The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows

Hofer, Marvin; Hellmann, Sebastian; Dojchinovski, Milan; Frey, Johannes

doi:10.1007/978-3-030-59833-4_1

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…We started with DBpedia, a popular and large knowledge base comprising billions of triples extracted from Wikipedia, texts, and other sources [ 20 , 21 ]. We analyzed DBpedia to identify possible instances or subclasses for the HITO classes.…”

Section: Methodsmentioning

confidence: 99%

A Linked Open Data–Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study

Jahn¹,

Ammenwerth²,

Dornauer³

et al. 2023

JMIR Med Inform

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Background There is a variety of libre/free and open-source software (LIFOSS) products for medicine and health care. To support health care and IT professionals select an appropriate software product for given tasks, several comparison studies and web platforms, such as Medfloss.org, are available. However, due to the lack of a uniform terminology for health informatics, ambiguous or imprecise terms are used to describe the functionalities of LIFOSS. This makes comparisons of LIFOSS difficult and may lead to inappropriate software selection decisions. Using Linked Open Data (LOD) promises to address these challenges. Objective We describe LIFOSS systematically with the help of the underlying Health Information Technology Ontology (HITO). We publish HITO and HITO-based software product descriptions using LOD to obtain the following benefits: (1) linking and reusing existing terminologies and (2) using Semantic Web tools for viewing and querying the LIFOSS data on the World Wide Web. Methods HITO was incrementally developed and implemented. First, classes for the description of software products in health IT evaluation studies were identified. Second, requirements for describing LIFOSS were elicited by interviewing domain experts. Third, to describe domain-specific functionalities of software products, existing catalogues of features and enterprise functions were analyzed and integrated into the HITO knowledge base. As a proof of concept, HITO was used to describe 25 LIFOSS products. Results HITO provides a defined set of classes and their relationships to describe LIFOSS in medicine and health care. With the help of linked or integrated catalogues for languages, programming languages, licenses, features, and enterprise functions, the functionalities of LIFOSS can be precisely described and compared. We publish HITO and the LIFOSS descriptions as LOD; they can be queried and viewed using different Semantic Web tools, such as Resource Description Framework (RDF) browsers, SPARQL Protocol and RDF Query Language (SPARQL) queries, and faceted searches. The advantages of providing HITO as LOD are demonstrated by practical examples. Conclusions HITO is a building block to achieving unambiguous communication among health IT professionals and researchers. Providing LIFOSS product information as LOD enables barrier-free and easy access to data that are often hidden in user manuals of software products or are not available at all. Efforts to establish a unique terminology of medical and health informatics should be further supported and continued.

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Section: Methodsmentioning

confidence: 99%

A Linked Open Data–Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study

Jahn¹,

Ammenwerth²,

Dornauer³

et al. 2023

JMIR Med Inform

View full text Add to dashboard Cite

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“…We run experiments on a server equipped with 96 cores and 900GB RAM running Ubuntu 18.04. All the resources used in the reported experimental study are publicly available 13 .…”

Section: ) Implementationmentioning

confidence: 99%

“…We choose the mentioned KGs because of: 1) the richness of the used KGs and how frequent they are updated. A new release for DBpedia is created every month [13]. Wikidata is updated daily with further contributions from the crowd 2 .…”

Section: Introductionmentioning

confidence: 99%

Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts

2022

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Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.

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“…Our motivation comes from the growth and incompleteness nature of real-world KGs. For example, the release bot of DBpedia [13] extracts about 21 billion new triples per month [10], and Wikidata [30] releases data dumps in a weekly cycle. 4 The new entities and triples bring about new alignment to be found and provide new clues for correcting the previous alignment.…”

Section: Introductionmentioning

confidence: 99%

Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs

Wang¹,

Cui²,

Liu³

et al. 2022

Preprint

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Entity alignment is a basic and vital technique in knowledge graph (KG) integration. Over the years, research on entity alignment has resided on the assumption that KGs are static, which neglects the nature of growth of real-world KGs. As KGs grow, previous alignment results face the need to be revisited while new entity alignment waits to be discovered. In this paper, we propose and dive into a realistic yet unexplored setting, referred to as continual entity alignment. To avoid retraining an entire model on the whole KGs whenever new entities and triples come, we present a continual alignment method for this task. It reconstructs an entity's representation based on entity adjacency, enabling it to generate embeddings for new entities quickly and inductively using their existing neighbors. It selects and replays partial pre-aligned entity pairs to train only parts of KGs while extracting trustworthy alignment for knowledge augmentation. As growing KGs inevitably contain non-matchable entities, different from previous works, the proposed method employs bidirectional nearest neighbor matching to find new entity alignment and update old alignment. Furthermore, we also construct new datasets by simulating the growth of multilingual DBpedia. Extensive experiments demonstrate that our continual alignment method is more effective than baselines based on retraining or inductive learning.

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The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows

Cited by 10 publications

References 12 publications

A Linked Open Data–Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study

A Linked Open Data–Based Terminology to Describe Libre/Free and Open-source Software: Incremental Development Study

Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts

Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs

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