Seed-Based Generation of Personalized Bio-ontologies for Information Extraction

Tao, Cui; Embley, David W.

doi:10.1007/978-3-540-76292-8_9

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…However, since automated tools usually require a huge training corpus, making intensive use of natural language processing algorithms (e.g., Aqualog [22], Watson [4]), the results are not always satisfactory [10], especially when there is the necessity to infer a higher morphological or semantic level in information retrieval systems. Pseudo-relevance feedback (PRF) is a technique used to automatically expand search queries by¯nding semantically relevant expansion words in top ranked documents [23].…”

Section: Related Work and State Of The Artmentioning

confidence: 99%

Assisted Knowledge Base Generation, Management and Competence Retrieval

Bellandi

Bellini

Cappuccio

et al. 2012

Int. J. Soft. Eng. Knowl. Eng.

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Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting di®erent kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in de¯ning relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

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Section: Related Work and State Of The Artmentioning

confidence: 99%

Assisted Knowledge Base Generation, Management and Competence Retrieval

Bellandi

Bellini

Cappuccio

et al. 2012

Int. J. Soft. Eng. Knowl. Eng.

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“…Tao and Embley [11] have analyzed how different generelated information is stored in different biological data sources. This study has been very useful for us since they have detected a set of relevant data inconsistencies, data redundancies, some incomplete information, and a set of data defects.…”

Section: Related Workmentioning

confidence: 99%

Integrating Human Genome Variation Data: An Information System Approach

Martin

Celma

2011

2011 22nd International Workshop on Database and Expert Systems Applications

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The goal of this work is to design and develop an Information System that integrates human genome variation data currently scattered in different repositories. The continuous and increasing interest generated around the variations knowledge, makes the study of this research topic from an Information System point of view extremely attractive.The system has been developed following a conceptual-model based methodology. The conceptual model represents, in a formal way, genome variation knowledge. The definition and categorization of variations is unified using this conceptualization. Once this conceptual model is established, it is implemented in a database (Human Genome DataBase, HGDB). The database acts as a unified variation repository of integrated information that will allow biologists to perform efficient recovery tasks. Lastly, a loading module has been implemented, using an extraction-transformation-load (ETL) strategy, in order to integrate data from three relevant variation repositories: HapMap, Ensembl and Cosmic. An exploitation module for final users is also provided.

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“…But how this process is done and what format the generated ontologies have is not discussed in the paper. SIH [40] and TANGO [43] are two ongoing projects we are currently working on. SIH tries to generate user-specified ontologies depending on user-generated forms.…”

Section: Ontology Generationmentioning

confidence: 99%

“…Tools such as SIH [40], TANGO [43], and the one developed by Pivk [34] use structured information (HTML tables) as a source for learning ontologies. Structured information makes it easier to interpret new items and relations.…”

Section: Ontology Generationmentioning

confidence: 99%

Automatic hidden-web table interpretation, conceptualization, and semantic annotation

Tao

Embley

2009

Data & Knowledge Engineering

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The longstanding problem of automatic table interpretation still illudes us. Its solution would not only be an aid to table processing applications such as large volume table conversion, but would also be an aid in solving related problems such as information extraction, semantic annotation, and semi-structured data management. In this paper, we offer a solution for the common special case in which so-called sibling pages are available. The sibling pages we consider are pages on the hidden web, commonly generated from underlying databases. Our system compares them to identify and connect nonvarying components (category labels) and varying components (data values). We tested our solution using more than 2,000 tables in source pages from three different domains -car advertisements, molecular biology, and geopolitical information. Experimental results show that the system can successfully identify sibling tables, generate structure patterns, interpret tables using the generated patterns, and automatically adjust the structure patterns as it processes a sequence of hidden-web pages. For these activities, the system was able to achieve an overall F-measure of 94.5%. Further, given that we can automatically interpret tables, we next show that this leads immediately to a conceptualization of the data in these interpreted tables and thus also to a way to semantically annotate these interpreted tables with respect to the ontological conceptualization. Labels in nested table structures yield ontological concepts and interrelationships among these concepts, and associated data values become annotated information. We further show that semantically annotated data leads immediately to queriable data. Thus, the entire process, which is fully automatic, transform facts embedded within tables into facts accessible by standard query engines.

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Seed-Based Generation of Personalized Bio-ontologies for Information Extraction

Cited by 6 publications

References 8 publications

Assisted Knowledge Base Generation, Management and Competence Retrieval

Assisted Knowledge Base Generation, Management and Competence Retrieval

Integrating Human Genome Variation Data: An Information System Approach

Automatic hidden-web table interpretation, conceptualization, and semantic annotation

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