Logical Linked Data Compression

Linked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity

Automatic Text Summarization Based on Semantic Networks and Corpus Statistics

One simple automatic text summarization method that can minimize redundancy, in summary, is the Maximum Marginal Relevance (MMR) method. The MMR method has the disadvantage of having parts that are separated from each other in summary results that are not semantically connected. Therefore, this study aims to compare summary results using the MMR method based on semantic and non-semantic based MMR. Semantic-based MMR methods utilize WordNet Bahasa and corpus in processing text summaries. The MMR method is non-semantic based on the TF-IDF method. This study also carried out summary compression of 30%, 20%, and 10%. The research data used is 50 online news texts. Testing of the summary text results is done using the ROUGE toolkit. The results of the study state that the best value of the f-score in the semantic-based MMR method is 0.561, while the best f-score in the non-semantic MMR method is 0.598. This value is generated by adding a preprocessing process in the form of stemming and compression of a 30% summary result. The difference in value obtained is due to incomplete WordNet Bahasa and there are several words in the news title that are not in accordance with EYD (KBBI)

Reasoning with Data Flows and Policy Propagation Rules

Data-oriented systems and applications are at the centre of current developments of the World Wide Web. In these scenarios, assessing what policies propagate from the licenses of data sources to the output of a given data-intensive system is an important problem. Both policies and data flows can be described with Semantic Web languages. Although it is possible to define Policy Propagation Rules (PPR) by associating policies to data flow steps, this activity results in a huge number of rules to be stored and managed. In a recent paper, we introduced strategies for reducing the size of a PPR knowledge base by using an ontology of the possible relations between data objects, the Datanode ontology, and applying the (A)AAAA methodology, a knowledge engineering approach that exploits Formal Concept Analysis (FCA). In this article, we investigate whether this reasoning is feasible and how it can be performed. For this purpose, we study the impact of compressing a rule base associated with an inference mechanism on the performance of the reasoning process. Moreover, we report on an extension of the (A)AAAA methodology that includes a coherency check algorithm, that makes this reasoning possible. We show how this compression, in addition to being beneficial to the management of the knowledge base, also has a positive impact on the performance and resource requirements of the reasoning process for policy propagation