MateTee: A Semantic Similarity Metric Based on Translation Embeddings for Knowledge Graphs

2017 ◽  
pp. 246-263 ◽  
Author(s):  
Camilo Morales ◽  
Diego Collarana ◽  
Maria-Esther Vidal ◽  
Sören Auer
Keyword(s):  
Semantic Similarity ◽  
Similarity Metric ◽  
Knowledge Graphs

Sentence Similarity Metric and its Application in FAQ System

2013 ◽  
Vol 718-720 ◽  
pp. 2248-2251
Author(s):  
Pei Ying Zhang
Keyword(s):  
Semantic Similarity ◽  
Question Answering ◽  
Similarity Metric ◽  
Question Answering System ◽  
Sentence Similarity

FAQ system is a question answering system which finds the question sentence from question-answer collection and then returns its corresponding answer to user. The task of matching questions to corresponding question-answer pairs has become a major challenge in FAQ system. This paper proposes a method for sentence similarity metric between questions according to its semantic similarity as well as the length of question length. Experiments show that this method can improve the accuracy and intelligence of answering system, has some practical value.

Sematch: Semantic similarity framework for Knowledge Graphs

2017 ◽  
Vol 130 ◽  
pp. 30-32 ◽  
Author(s):  
Ganggao Zhu ◽  
Carlos A. Iglesias
Keyword(s):  
Semantic Similarity ◽  
Knowledge Graphs

A semantic metric for concepts similarity in knowledge graphs

2021 ◽  
pp. 016555152110205
Author(s):  
Majed A Alkhamees ◽  
Mohammed A Alnuem ◽  
Saleh M Al-Saleem ◽  
Abdulrakeeb M Al-Ssulami
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Depth Information ◽  
Data Sets ◽  
Word Meanings ◽  
Word Similarity ◽  
Knowledge Graphs ◽  
Highly Correlated ◽  
Degree Of Similarity

Semantic similarity between concepts concerns expressing the degree of similarity in meaning between two concepts in a computational model. This problem has recently attracted considerable attention from researchers in attempting to automate the understanding of word meanings to expedite the classification of users’ opinions and attitudes embedded in text. In this article, a semantic similarity metric is presented. The proposed metric, namely, weighted information-content ( wic), exploits the information content of the least common subsumer of two compared concepts and the depth information in knowledge graphs such as DBPedia and YAGO. The two similarity components were combined using calibrated cooperative contributions from both similarity components. A statistical test using the Spearman correlations on well-known human judgement word-similarity data sets showed that the wic metric produced more highly correlated similarities compared with state-of-the-art metrics. In addition, a real-world aspect category classification was evaluated, which exhibited further increased accuracy and recall.

scholarly journals Object-Action Association Extraction from Knowledge Graphs

2021 ◽  
Author(s):  
Alexandros Vassiliades ◽  
Theodore Patkos ◽  
Vasilis Efthymiou ◽  
Antonis Bikakis ◽  
Nick Bassiliades ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Semantic Similarity ◽  
Semantic Information ◽  
Research Training ◽  
Relevant Literature ◽  
Physical World ◽  
Knowledge Graph ◽  
Training Systems ◽  
Novel Method ◽  
Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long-lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense. In this paper, we propose a novel method for extracting and evaluating relations between objects and actions from knowledge graphs, such as ConceptNet and WordNet. We present a complete methodology of locating, enriching, evaluating, cleaning and exposing knowledge from such resources, taking into consideration semantic similarity methods. One important aspect of our method is the flexibility in deciding how to deal with the noise that exists in the data. We compare our method with typical approaches found in the relevant literature, such as methods that exploit the topology or the semantic information in a knowledge graph, and embeddings. We test the performance of these methods on the Something-Something Dataset.

Improving Ontology Alignment through Genetic Algorithms

2010 ◽  
pp. 240-259 ◽  
Author(s):  
José Manuel Vázquez Naya ◽  
Marcos Martínez Romero ◽  
Javier Pereira Loureiro ◽  
Cristian R. Munteanu ◽  
Alejandro Pazos Sierra
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Semantic Similarity ◽  
Common Knowledge ◽  
Complex Problem ◽  
Initial Population ◽  
Ontology Alignment ◽  
Alignment Quality ◽  
Single Measure ◽  
Similarity Metric

Ontology alignment is recognized as a fundamental process to achieve an adequate interoperability between people or systems that use different, overlapping ontologies to represent common knowledge. This process consists of finding the semantic relations between different ontologies. There are different techniques conceived to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple measures into a single similarity metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or calculated through general methods that does not provide optimal results. In this chapter, a genetic algorithm based approach to find out how to aggregate different similarity metrics into a single measure is presented. Starting from an initial population of individuals, each one representing a specific combination of measures, the algorithm finds the combination that provides the best alignment quality.

scholarly journals A Structural-Lexical Measure of Semantic Similarity for Geo-Knowledge Graphs

2015 ◽  
Vol 4 (2) ◽  
pp. 471-492 ◽  
Author(s):  
Andrea Ballatore ◽  
Michela Bertolotto ◽  
David Wilson
Keyword(s):  
Semantic Similarity ◽  
Knowledge Graphs

scholarly journals Evolving knowledge graph similarity for supervised learning in complex biomedical domains

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Rita T. Sousa ◽  
Sara Silva ◽  
Catia Pesquita
Keyword(s):  
Supervised Learning ◽  
Semantic Similarity ◽  
Protein Interaction ◽  
Expert Knowledge ◽  
Learning Task ◽  
Knowledge Graph ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Knowledge Graphs

Abstract Background In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge. Results We have developed a novel approach, evoKGsim, that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The approach was evaluated on several benchmark datasets for protein-protein interaction prediction using the Gene Ontology as the knowledge graph to support semantic similarity, and it outperformed competing strategies, including manually selected combinations of semantic aspects emulating expert knowledge. evoKGsim was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting protein-protein interactions for species with fewer known interactions. Conclusions evoKGsim can overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications.

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