A Logic-Based Approach to Named-Entity Disambiguation in the Web of Data

Giannini, Silvia; Colucci, Simona; Donini, Francesco M.; Sciascio, Eugenio Di

doi:10.1007/978-3-319-24309-2_28

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…KORE uses keyphrase overlap relatedness to relate entities in the text to pre-extracted entities from Wikipedia (Hoffart et al, 2012). Other approaches use a notion of exclusivity-base relatedness to measure how similar two nodes are in a graph (Giannini et al, 2015), and a common subsumers algorithm between ambiguous entities and close entities that are unambiguous (Hulpus et al, 2015) for disambiguation.…”

Section: Named Entity Disambiguationmentioning

confidence: 99%

Knowledge models from PDF textbooks

Alpizar-Chacon

Sosnovsky

2021

New Review of Hypermedia and Multimedia

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Textbooks are educational documents created, structured and formatted by domain experts with the primary purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extensible approach towards automated extraction of knowledge models from textbooks and enrichment of their content with additional links (both internal and external). The textbooks themselves essentially become hypertext documents where individual pages are annotated with important concepts in the domain. The evaluation experiments examine several aspects and stages of the approach, including the accuracy of model extraction, the pragmatic quality of extracted models using one of their possible applications-semantic linking of textbooks in the same domain, the accuracy of linking models to external knowledge sources and the effect of integration of multiple textbooks from the same domain. The results indicate high accuracy of model extraction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.

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Section: Named Entity Disambiguationmentioning

confidence: 99%

Knowledge models from PDF textbooks

Alpizar-Chacon

Sosnovsky

2021

New Review of Hypermedia and Multimedia

View full text Add to dashboard Cite

show abstract

“…KORE [130] uses keyphrase overlap relatedness to relate entities in the text to pre-extracted entities from Wikipedia. Other approaches use a notion of exclusivity-base relatedness to measure how similar two nodes in a graph are [111], and a common subsumers algorithm between ambiguous entities and close entities that are unambiguous [133] for disambiguation.…”

Section: Linking Resources To Entitiesmentioning

confidence: 99%

Extraction of Knowledge Models from Textbooks

Chacón¹

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Many adaptive educational systems and other artificial intelligence applications rely on high-quality knowledge representations. Still, knowledge acquisition remains the primary bottleneck hindering large-scale deployment and adoption of knowledge-based systems. One path to scalable knowledge extraction is using digital textbooks, given their domain-oriented content, structure, and availability. This dissertation presents a unified approach for automatically extracting high-quality and domain-specific knowledge models from digital textbooks. The proposed approach leverages the authors’ knowledge encoded in the textbooks’ elements that facilitate navigation and understanding of the material (table of contents, index, formatting styles) to create knowledge models. The proposed workflow first extracts initial information elements from the textbooks: the structure of chapters and subchapters using the Table of Content, the content of each section, and domain terminology from the back-of-the-book index. Then, new information is added: domain terms are linked to external entities in a knowledge graph (DBpedia) and are enriched with semantic content (e.g., abstracts and categories). Finally, the knowledge about the domain is refined by identifying the relevance of concepts to the target domain. The extracted knowledge is represented in a model using the Text Encoding Initiative. Multiple evaluations show that the extracted knowledge models have high levels of quality across several properties: accuracy, semantics, coverage, specificity, cognitive validity, and granularity. Additionally, the approach is effective in multiple domains—for example, statistics, ancient philosophy, and Python programming. Finally, there are many potential applications for the extracted knowledge models. This dissertation presents three different educational systems supported by the knowledge models.

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Logical comparison over RDF resources in bio-informatics

Colucci

Donini

Sciascio

2017

Journal of Biomedical Informatics

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Comparison of resources is a frequent task in different bio-informatics applications, including drug-target interaction, drug repositioning and mechanism of action understanding, among others. This paper proposes a general method for the logical comparison of resources modeled in Resource Description Framework and shows its distinguishing features with reference to the comparison of drugs. In particular, the method returns a description of the commonalities between resources, rather than a numerical value estimating their similarity and/or relatedness. The approach is domain-independent and may be flexibly adapted to heterogeneous use cases, according to a process for setting parameters which is completely explicit. The paper also presents an experiment using the dataset Bioportal as knowledge source; the experiment is fully reproducible, thanks to the elicitation of criteria and values for parameter customization.

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A Logic-Based Approach to Named-Entity Disambiguation in the Web of Data

Cited by 6 publications

References 22 publications

Knowledge models from PDF textbooks

Knowledge models from PDF textbooks

Extraction of Knowledge Models from Textbooks

Logical comparison over RDF resources in bio-informatics

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