What is she best known for?
The fields of study she is best known for:
- Database
- Artificial intelligence
- Operating system
Her main research concerns Information retrieval, Ontology alignment, Data mining, Data integration and User interface.
Her Information retrieval study combines topics in areas such as World Wide Web, XML database and XML schema.
Ontology alignment is a subfield of Ontology that she explores.
In Data integration, Isabel F. Cruz works on issues like Ontology-based data integration, which are connected to Upper ontology, Conceptualization and Representation.
Her User interface research includes themes of Usability and Human–computer interaction.
Her work on Semantic Web Stack as part of general Semantic Web study is frequently linked to Extensible architecture and Structural alignment, bridging the gap between disciplines.
Her most cited work include:
- The Semantic Web - ISWC 2006 (621 citations)
- A graphical query language supporting recursion (242 citations)
- AgreementMaker: efficient matching for large real-world schemas and ontologies (229 citations)
What are the main themes of her work throughout her whole career to date?
Isabel F. Cruz mainly focuses on Information retrieval, Ontology alignment, World Wide Web, Ontology and Semantic Web.
Her study of Ontology-based data integration is a part of Information retrieval.
In her research on the topic of Ontology alignment, Theoretical computer science is strongly related with Data mining.
The various areas that Isabel F. Cruz examines in her World Wide Web study include User interface and Visualization.
Her Ontology study incorporates themes from Linked data and Data integration.
A large part of her Semantic Web studies is devoted to Semantic Web Stack.
She most often published in these fields:
- Information retrieval (38.46%)
- Ontology alignment (21.98%)
- World Wide Web (21.43%)
What were the highlights of her more recent work (between 2014-2021)?
- Information retrieval (38.46%)
- Ontology (20.88%)
- Ontology alignment (21.98%)
In recent papers she was focusing on the following fields of study:
Isabel F. Cruz mainly focuses on Information retrieval, Ontology, Ontology alignment, Data mining and Artificial intelligence.
Isabel F. Cruz merges many fields, such as Information retrieval and Facet, in her writings.
Her biological study spans a wide range of topics, including Predictive analytics, Linked data, Semantic Web, Data integration and Semantics.
Ontology alignment is a primary field of her research addressed under Ontology-based data integration.
Her Data mining study integrates concerns from other disciplines, such as Computer network programming and Robustness.
Her research in Artificial intelligence focuses on subjects like Natural language processing, which are connected to Telegraphic speech, Vocabulary, Query language and Trip planning.
Between 2014 and 2021, her most popular works were:
- Tackling the challenges of matching biomedical ontologies (18 citations)
- Results of AML participation in OAEI 2018. (11 citations)
- OAEI 2016 Results of AML (10 citations)
In her most recent research, the most cited papers focused on:
- Database
- Artificial intelligence
- Operating system
Isabel F. Cruz spends much of her time researching Artificial intelligence, Natural language processing, Ontology alignment, Data mining and Information retrieval.
Her study focuses on the intersection of Artificial intelligence and fields such as Query language with connections in the field of Ontology.
Her Ontology research integrates issues from Trip planning, Association rule learning, Filter, Semantics and Open data.
Her work on Relationship extraction, Predicate and Noun phrase is typically connected to Evaluation methods and Extractor as part of general Natural language processing study, connecting several disciplines of science.
Her studies in Ontology alignment integrate themes in fields like Multi-user, Computer network programming, Theoretical computer science and Robustness.
She works on Information retrieval which deals in particular with Open Biomedical Ontologies.
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