Semantic Web - Volume Pre-press, issue Pre-press

Authors: Portisch, Jan | Hladik, Michael | Paulheim, Heiko

Article Type: Research Article

Abstract: Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as …the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process. Show more

Keywords: Ontology matching, schema matching, background knowledge, data integration, semantic integration, knowledge graphs, ontologies

DOI: 10.3233/SW-223085

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-55, 2022

Authors: Nguyen, Phuc | Kertkeidkachorn, Natthawut | Ichise, Ryutaro | Takeda, Hideaki

Article Type: Research Article

Abstract: Semantic annotation of tabular data is the process of matching table elements with knowledge graphs. As a result, the table contents could be interpreted or inferred using knowledge graph concepts, enabling them to be useful in downstream applications such as data analytics and management. Nevertheless, semantic annotation tasks are challenging due to insufficient tabular data descriptions, heterogeneous schema, and vocabulary issues. This paper presents an automatic semantic annotation system for tabular data, called MTab4D, to generate annotations with DBpedia in three annotation tasks: 1) matching table cells to entities, 2) matching columns to entity types, and 3) matching pairs of …columns to properties. In particular, we propose an annotation pipeline that combines multiple matching signals from different table elements to address schema heterogeneity, data ambiguity, and noisiness. Additionally, this paper provides insightful analysis and extra resources on benchmarking semantic annotation with knowledge graphs. Experimental results on the original and adapted datasets of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019) show that our system achieves an impressive performance for the three annotation tasks. MTab4D’s repository is publicly available at https://github.com/phucty/mtab4dbpedia . Show more

Keywords: Table annotation, knowledge graph, DBpedia, semantic table interpretation

DOI: 10.3233/SW-223098

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-25, 2022

Authors: Erenrich, Daniel

Article Type: Research Article

Abstract: Despite its size, Wikidata remains incomplete and inaccurate in many areas. Hundreds of thousands of articles on English Wikipedia have zero or limited meaningful structure on Wikidata. Much work has been done in the literature to partially or fully automate the process of completing knowledge graphs, but little of it has been practically applied to Wikidata. This paper presents two interconnected practical approaches to speeding up the Wikidata completion task. The first is Wwwyzzerdd, a browser extension that allows users to quickly import statements from Wikipedia to Wikidata. Wwwyzzerdd has been used to make over 100 thousand edits to Wikidata. …The second is Psychiq, a new model for predicting instance and subclass statements based on English Wikipedia articles. Psychiq’s performance and characteristics make it well suited to solving a variety of problems for the Wikidata community. One initial use is integrating the Psychiq model into the Wwwyzzerdd browser extension. Show more

Keywords: Wikidata, Wikipedia, browser extension, knowledge graph completion

DOI: 10.3233/SW-233450

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Amaral, Gabriel | Rodrigues, Odinaldo | Simperl, Elena

Article Type: Research Article

Abstract: Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe …aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare. Show more

Keywords: Fact verification, data verbalisation, knowledge graphs

DOI: 10.3233/SW-233467

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-34, 2023

Authors: Faria, Daniel | Santos, Emanuel | Balasubramani, Booma Sowkarthiga | Silva, Marta C. | Couto, Francisco M. | Pesquita, Catia

Article Type: Research Article

Abstract: Ontology matching establishes correspondences between entities of related ontologies, with applications ranging from enabling semantic interoperability to supporting ontology and knowledge graph development. Its demand within the Semantic Web community is on the rise, as the popularity of knowledge graph supporting information systems or artificial intelligence applications continues to increase. In this article, we showcase AgreementMakerLight (AML), an ontology matching system in continuous development since 2013, with demonstrated performance over nine editions of the Ontology Alignment Evaluation Initiative (OAEI), and a history of real-world applications across a variety of domains. We overview AML’s architecture and algorithms, its user interfaces …and functionalities, its performance, and its impact. AML has participated in more OAEI tracks since 2013 than any other matching system, has a median rank by F-measure between 1 and 2 across all tracks in every year since 2014, and a rank by run time between 3 and 4. Thus, it offers a combination of range, quality and efficiency that few matching systems can rival. Moreover, AML’s impact can be gauged by the 263 (non-self) publications that cite one or more of its papers, among which we count 34 real-world applications. Show more

Keywords: Ontology matching, instance matching, tool

DOI: 10.3233/SW-233304

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-13, 2023

Authors: Iglesias, Enrique | Vidal, Maria-Esther | Collarana, Diego | Chaves-Fraga, David

Article Type: Research Article

Abstract: The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the …complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine. Show more

Keywords: Data integration systems, knowledge graphs, RDF mapping languages

DOI: 10.3233/SW-243580

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-28, 2024

Authors: Ragab, Mohamed | Adidarma, Adam Satria | Tommasini, Riccardo

Article Type: Research Article

Abstract: Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks’ performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of the schema, the partitioning technique, and the storage formats. PPA, and in particular ranking functions, helps enable actionable insights on performance data, leading practitioners to an easier choice of the best way to deploy BD frameworks, especially for graph processing. However, the amount of experimental work …required to implement PPA is still huge. In this paper, we present PAPAYA,1 1 https://github.com/DataSystemsGroupUT/PAPyA a library for implementing PPA that allows (1) preparing RDF graphs data for a processing pipeline over relational BD systems, (2) enables automatic ranking of the performance in a user-defined solution space of experimental dimensions; (3) allows user-defined flexible extensions in terms of systems to test and ranking methods. We showcase PAPAYA on a set of experiments based on the SparkSQL framework. PAPAYA simplifies the performance analytics of BD systems for processing large (RDF) graphs. We provide PAPAYA as a public open-source library under an MIT license that will be a catalyst for designing new research prescriptive analytical techniques for BD applications. https://github.com/DataSystemsGroupUT/PAPyA Show more

Keywords: Benchmarking, RDF systems, big data, analytics, apache spark

DOI: 10.3233/SW-243582

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Arenas-Guerrero, Julián | Iglesias-Molina, Ana | Chaves-Fraga, David | Garijo, Daniel | Corcho, Oscar | Dimou, Anastasia

Article Type: Research Article

Abstract: RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar , which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use …cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files. Show more

Keywords: Knowledge graphs, RDF-star, RML-star, data integration

DOI: 10.3233/SW-243602

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Thornton, Katherine | Seals-Nutt, Kenneth | Chen, Anne

Article Type: Research Article

Abstract: We introduce Dura-Europos Stories, a multimedia application for viewing artifacts and places related to the Dura-Europos archaeological excavation. We describe the process of mapping data to the Wikidata data model as well as the process of contributing data to Wikidata. We provide an overview of the functionality of an interactive application for viewing images of the artifacts in the context of their metadata. We contextualize this project as an example of using knowledge graphs in research projects in order to leverage technologies of the Semantic Web in such a way that data related to the project can be easily combined …with other data on the web. Presenting artifacts in this story-based application allows users to explore these objects visually, and provides pathways for further exploration of related information. Show more

Keywords: Wikidata, art history, archaeology, cultural heritage, digital humanities

DOI: 10.3233/SW-243552

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Hosseini Beghaeiraveri, Seyed Amir | Labra Gayo, Jose Emilio | Waagmeester, Andra | Ammar, Ammar | Gonzalez, Carolina | Slenter, Denise | Ul-Hasan, Sabah | Willighagen, Egon | McNeill, Fiona | Gray, Alasdair J.G.

Article Type: Research Article

Abstract: Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and …extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint. Show more

Keywords: Knowledge Graph, Wikidata, Subsetting, Big Data, Accuracy, Performance

DOI: 10.3233/SW-233491

Citation: Semantic Web, vol. Pre-press, no. Pre-press, pp. 1-27, 2023