OntoKhoj

Patel, Chintan; Supekar, Kaustubh; Lee, Yugyung; Park, E. K.

doi:10.1145/956699.956712

Cited by 98 publications

(13 citation statements)

References 2 publications

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“…Only by knowing to which knowledge domains a class/property belongs to, will it be possible to associate an observed phenomenon with them. There is valuable research to address LOD dataset classification [10], [11], [12], [13], [14], [15], [16], [17]. Most of these methods are experimented on small scale data and use limited classification systems (e.g.…”

Section: B Knowledge Domains Covered By Linked Open Datamentioning

confidence: 99%

“…Topical classification is the task of associating a LOD dataset (or an ontology) with one or multiple knowledge domains (or topics). Domain annotation has been used for enhancing semantic web archives and search engines [10], [12], for assessing provenance and quality of data [21], [22], and for improving performance in learning tasks [23]. We identify two types of topical classification approaches: machine learning-based and alignment-based.…”

Section: A Topical Classification Of Lod Datasetsmentioning

confidence: 99%

“…Patel et al [10] propose to treat ontologies as plain text and to train a single-label classifier (with Naïve Bayes, Probabilistic Indexing or K-Nearest Neighbours) over the DMOZ dataset. 7 They show good results but the experiments use a limited number (five) of knowledge domains, making the classification too general for many applications e.g.…”

Section: ) Machine Learning-based Approachesmentioning

confidence: 99%

“…A classification system should have a large coverage of KDs, however it is a challenge to find a good balance for their dimension: a too large classification system makes the classification task hard for both humans and machines, while a too small one makes the classification less effective in concrete applications such as searching, indexing, etc. Instead of defining yet a new hierarchy from scratch, we align and integrate popular KD classification systems: Wikipedia Categories, 8 Dewey Decimal Classification, 9 Library of Congress Classification, 10 WordNet domains [33], BabelNet domains [34], LOD cloud classification [19], LOV keywords [18].…”

Section: A Reference Classification Systems Of Knowledge Domainsmentioning

confidence: 99%

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Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Asprino

Presutti

2023

IEEE Access

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Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character.INDEX TERMS Intelligent web services and semantic web, knowledge engineering methodologies, knowledge and data engineering tools and techniques.

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Section: B Knowledge Domains Covered By Linked Open Datamentioning

confidence: 99%

Section: A Topical Classification Of Lod Datasetsmentioning

confidence: 99%

Section: ) Machine Learning-based Approachesmentioning

confidence: 99%

Section: A Reference Classification Systems Of Knowledge Domainsmentioning

confidence: 99%

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Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Asprino

Presutti

2023

IEEE Access

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“…Ontology selection is the process of selecting and ranking a list of ontologies, sorted by how well they meet a certain ontology evaluation task [86]. Existing ontology selection approaches can be classified into three types: based on popularity [74] [24] [12], based on richness of knowledge [1] and based on topic coverage [57]. We provide a new ontology selection task.…”

Section: Introductionmentioning

confidence: 99%

Scientific dataset recommendation with semantic techniques

Wang¹

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Scientific datasets are important in scientific research. Researchers always want to find a way to improve their research. Data discovery and reuse is a recent and popular way of finding more scientific datasets to help researchers. There is much current research work for data discovery and reuse. Nevertheless, most of these works are limited because researchers only trust and reuse data through social networks (such as colleagues or collaborators). Also, the scientific data from social networks bring limitations. For instance, they will only recommend data that they consider helpful. However, one scientist's useful data sometimes might be another's noisy data. A dataset search engine is a solution to help researchers find data outside their social networks by exploring scientific datasets in open data repositories or hubs. Such a dataset search engine typically returns related or relevant datasets based on a keyword query from researchers. Several existing dataset search engines already provide millions of open datasets from open-access sources. However, the performance of a dataset search engine is highly related to the quality of the keyword queries submitted by researchers. For instance, two different queries with the same meaning would get different returns from the same dataset search engine. The main question of this thesis is how to find more relevant scientific datasets from a query on a dataset search engine. A new idea is provided as the solution with dataset recommendation, which is adding one step, "if you like this dataset, do you also like these other datasets," after researchers or users submitted a query to a dataset search engine. Users submit keywords for query to a dataset search engine and then get returned list of datasets. Users can then tell the dataset search engine which returned dataset they like, and the dataset search engine will say, "If you like this dataset, you would also like those other datasets," to recommend more datasets. This thesis aims to help dataset search engines with such scientific dataset recommendations using Semantic Web technologies. As mentioned, the main idea of this thesis is "if you like this dataset, you would also like that other dataset". This thesis states the challenges and possibilities of using Semantic Web technologies on scientific dataset recommendation, as well as explores 1) how ontologies can be used for dataset recommendation, and 2) how knowledge graphs can be used for dataset recommendation? These two questions are addressed in six chapters. A scientific item (dataset or paper) recommendation benchmark was provided during the exploring work of this thesis to provide a standard for evaluating scientific item recommendation methods. This benchmark contains a large-scale corpus with millions of datasets and papers as well as the trusted links between them. Also, a collection of entity embeddings was created in the work of this thesis. These embeddings are scientific item embeddings by using knowledge graph embedding on the citation network from Microsoft Academic knowledge graph.

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SQORE: an ontology retrieval framework for the next generation Web

Ungrangsi

Anutariya

Wuwongse

2009

Concurrency and Computation

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SUMMARYIn order to resolve semantic issues in the Semantic Web and in the Semantic Knowledge Grid, tools for retrieving a suitable ontology from an ontology database are essential. However, existing approaches to ontology retrieval base their search mechanisms solely on keyword matching and return a lengthy list of relevant ontologies that may not satisfy user requirements. Users, therefore, are not equipped with expressive means to structurally and semantically describe their ontology needs. To tackle this problem, this paper develops a framework for Semantic Query based Ontology Retrieval, namely, SQORE. It enables precise formulation of a semantic query in order to best capture a user's ontology requirements, which include not only the desired class and property names, but also their relations and restrictions. SQORE employs the XML Declarative Description theory as its theoretical foundation for modeling ontology databases and evaluating semantic queries. Moreover, similarity score is formally defined as a key metric for ranking the resulting ontologies based on their conceptual closeness, their query coverage and their compactness with respect to the given query. A Web-based prototype system is developed as a proof of concept, and experiments are conducted to evaluate the effectiveness of the SQORE framework. Experimental results indicate that SQORE yields better results and better rankings compared with existing systems.

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OntoKhoj

Cited by 98 publications

References 2 publications

Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Scientific dataset recommendation with semantic techniques

SQORE: an ontology retrieval framework for the next generation Web

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