Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Amith, Muhammad; Fujimoto, Kayo; Mauldin, Rebecca L.; Tao, Cui

doi:10.1186/s12911-020-01287-8

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…Other computational approaches include models based on Deep Learning ( 43 , 49 , 55 ), Convolutional Neural Network Ensemble ( 51 ), Decision Tree ( 47 ), RF ( 31 , 49 , 50 ), Gradient Boosting Machine ( 49 ), Extreme Gradient Boosting (XGBoost) ( 47 – 49 ), Autoregressive Integrated Moving Average (ARIMA) ( 33 , 38 , 41 ), ARIMA with Explanatory Variable ( 38 ), Decomposition ( 38 ), Generalized Estimating Equations ( 36 ), NLP ( 37 , 45 ), Ontology ( 42 ), Complex Networks ( 46 ), Knowledge Embedding Representation ( 40 ), Sexual Infections as Large-Scale Agent-based Simulation model ( 44 ), and Gray Model ( 39 ). Table 4 shows the techniques that obtained the best performances in each study and their respective values according to the metric used for evaluation.…”

Section: Resultsmentioning

confidence: 99%

Computational methods applied to syphilis: where are we, and where are we going?

Albuquerque,

Fernandes,

Barbalho

et al. 2023

Front. Public Health

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Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

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Section: Resultsmentioning

confidence: 99%

Computational methods applied to syphilis: where are we, and where are we going?

Albuquerque,

Fernandes,

Barbalho

et al. 2023

Front. Public Health

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“…This would provide more opportunities toward both speed-ups and semantic-based filtering. We are also investigating the use of alternative knowledge sources such as Google [1], Wikipedia [86], and FOAF [4] to acquire a wider word sense coverage, and explore our approach in practical applications, namely semantic-aware document and schema matching [82,83], RSS news feed merging [68,69], affective blog analysis [26,27], social event detection [3,5], and semantic relations' identification from social media data [2]. On the long run, we aim to investigate word embeddings and learning statistical distributions in a corpus [34,92], to infer semantics without the need for predefined knowledge bases.…”

Section: Discussionmentioning

confidence: 99%

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Tekli

Chbeir

2023

COMSIS J

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Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach.

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“…Among the languages used to specify ontologies [17][18][19][20][21][22], the following stand out: Loom, CycL, Ontolingua, XML Schema, RDF (Resource Description Framework), RDF Schema (or RDF-S) and OWL 2 (Web Ontology Language). Slimani compared how the most popular languages manage to meet attribute criteria, facet, taxonomy, function and general issue criteria [21].…”

Section: Tools and Methodologymentioning

confidence: 99%

Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

Martínez

Varén

et al. 2022

Information

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The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.

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Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Cited by 10 publications

References 24 publications

Computational methods applied to syphilis: where are we, and where are we going?

Computational methods applied to syphilis: where are we, and where are we going?

Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying

Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

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