of entities level, the MFSM model follows the steps advanced by the entity-relationship model (Chen, 1976;Zhang et al, 2016), consolidated in the data modeling over the course of several decades.…”

Section: A Comparative Analysis Of the Mfsm With Current Modelssupporting

confidence: 59%

“…By comparing the MFSM with the content of 27 revised articles addressing semantic modeling, this model is more comprehensive than 26 of them insofar as it proposes four levels of semantic modeling. As such, in an interpretative view, we consider that only Troncy et al (2017) address in a subjacent form the four modeling levels in an article that presents an application for finding events and places in a city. In contrast with the Troncy paper (that presents an application), in this paper we outline the MFSM with its conceptual and theoretical underpinnings in order to focus more on the model than on its potential applications.…”

Section: A Comparative Analysis Of the Mfsm With Current Modelsmentioning

confidence: 99%

“…For instance, in addition to the fact that a given car can be easily identified through the three previous processes, they do not enable the association of this same car with domains such as all existing types of cars, all types of vehicles, or all types of transport systems, or with the specific spatial-temporal domain in which it is inserted. Thus, it is important to consider that besides its intrinsic meaning, an object may be associated with different domains that can be purely thematic, or spatial-temporal (Bastos et al, 2018;Kim et al, 2018;Pfaff et al, 2018;Sladic et al, 2017;Troncy et al, 2017). Thus, the domains are important elements to show the relative positioning of a given object in the semantic context [5] to which it is related.…”

mentioning

confidence: 99%

“…In addition to the classification process, some articles also present the signification of an object inside a domain. The broadness of each model varies as follows: classification plus contextualization plus decomposition (Dadzie et al, 2018); classification plus contextualization (Dijkshoorn et al, 2018;Gursoy et al, 2018;Lee and Robinson, 2018;Madden et al, 2013;Schulz et al, 2015) Troncy et al (2017, which is the only paper that addresses the four processes.…”

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confidence: 99%

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A multi-layer framework for semantic modeling

Silva¹,

Reis²,

Fernandes

et al. 2019

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Purpose The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization. Design/methodology/approach The deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science. Findings The MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search. Research limitations/implications The use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users. Practical implications The MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches. Social implications The MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users. Originality/value The MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.

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Section: A Comparative Analysis Of the Mfsm With Current Modelssupporting

confidence: 59%

Section: A Comparative Analysis Of the Mfsm With Current Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A multi-layer framework for semantic modeling

Silva¹,

Reis²,

Fernandes

et al. 2019

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“…The generation of each city-driven 3cixty knowledge base follows a strict data integration pipeline, that ranges from the definition of the data model, the selection of the primary sources used to populate the knowledge base, till the entity matching used for distilling the entities forming the final stream of cleaned data that is then presented to the users via multi-platform user interfaces. A detailed description of the technology leading to the creation of the knowledge base is summarized in [59]. In the remainder of this section we introduce the data model and the data sources used in 3cixty.…”

Section: Cixty Knowledge Base Generationmentioning

confidence: 99%

STEM: Stacked threshold-based entity matching for knowledge base generation

Palumbo

Rizzo

Troncy

2018

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One of the major issues encountered in the generation of knowledge bases is the integration of data coming from a collection of heterogeneous data sources. A key essential task when integrating data instances is the entity matching. Entity matching is based on the definition of a similarity measure among entities and on the classification of the entity pair as a match if the similarity exceeds a certain threshold. This parameter introduces a trade-off between the precision and the recall of the algorithm, as higher values of the threshold lead to higher precision and lower recall, and lower values lead to higher recall and lower precision. In this paper, we propose a stacking approach for threshold-based classifiers. It runs several instances of classifiers corresponding to different thresholds and use their predictions as a feature vector for a supervised learner. We show that this approach is able to break the trade-off between the precision and recall of the algorithm, increasing both at the same time and enhancing the overall performance of the algorithm. We also show that this hybrid approach performs better and is less dependent on the amount of available training data with respect to a supervised learning approach that directly uses properties' similarity values. In order to test the generality of the claim, we have run experimental tests using two different threshold-based classifiers on two different data sets. Finally, we show a concrete use case describing the implementation of the proposed approach in the generation of the 3cixty Nice knowledge base.

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Building an Ecosystem for the Tyrolean Tourism Knowledge Graph

Kärle

Şimşek

Panasiuk

et al. 2018

Lecture Notes in Computer Science

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The introduction of the schema.org vocabulary was a big step towards making websites machine read-and understandable. Due to schema.org's RDF-like nature storing annotations in a graph database is easy and efficient. In this paper the authors show how they gather touristic data in the Austrian region of Tirol and provide this data publicly in a knowledge graph. The definition of subsets of the vocabulary is followed by providing means to map data sources efficiently to schema.org and then store the annotated content into the graph. To showcase the consumption of the touristic data four scenarios are described which use the knowledge graph for real life applications and data analysis.

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3cixty: Building Comprehensive Knowledge Bases for City Exploration

Cited by 4 publications

References 10 publications

A multi-layer framework for semantic modeling

A multi-layer framework for semantic modeling

STEM: Stacked threshold-based entity matching for knowledge base generation

Building an Ecosystem for the Tyrolean Tourism Knowledge Graph

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