86,532 research outputs found
Semantic Similarity with Concept Senses
No full textThis 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
No full textThis 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
SemSimp: A Parametric Method for Evaluating the Semantic Similarity of Digital Resources
No full textSemSimp is a parametric method for evaluating the semantic similarity of digital resources that is based on the notion of information content. It exploits a weighted reference ontology of concepts and requires resources to be semantically annotated, each by means of a set of concepts from the ontology. Specifically, the weights of the concepts can be calculated either by considering the available annotations or only the structure of the ontology. SemSimp was evaluated against six representative semantic similarity methods proposed in the literature. Experiments were run on a large real-world dataset based on the Association for Computing Machinery (ACM) digital library, including both a statistical analysis and an expert judgment assessment. The main result shows that the SemSimp annotation frequency configuration, when combined with the geometric average normalization factor, outperforms the other methods
An ontology-based approach to improve data querying and organization of Alzheimer's Disease data
No full textThe recent advances in biotechnology and IT have led to an ever-increasing availability of public biomedical data distributed in large databases. Analyzing this huge volume of data is a challenging task because of its complexity, high heterogeneity and its multiple and numerous correlated factors. In the framework of neurodegenerative diseases, the last years have witnessed the creation of specialized databases such as the international projects ADNI (Alzheimer's Disease Neuroimaging Initiative). The main problems to fully exploit this database are related to the querying, integration, and analysis of data themselves. Here, we aim to develop a detailed ontology for clinical multidimensional datasets from ADNI repository in order to simplify the data access and to obtain new diagnostic knowledge about Alzheimer's Disease
Harmonise - Towards Interoperability in the Tourism Domain
No full textHarmonise tackles the interoperability problem in the tourism domain, where organisations following different standards should be enabled to exchange information in a seamless manner. The project puts a strong emphasis on the combination of a social consensus process with the application of new technologies. Carefully analysing and observing the requirements of the tourist domain and its current state of the art, a comprehensive methodology has been elaborated to achieve the desired harmonisation effect. Harmonise combines suitable tools into a platform supporting an ontology-based mediation process. In this paper we describe three fundamentals of the harmonisation effort: interoperability, ontologies and mediators. And we draw a vision of a future electronic tourism market based on these fundamental
A comparative assessment of ontology weighting methods in semantic similarity search
No full textSemantic search is the new frontier for the search engines of the last generation. Advanced semantic search methods are exploring the use of weighted ontologies, i.e., domain ontologies where concepts are associated with weights, inversely related to their selective power. In this paper, we present and assess four different ontology weighting methods, organized according to two groups: intensional methods, based on the sole ontology structure, and extensional methods, where also the content of the search space is considered. The comparative assessment is carried out by embedding the different methods within the semantic search engine SemSim, based on weighted ontologies, and then by running four retrieval tests over a search space we have previously proposed in the literature. In order to reach a broad audience of readers, the key concepts of this paper have been presented by using a simple taxonomy, and the already experimented dataset
A comparative study of LLMs and NLP approaches for supporting business process analysis
No full textThis paper compares two approaches to support Business Process Analysis (BPA) for the construction of a Business Process Knowledge Base (BPKB). The methodology is based on the BPA Canvas metamodel, which starts from the preliminary output of the business analysis (an interview). We focus on the extraction of key elements from the text to build the BPKBcore. We experiment with approaches based on Natural Language Processing (NLP) and Large Language Models (LLMs), using a running example to compare the outcomes with manual annotations. The experiment shows that the LLM-based approach yields better performance, especially with enriched prompts
SemanticSimilarityWithConceptSenses
No full textThis 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
Building theories from IT project design: the HOPES case
No full textDesign science is increasingly attracting the interest of scholars in the field of Information Systems. Starting from a design problem, a researcher selects the kernel theories from which to derive prescriptions for the meta-requirements, the product features (meta-design), the design process (design method) and some testable design product and process hypotheses. The theoretical contribution of this research stream is related to both the new artifact and the practical guidelines for developing it. In this paper we argue that design science as a research strategy can also have an impact on the available knowledge on the social phenomenon to which the design problem refers. In fact, especially when multi-disciplinary teams participate to the design of an IT system, kernel theories can benefit from the different perspectives of actors involved. The design process of a multimedia platform providing innovative social e-services to European elderly persons and their social entourage represents the case study for supporting our hypothesis
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