GoPubMed: Exploring PubMed with Ontological Background Knowledge

Dietze, Heiko; Alexopoulou, Dimitra; Alvers, Michael R.; Barrio-Alvers, Liliana; Andreopoulos, Bill; Doms, Andreas; Hakenberg, Jörg; Mönnich, Jan; Plake, Conrad; Reischuck, Andreas; Royer, Loïc; Wächter, Thomas; Zschunke, Matthias; Schroeder, Michael

doi:10.1007/978-1-59745-440-7_20

Cited by 20 publications

(19 citation statements)

References 17 publications

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“…The activities can be divided into development of the TBox by means of searching for candidate terms and relations in text documents, and ontology populations (ABox) that amounts to document retrieval enhanced by ontologies and tagged text (e.g., GoPubMed (Dietze et al, 2008)). The former is known to be challenging and an iterative process that requires considerable domain expert input Gliozzo et al, 2007).…”

Section: Bottom-up Development Of Ontologies Through Ontology Learningmentioning

confidence: 99%

Dependencies between ontology design parameters

Keet

2010

IJMSO

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Development and use of ontologies is increasing, but hampered by new challenges, such as determining which ontologies to reuse and which language to use. Ontology development methodologies provide scenarios, but they do not address the dependencies between the permutations at the different stages in the development process. To improve the efficiency and effectiveness of this endeavour, we group inputs into parameters and examine the dependencies between them. The parameters considered are: nine main purpose(s) of the ontology, reuse of the three main types of ontologies, eight different ways for bottom-up ontology development, eight ontology languages, and four types of reasoning services. All dependencies between these parameters have been assessed and useful combinations are motivated. The dependencies are due to, primarily, computational challenges and types of subject domain of the ontologies. This analysis is assessed against a set of ontologies and a survey among ontology developers, whose results concur with the theoretical assessment.

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Section: Bottom-up Development Of Ontologies Through Ontology Learningmentioning

confidence: 99%

Dependencies between ontology design parameters

Keet

2010

IJMSO

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“…FindUR [9], Melisa [10] and GoPubMed [11] are ontology-based front-ends to a traditional publication full-text search. They allow either for effective restriction and intelligent visualisation of the query results (GoPubMed), or for focusing the queries onto particular topics based on an ontology (FindUR and Melisa).…”

Section: Related Workmentioning

confidence: 99%

CORAAL – Towards Deep Exploitation of Textual Resources in Life Sciences

Nováček

Groza

Handschuh

2009

Artificial Intelligence in Medicine

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Abstract. Prominent biomedical literature search tools like ScienceDirect, PubMed Central or MEDLINE allow for efficient retrieval of resources based on key words. Due to vast amounts of data available in life sciences, key word search is not always sufficient, though. One would often welcome more intelligent search for knowledge, i.e., for concepts and their mutual relations. This is, however, still a major challenge, since getting the necessary machine-readable knowledge manually is virtually impossible in large scale, while its automatic extraction is not particularly reliable. We have researched a novel framework actually enabling practical exploitation of automatically extracted knowledge, though. On the top of the framework, we implemented CORAAL, a prototype for knowledge-based biomedical literature search. This paper describes its essential principles, innovative capabilities and current results.

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“…FindUR [15], Melisa [1] and GoPubMed [8] are ontologybased front-ends to a traditional publication full-text search. They allow either for effective restriction and intelligent visualisation of the query results (GoPubMed), or for focusing the queries onto particular topics based on an ontology (FindUR and Melisa).…”

Section: A Related Work Overviewmentioning

confidence: 99%

Knowledge-based search for oncological literature

Nováček

Groza

Handschuh

2009

2009 22nd IEEE International Symposium on Computer-Based Medical Systems

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Abstract-Using the current state of the art in life science publication search (e.g., PubMed), one can efficiently search for resources containing particular key-words or their combinations. It is impossible to search for abstract concepts and expressive relations between them (e.g., type of, different from or part of), though. Nevertheless, such a more expressive-semantic-search could largely reduce the efforts related to finding appropriate answers in biomedical articles. In this paper we identify challenges related to building a semantic publication search engine. Then we describe the architecture and usage principles of a tool tackling them. Eventually, we report on the tool's deployment on oncological literature data and preliminary tests with domain experts.

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GoPubMed: Exploring PubMed with Ontological Background Knowledge

Cited by 20 publications

References 17 publications

Dependencies between ontology design parameters

Dependencies between ontology design parameters

CORAAL – Towards Deep Exploitation of Textual Resources in Life Sciences

Knowledge-based search for oncological literature

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