Developing question answering (QA) systems that process natural language is a popular research topic. Conventionally, when QA systems receive a natural language question, they choose useful words or phrases based on their parts-of-speech (POS) tags. In general, words tagged as nouns are mapped to class entities, words tagged as verbs are mapped to property entities, and words tagged as proper nouns are mapped to named entities, although the accuracy of entity type identification remains low. Afterward, the relationship between entity types as RDF types determines the first element to be a pivot word to generate the SPARQL (acronym for SPARQL protocol and RDF query language) query on the basis of the sequences by a specific graph or tree structure, such as dependence tree or directed acyclic graph (DAG). However, the generated SPARQL query is difficult to adapt to the given query request in that the sequences are decided by a fixed structure. Unlike in previous research, SPARQL generation occurs automatically according to the entity type identification and RDF type identification results. This study attempts to design a method that leverages machine learning to learn human experiences in entity type identification as well as RDF-type identification. We approach the problem as a multiclass classification problem and propose a two-stage maximum-entropy Markov model (MEMM). The first stage identifies the entity type and the second identifies the RDF type for the purpose of generating appropriate SPARQL queries to meet the query request. Along with the templates designed for the two-stage MEMM model, we develop an automatic question answering prototype system called QAWizard. The experimental results show that QAWizard outperforms all other systems in question answering when evaluated on Linked Data version 8 (QALD-8) metrics.INDEX TERMS Question answering system (QA), Parts-of-speech (POS), SPARQL query, Maximumentropy Markov model.

Much like traditional database querying, the question answering process in a Question Answering (QA) system involves converting a user's question input into query grammar, querying the knowledge base through the query grammar, and finally returning the query result (i.e., the answer) to the user. The accuracy of query grammar generation is therefore important in determining whether a Question Answering system can produce a correct answer. Generally speaking, incorrect query grammar will never find the right answer. SPARQL is the most frequently used query language in question answering systems. In the past, SPARQL was generated based on graph structures, such as dependency trees, syntax trees and so on. However, the query cost of generating SPARQL is high, which creates long processing times to answer questions. To reduce the query cost, this work proposes a low-cost SPARQL generator named Light-QAWizard, which integrates multi-label classification into a recurrent neural network (RNN), builds a template classifier, and generates corresponding query grammars based on the results of template classifier. Light-QAWizard reduces query frequency to DBpedia by aggregating multiple outputs into a single output using multilabel classification. In the experimental results, Light-QAWizard's performance on Precision, Recall and F-measure metrics were evaluated on the QALD-7, QALD8 and QALD-9 datasets. Not only did Light-QAWizard outperform all other models, but it also had a lower query cost that was nearly half that of QAWizard. bullet.png INDEX TERMS Question answering system (QA), SPARQL query, Query cost, Recurrent neural network (RNN), Question Answering over Linked Data (QALD).

DBpedia is one of the most resourceful link databases today, and to access information in DBpedia databases, we need to use query syntax (e.g., SPARQL). However, not all users know SPARQL, so we must use a natural language query system to translate the user's query into the corresponding query syntax. It is costly and time-consuming for the query system to generate query syntax. Therefore, this paper proposes generating query syntax from natural language. Two multi-label learning methods are used for question transformation: Binary Relevance (BR) and Classifier Chains (CC). To predict all the labels that match the query intentions, we use Recurrent Neural Networks (RNNs) to build a multi-label classifier for generating RDF triples. To better consider the relationship between RDF triples, the Binary Relevance is integrated into an ensemble learning approach to propose an Ensemble BR. The experiments perform better than the other research to improve the query accuracy.

As Internet technology continues to profoundly impact our lives, techniques for information protection have become increasingly advanced and become a common discussion topic. With the aim to protect private images, this paper splits a secret image into n individual shares using a Sudoku matrix with authentication features. Later, the shares can be compiled to completely reconstruct the secret image. The shares are meaningful ones in order to avoid detection and suspicion among malicious users. Our proposed matrix is unique because the embedding rate of the secret data is very high, while the visual quality of the shares can be well guaranteed. In addition, the embedded authentication codes can be retrieved to authenticate the integrity of the secret image. Experimental results prove the advantages of our approach in terms of visual quality and authentication ability.