IJCKG

Reasoning results computed by description logic systems can be hard to comprehend. When an ontology does not entail an expected subsumption relationship, generating an explanation of this non-entailment becomes necessary. In this paper, we use ...

Ontology-based data access (OBDA) is a well-established approach to information management which facilitates the access to a (single) relational data source through the mediation of a high-level ontology, and the use of a declarative mapping linking ...

Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in ...

Named entity extraction is a crucial task to support the population of Knowledge Bases (KBs) from documents written in natural language. However, in many application domains, these documents must be collected and processed incrementally to update the ...

Real-world SPARQL querying benchmarks, which make use of the real-world RDF datasets and/or SPARQL queries, are the key element in testing the performance of different RDF Knowledge graph management systems in real-world settings. Over the last years, ...

Knowledge Graphs are relevant for many applications, but are inherently incomplete. Thus, Link Prediction methods have been proposed to infer new triples in order to complete a given Knowledge Graph. Many Link Prediction methods ignore literals, in ...

Empathetic dialogue generation is dedicated to generating responses to empathize with users by perceiving and understanding context emotions and dialogue situations. Existing works typically emphasize that an empathetic response needs to express ...

In order to utilize artificial intelligence (AI) safely and securely in society, explainable artificial intelligence (XAI) technology, which has the property of being able to explain the reasons why a system has reached a conclusion, is necessary. ...

Relation linking is an essential module of knowledge base question answering systems. To overcome the ambiguity of natural language and lack of training data, existing relation linking systems employ varieties of heuristics or aggregations of multiple ...

Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning ...

Relation extraction is an important task in natural language processing. In recent years, researchers have gradually discovered that relation extraction can be used for teaching and learning assessment. Among various dependencies, the ISA is a relation ...

Data analytics including machine learning analytics is essential to extract insights from production data in modern industries. Visual analytics is essential for data analytics for e.g., presenting the data to provide an instinctive perception in ...

When the estimated probabilities do not match the relative frequencies, we say these estimated probabilities are uncalibrated [39], which may cause incorrect decision making, and is particularly undesired in high-stakes tasks [45]. Knowledge Graph ...

Traffic flow prediction is a critical issue for researchers and practitioners in the field of transportation. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, ...

Visual Question Answering (VQA) can facilitate social convenience, which needs to study complex joint reasoning in the visual and language over external knowledge. Recently, Knowledge-Based VQA has attracted the attention of researchers. There are many ...

BERTScore is an effective and robust automatic metric for reference-based machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the ...