Generating complex ontology instances from documents

Dánger, Roxana; Berlanga, Rafael

doi:10.1016/j.jalgor.2009.02.006

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…Finally, at the highest level of the architecture, the ontological instance generation module produces an ontological representation, as much complete as possible, of the all mentioned concepts and entities in a text. For doing this, we will follow the technique described in [59], which produced satisfactory results in the archeology domain.…”

Section: Named Entity Detectionmentioning

confidence: 99%

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Dánger

Pla

Molina

et al. 2014

Knowledge-Based Systems

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The majority of biological functions of any living being are related to ProteinProtein Interactions (PPI). PPI discoveries are reported in form of research publications whose volume grows day after day. Consequently, automatic PPI information extraction systems are a pressing need for biologists. In this paper we are mainly concerned with the named entity detection module of PPIES (the PPI Information extraction system we are implementing) which recognizes twelve entity types relevant in PPI context. It is composed of two sub-modules: a dictionary look-up with extensive normalization and acronym detection, and a Conditional Random Field classifier. The dictionary look-up module has been tested with Interaction Method Task (IMT), and it improves by approximately 10% the current solutions that do not use Machine Learning (ML). The second module has been used to create a classifier using the Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA'04) data set. It does not use any external resources, or complex or ad-hoc post-processing, and obtains 77.25%, 75.04% and 76.13 for precision, recall, and F1-measure, respectively, improving all previous results obtained for this data set.

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Section: Named Entity Detectionmentioning

confidence: 99%

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Dánger

Pla

Molina

et al. 2014

Knowledge-Based Systems

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“…However, this is not appropriate for expressive and heterogeneous annotations. By combining IE techniques with logical reasoning, in [18] they propose a MD model specially devised to select, group and aggregate the instances of an ontology. In our previous work [31] we define the semantic data warehouse as a new semi-structured repository consisting of semantic annotations along with their associated set of ontologies.…”

Section: Related Workmentioning

confidence: 99%

Building data warehouses with semantic web data

NebotVictoria

BerlangaRafael

2012

Decision Support Systems

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The Semantic Web (SW) deployment is now a realization and the amount of semantic annotations is ever increasing thanks to several initiatives that promote a change in the current Web towards the Web of Data, where the semantics of data become explicit through data representation formats and standards such as RDF/(S) and OWL. However, such initiatives have not yet been accompanied by efficient intelligent applications that can exploit the implicit semantics and thus, provide more insightful analysis. In this paper, we provide the means for efficiently analyzing and exploring large amounts of semantic data by combining the inference power from the annotation semantics with the analysis capabilities provided by OLAP-style aggregations, navigation, and reporting. We formally present how semantic data should be organized in a well-defined conceptual MD schema, so that sophisticated queries can be expressed and evaluated. Our proposal has been evaluated over a real biomedical scenario, which demonstrates the scalability and applicability of the proposed approach.

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“…However, the proliferation of comprehensive KRs has extended the notion of entity to any kind of object or concept. For example, in (Dánger and Berlanga, 2009;Berlanga et al, 2010), domain ontology concepts are considered potential entities whose instances occur in the texts. In the biomedical scenario, proteins, diseases and organs are considered entities to be identified from texts.…”

Section: Normalizationmentioning

confidence: 99%

A semantic approach for the requirement-driven discovery of web resources in the Life Sciences

et al. 2012

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Abstract. Research in the Life Sciences depends on the integration of large, distributed and heterogeneous web resources (e.g. data sources and web services). The discovery of which of these resources are the most appropriate to solve a given task is a complex research question, since there are many candidate resources and there is little, mostly unstructured, metadata to be able to decide among them.In this paper we contribute with a semi-automatic approach, based on semantic techniques, to assist researchers in the discovery of the most appropriate web resources to fulfill a set of requirements. The main feature of our approach is that it exploits broad knowledge resources in order to annotate the unstructured texts that are available in the emerging web-based repositories of web resource metadata.The results show that the web resource discovery process benefits from a semanticbased approach in several important aspects. One of the advantages is that the user can express her requirements in natural language avoiding the use of specific vocabularies or query languages. Moreover, the discovery exploits not only the categories or tags of web resources, but also their description and documentation.

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Generating complex ontology instances from documents

Cited by 15 publications

References 16 publications

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Towards a Protein–Protein Interaction information extraction system: Recognizing named entities

Building data warehouses with semantic web data

A semantic approach for the requirement-driven discovery of web resources in the Life Sciences

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