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Mosser, M., Pieressa, F., Reutter, JL., Soto, A., Vrgoc, D. (2022). Querying APIs with SPARQL. Inf. Syst., 105, 101650.
Abstract: Although the amount of RDF data has been steadily increasing over the years, the majority of information on the Web is still residing in other formats, and is often not accessible to Semantic Web services. A lot of this data is available through APIs serving JSON documents. In this work we propose a way of extending SPARQL with the option to consume JSON APIs and integrate this information into SPARQL query answers, obtaining a language that combines data from the �traditional� Web to the Semantic Web. Our proposal is based on an extension of the SERVICE operator with the ability to connect to JSON APIs. With the aim of evaluating these queries as efficiently as possible, we show that the main bottleneck is the amount of API requests, and present an algorithm that produces �worst-case optimal� query plans that reduce the number of requests as much as possible. We note that the analysis of this algorithm is by a reduction to an algorithm for evaluating relational queries with access methods with the minimal number of access queries, which is of independent interest. We show the superiority of the worst-case optimal approach in a series of experiments that take existing SPARQL benchmarks, and augment them with the ability to connect to JSON APIs in order to obtain additional information.
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Gutierrez, C., Hurtado, C. A., Mendelzon, A. O., & Perez, J. (2011). Foundations of Semantic Web databases. J. Comput. Syst. Sci., 77(3), 520–541.
Abstract: The Semantic Web is based on the idea of a common and minimal language to enable large quantities of existing data to be analyzed and processed. This triggers the need to develop the database foundations of this basic language, which is the Resource Description Framework (RDF). This paper addresses this challenge by: 1) developing an abstract model and query language suitable to formalize and prove properties about the RDF data and query language; 2) studying the RDF data model, minimal and maximal representations, as well as normal forms; 3) studying systematically the complexity of entailment in the model, and proving complexity bounds for the main problems; 4) studying the notions of query answering and containment arising in the RDF data model; and 5) proving complexity bounds for query answering and query containment. (C) 2010 Elsevier Inc. All rights reserved.
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Vargas-Vera, M., & Nagy, M. (2014). Establishing agent trust for contradictory evidence by means of fuzzy voting model: An ontology mapping case study. Comput. Hum. Behav., 30, 745–752.
Abstract: This paper introduces a novel trust assessment formalism for contradicting evidence in the context of multi-agent ontology mapping. Evidence combination using the Dempster rule tend to ignore contradictory evidence and the contemporary approaches for managing these conflicts introduce additional computation complexity i.e. increased response time of the system. On the Semantic Web, ontology mapping systems that need to interact with end users in real time cannot afford prolonged computation. In this work, we have made a step towards the formalisation of eliminating contradicting evidence, to utilise the original Dempster's combination rule without introducing additional complexity. Our proposed solution incorporates the fuzzy voting model to the Dempster-Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem. (C) 2013 Elsevier Ltd. All rights reserved.
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Vargas-Vera, M. (2015). A Framework for Extraction of Relations from Text using Relational Learning and Similarity Measures. J. Univers. Comput. Sci., 21(11), 1482–1495.
Abstract: Named entity recognition (NER) has been studied largely in the Information Extraction community as it is one step in the construction of an Information Extraction System. However, to extract only names without contextual information is not sufficient if we want to be able to describe facts encountered in documents, in particular, academic documents. Then, there is a need for extracting relations between entities. This task is accomplished using relational learning algorithms embedded in an Information Extraction framework. In particular, we have extended two relational learning frameworks RAPIER and FOIL. Our proposed extended frameworks are equipped with DSSim (short for Dempster-Shafer Similarity) our similarity service. Both extended frameworks were tested using an electronic newsletter consisting of news articles describing activities or events happening in an academic institution as our main application is on education.
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