Research on Semantic Similarity of Entities with the Case of Event Knowledge Graph

2020 ◽  
Author(s):  
Taoyuan Li ◽  
Liangli Ma ◽  
Jiwei Qin
Keyword(s):  
Semantic Similarity ◽  
Knowledge Graph ◽  
Event Knowledge

Event Relation Reasoning Based on Event Knowledge Graph

2021 ◽  
pp. 491-503
Author(s):  
Tingting Tang ◽  
Wei Liu ◽  
Weimin Li ◽  
Jinliang Wu ◽  
Haiyang Ren
Keyword(s):  
Knowledge Graph ◽  
Event Knowledge

Risk Warning for Special work Based on an Event Knowledge Graph

2020 ◽  
Author(s):  
Wang Yunlong ◽  
Wang Tingchun ◽  
Mu Bo ◽  
Guo Xiaoyan ◽  
Zhang Guozhi ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Event Knowledge ◽  
Risk Warning

scholarly journals Recommendation Model Based on Semantic Features and a Knowledge Graph

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yudong Liu ◽  
Wen Chen
Keyword(s):  
Semantic Similarity ◽  
Information Science ◽  
Absolute Error ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Short Text ◽  
Model Based ◽  
Recommendation Algorithms ◽  
Tremendous Amount ◽  
Series Of Experiments

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.

scholarly journals Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata

2019 ◽  
Author(s):  
Charlotte Rudnik ◽  
Thibault Ehrhart ◽  
Olivier Ferret ◽  
Denis Teyssou ◽  
Raphael Troncy ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Event Knowledge

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.

scholarly journals A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Carlota Cardoso ◽  
Rita T Sousa ◽  
Sebastian Köhler ◽  
Catia Pesquita
Keyword(s):  
Semantic Similarity ◽  
Protein Interactions ◽  
Knowledge Graph ◽  
Data Sets ◽  
Biomedical Domain ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Human Phenotype ◽  
Benchmark Data ◽  
Gene Similarity

Abstract The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein–protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein–protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

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.

scholarly journals Implementation of Tactical Decision Aids Based on Event Knowledge Graph

2021 ◽  
Vol 2078 (1) ◽  
pp. 012024
Author(s):  
Zhen Jia ◽  
Yang Chu ◽  
Zhi Liu
Keyword(s):  
Decision Aid ◽  
Prior Probability ◽  
Transition Probability ◽  
Decision Aids ◽  
Knowledge Graph ◽  
Occurrence Probability ◽  
Network Construction ◽  
Hierarchical Task Network ◽  
Event Knowledge ◽  
Tactical Decision

Abstract This paper proposes a new tactical decision aids method based on event knowledge graph (EventKG). In the warfare domain, EventKG can be constructed through event types design, event network construction and transition probability computation between events. Initially, four event classes are introduced in accordance with the OODA loop, and eighteen subclasses are further decomposed. With the aids of a common event template, all the events taking place in the battle field can be described. Event networks are built by adopting the hierarchical task network (HTN) and described through Bayesian network, to exhibit various relations between battle events. Transition probability, namely the occurrence probability of next possible event, is computed by using the prior probability and conditional probability of event occurring. On the basis of structured EventKG, entity knowledge graph (EKG) and entity relation knowledge graph (ERKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.

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