An ontology-based approach for modelling and querying Alzheimer’s disease data

Background The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.Methods Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.Results We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.Conclusions The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources

ONS : an ontology for a standardized description of interventions and observational studies in nutrition

Background: The multidisciplinary nature of nutrition research is one of its main strengths. At the same time, however, it presents a major obstacle to integrate data analysis, especially for the terminological and semantic interpretations that specific research fields or communities are used to. To date, a proper ontology to structure and formalize the concepts used for the description of nutritional studies is still lacking. Results: We have developed the Ontology for Nutritional Studies (ONS) by harmonizing selected pre-existing de facto ontologies with novel health and nutritional terminology classifications. The ONS is the result of a scholarly consensus of 51 research centers in nine European countries. The ontology classes and relations are commonly encountered while conducting, storing, harmonizing, integrating, describing, and searching nutritional studies. The ONS facilitates the description and specification of complex nutritional studies as demonstrated with two application scenarios. Conclusions: The ONS is the first systematic effort to provide a solid and extensible formal ontology framework for nutritional studies. Integration of new information can be easily achieved by the addition of extra modules (i.e., nutrigenomics, metabolomics, nutrikinetics, and quality appraisal). The ONS provides a unified and standardized terminology for nutritional studies as a resource for nutrition researchers who might not necessarily be familiar with ontologies and standardization concepts

Semantic Similarity with Concept Senses

This dataset represents the results of the experimentation of a method for evaluating semantic similarity between concepts in a taxonomy.The method is based on the information-theoretic approach and allows senses of concepts in a given context to be considered.Relevance of senses is calculated in terms of semantic relatedness with the compared concepts.In a previous work [9], the adopted semantic relatedness method was the one described in [10], while in this work we also adopted the ones described in [11], [12], [13], [14], and [15]. We applied our proposal by extending 7 methods for computing semantic similarity in a taxonomy, selected from the literature.The methods considered in the experiment are referred to as R[2], W&P[3], L[4], J&C[5], P&S[6], A[7], and A&M[8]The experiment was run on the well-known Miller and Charles benchmark dataset [1] for assessing semantic similarity.The results are organized in six folders, each with the results related to one of the above semantic relatedness methods.In each folder there is a set of files, each referring to one pair of the Miller and Charles dataset. In fact, for each pair of concepts, all the 28 pairs are considered as possible different contexts. REFERENCES[1] Miller G.A., Charles W.G. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1).[2] Resnik P. 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Int. Joint Conf. on Artificial Intelligence, Montreal.[3] Wu Z., Palmer M. 1994. Verb semantics and lexical selection. 32nd Annual Meeting of the Associations for Computational Linguistics.[4] Lin D. 1998. An Information-Theoretic Definition of Similarity. Int. Conf. on Machine Learning.[5] Jiang J.J., Conrath D.W. 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Inter. Conf. Research on Computational Linguistics.[6] Pirrò G. 2009. A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data Knowl. Eng, 68(11).[7] Adhikari A., Dutta B., Dutta A., Mondal D., Singh S. 2018. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. J. Assoc. Inf. Sci. Technol. 69(8).[8] Adhikari A., Singh S., Mondal D., Dutta B., Dutta A. 2016. A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet. CoRR, arXiv:1607.05422, abs/1607.05422.[9] Formica A., Taglino F. 2021. An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy. IEEE Access, vol. 9.[10] Information Content-based approach [Schuhmacher and Ponzetto, 2014]. [11] Linked Data Semantic Distance (LDSD) [Passant, 2010]. [12] Wikipedia Link-based Measure (WLM ) [Witten and Milne, 2008];[13] Linked Open Data Description Overlap-based approach (LODDO) [Zhou et al. 2012] [14] Exclusivity-based [Hulpuş et al 2015][15] ASRMP [El Vaigh et al. 2020

ACM dataset for experimental assessment of semantic similarity methods

This dataset collects data about ACM Transactions on Database Systems (TODS) and ACM Transactions on Information Systems (TOIS) papers published from January 1997 to July 2017. The dataset can be used for experimental evaluation of semantic similarity methods. The dataset has been also used to assess the performance of the SemSimp semantic similarity method

SemanticSimilarityWithConceptSenses

This dataset represents the results of the experimentation of a method for evaluating semantic similarity between concepts in a taxonomy.The method is based on the information-theoretic approach and allows senses of concepts in a given context to be considered.Relevance of senses is calculated in terms of semantic relatedness with the compared concepts.In a previous work [9], the adopted semantic relatedness method was the one described in [10], while in this work we adopted the one described in [11]. This results in an improvement of the method.The dataset is composed of two folders, which contain the results of the previous and the new experimentation, respectively. In particular, in each folder there is a set of files, each referring to one pair of the well-known Miller and Charles benchmark dataset [1] for assessing semantic similarity.For each pair of concepts, the same 28 pairs are all considered as possible different contexts. We applied our proposal by extending 7 methods for computing semantic similarity in a taxonomy, selected from the literature.The methods considered in the experiment are referred to as (R[2], W&P[3], L[4], J&C[5], P&S[6], A[7], A&M[8]):REFERENCES[1] Miller G.A., Charles W.G. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1).[2] Resnik P. 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Int. Joint Conf. on Artificial Intelligence, Montreal.[3] Wu Z., Palmer M. 1994. Verb semantics and lexical selection. 32nd Annual Meeting of the Associations for Computational Linguistics.[4] Lin D. 1998. An Information-Theoretic Definition of Similarity. Int. Conf. on Machine Learning.[5] Jiang J.J., Conrath D.W. 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Inter. Conf. Research on Computational Linguistics.[6] Pirrò G. 2009. A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data Knowl. Eng, 68(11).[7] Adhikari A., Dutta B., Dutta A., Mondal D., Singh S. 2018. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. J. Assoc. Inf. Sci. Technol. 69(8).[8] Adhikari A., Singh S., Mondal D., Dutta B., Dutta A. 2016. A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet. CoRR, arXiv:1607.05422, abs/1607.05422.[9] Formica A., Taglino F. 2021. An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy. IEEE Access, vol. 9.[10] Schuhmacher M., Ponzetto S. P. 2014. Knowledge-based Graph Document Modeling. 7th ACM International Conference on Web Search and Data Mining.[11] El Vaigh C. B., Goasdoué F., Gravier G., Sébillot P. 2020. A Novel Path-Based Entity Relatedness Measure for Efficient Collective Entity Linking. ISWC 2020. Finally, in each file, the Pearson's and the Spearman's correlations of our proposal with respect to human judgement is reported.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Semantic Similarity with Concept Senses: new Experiment

This dataset represents the results of the experimentation of a method for evaluating semantic similarity between concepts in a taxonomy.The method is based on the information-theoretic approach and allows senses of concepts in a given context to be considered.Relevance of senses is calculated in terms of semantic relatedness with the compared concepts.In a previous work [9], the adopted semantic relatedness method was the one described in [10], while in this work we also adopted the ones described in [11], [12], [13], [14], [15], and [16]. We applied our proposal by extending 7 methods for computing semantic similarity in a taxonomy, selected from the literature.The methods considered in the experiment are referred to as R[2], W&P[3], L[4], J&C[5], P&S[6], A[7], and A&M[8]The experiment was run on the well-known Miller and Charles benchmark dataset [1] for assessing semantic similarity.The results are organized in seven folders, each with the results related to one of the above semantic relatedness methods.In each folder there is a set of files, each referring to one pair of the Miller and Charles dataset. In fact, for each pair of concepts, all the 28 pairs are considered as possible different contexts. REFERENCES[1] Miller G.A., Charles W.G. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1).[2] Resnik P. 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Int. Joint Conf. on Artificial Intelligence, Montreal.[3] Wu Z., Palmer M. 1994. Verb semantics and lexical selection. 32nd Annual Meeting of the Associations for Computational Linguistics.[4] Lin D. 1998. An Information-Theoretic Definition of Similarity. Int. Conf. on Machine Learning.[5] Jiang J.J., Conrath D.W. 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Inter. Conf. Research on Computational Linguistics.[6] Pirrò G. 2009. A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data Knowl. Eng, 68(11).[7] Adhikari A., Dutta B., Dutta A., Mondal D., Singh S. 2018. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. J. Assoc. Inf. Sci. Technol. 69(8).[8] Adhikari A., Singh S., Mondal D., Dutta B., Dutta A. 2016. A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet. CoRR, arXiv:1607.05422, abs/1607.05422.[9] Formica A., Taglino F. 2021. An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy. IEEE Access, vol. 9.[10] Information Content-based approach [Schuhmacher and Ponzetto, 2014]. [11] Linked Data Semantic Distance (LDSD) [Passant, 2010]. [12] Wikipedia Link-based Measure (WLM ) [Witten and Milne, 2008];[13] Linked Open Data Description Overlap-based approach (LODDO) [Zhou et al. 2012] [14] Exclusivity-based [Hulpuş et al 2015][15] ASRMP [El Vaigh et al. 2020][16] LDSDGN [Piao and Breslin, 2016]THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset for experimental assessment of semantic similarity methods

This dataset collects data about ACM Transactions on Database Systems (TODS) and ACM Transactions on Information Systems (TOIS) papers published from January 1997 to July 2017. The dataset can be used for experimental evaluation of semantic similarity methods. The dataset has been also used to assess the performance of the SemSimp semantic similarity method

Semantic Relatedness in DBpedia

These data refer to the benchmarking of 10 methods for computing semantic similarity applied to 13 golden datasets.The 10 addressed methods are: - Wikipedia Link-based Measure (WLM ) [Witten and Milne, 2008];- Linked Open Data Description Overlap-based approach (LODDO) [Zhou et al. 2012] - Linked Data Semantic Distance (LDSD) [Passant, 2010]- Linked Data Semantic Distance with Global Normalization (PLDSDGN) [Piao and Breslin, 2016] - Propagated Linked Data Semantic Distance (PLDSD) [Alfarhood et al. 2017] - Information Content-based approach [Schuhmacher and Ponzetto, 2014] - REWOrD [Pirrò, 2012]- Exclusivity-based [Hulpuş et al 2015]- ASRMP [El vaigh et al. 2020]- Proximity-based [Leal, 2013]The 14 golden datasets are: Atlasify240, B0, B1, GM30, MTurk287, WRG252, R122, RG65, MC30, KORE(composed by KORE-IT, KORE-HW, KORE-VG, KORE-TV, KORE-CN)In the experimentation, two DBpedia knowledge graphs have been considered, i.e., with and without the dbo:wikiPageWikiLink links.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

SemanticSimilarityWithConceptSenses

This dataset represents the results of the experimentation of a method for evaluating semantic similarity between concepts in a taxonomy.The method is based on the information-theoretic approach and allows senses of concepts in a given context to be considered.Relevance of senses is calculated in terms of semantic relatedness with the compared concepts.In a previous work [9], the adopted semantic relatedness method was the one described in [10], while in this work we adopted the one described in [11]. This results in an improvement of the method.The dataset is composed of two folders, which contain the results of the previous and the new experimentation, respectively. In particular, in each folder there is a set of files, each referring to one pair of the well-known Miller and Charles benchmark dataset [1] for assessing semantic similarity.For each pair of concepts, the same 28 pairs are all considered as possible different contexts. We applied our proposal by extending 7 methods for computing semantic similarity in a taxonomy, selected from the literature.The methods considered in the experiment are referred to as (R[2], W&P[3], L[4], J&C[5], P&S[6], A[7], A&M[8]):REFERENCES[1] Miller G.A., Charles W.G. 1991. Contextual correlates of semantic similarity. Language and Cognitive Processes 6(1).[2] Resnik P. 1995. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Int. Joint Conf. on Artificial Intelligence, Montreal.[3] Wu Z., Palmer M. 1994. Verb semantics and lexical selection. 32nd Annual Meeting of the Associations for Computational Linguistics.[4] Lin D. 1998. An Information-Theoretic Definition of Similarity. Int. Conf. on Machine Learning.[5] Jiang J.J., Conrath D.W. 1997. Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. Inter. Conf. Research on Computational Linguistics.[6] Pirrò G. 2009. A Semantic Similarity Metric Combining Features and Intrinsic Information Content. Data Knowl. Eng, 68(11).[7] Adhikari A., Dutta B., Dutta A., Mondal D., Singh S. 2018. An intrinsic information content-based semantic similarity measure considering the disjoint common subsumers of concepts of an ontology. J. Assoc. Inf. Sci. Technol. 69(8).[8] Adhikari A., Singh S., Mondal D., Dutta B., Dutta A. 2016. A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet. CoRR, arXiv:1607.05422, abs/1607.05422.[9] Formica A., Taglino F. 2021. An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy. IEEE Access, vol. 9.[10] Schuhmacher M., Ponzetto S. P. 2014. Knowledge-based Graph Document Modeling. 7th ACM International Conference on Web Search and Data Mining.[11] El Vaigh C. B., Goasdoué F., Gravier G., Sébillot P. 2020. A Novel Path-Based Entity Relatedness Measure for Efficient Collective Entity Linking. ISWC 2020. Finally, in each file, the Pearson's and the Spearman's correlations of our proposal with respect to human judgement is reported.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV