Improving Knowledge Discovery in Document Collections through Combining Text Retrieval and Link Analysis Techniques

Jin, Wei; Srihari, Rohini K.; Ho, Hung Hay; Wu, Xin

doi:10.1109/icdm.2007.62

Cited by 29 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…concept chains [8], network analysis [16], and logical reasoning [24] (see [21] for a survey of such extensions). For a concept pair A and C, these models identify the most obvious B and return a ranking of pairs using measures such as average minimum weight, linking term count and and literature cohesiveness [26].…”

Section: Literature-based Discovery: the State Of The Artmentioning

confidence: 99%

Improving Literature-Based Discovery with Advanced Text Mining

Korhonen

Guo

Baker

et al. 2015

Computational Intelligence Methods for Bioinformatics and Biostatistics

View full text Add to dashboard Cite

Abstract. Automated Literature Based Discovery (LBD) generates new knowledge by combining what is already known in literature. Facilitating large-scale hypothesis testing and generation from huge collections of literature, LBD could significantly support research in biomedical sciences. However, the uptake of LBD by the scientific community has been limited. One of the key reasons for this is the limited nature of existing LBD methodology. Based on fairly shallow methods, current LBD captures only some of the information available in literature. We discuss how advanced Text Mining based on Information retrieval, Natural Language Processing and data mining could open the doors to much deeper, wider coverage and dynamic LBD better capable of evolving with science, in particular when combined with sophisticated, state-of-the-art knowledge discovery techniques.

show abstract

Section: Literature-based Discovery: the State Of The Artmentioning

confidence: 99%

Improving Literature-Based Discovery with Advanced Text Mining

Korhonen

Guo

Baker

et al. 2015

Computational Intelligence Methods for Bioinformatics and Biostatistics

View full text Add to dashboard Cite

show abstract

“…Quest and Ali [31] used ontologies to help data mining in biological databases. Jin et al [17] integrated data mining and information retrieval techniques to further enhance knowledge discovery. Doan et al [8] proposed a model called GLUE and used machine learning techniques to find similar concepts in different ontologies.…”

Section: Ontology Learningmentioning

confidence: 99%

A Personalized Ontology Model for Web Information Gathering

Tao

Zhong

2011

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

show abstract

“…Bisociations are finally discovered by evaluating the similarity among nodes with vector-based similarity measures. A similar representation is used in [5], where a method combining frequent itemset mining and link analysis is proposed to identify chains of named entities and verbal forms (concepts) extracted from texts. A graph-structure is created by assigning frequent 2-itemsets (pairs of concepts) to a pair of nodes connected by an edge.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Discovering Temporal Bisociations for Linking Concepts over Time

Loglisci

Ceci

2011

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

Abstract. Bisociations represent interesting relationships between seemingly unconnected concepts from two or more contexts. Most of the existing approaches that permit the discovery of bisociations from data rely on the assumption that contexts are static or considered as unchangeable domains. Actually, several real-world domains are intrinsically dynamic and can change over time. The same domain can change and can become completely different from what/how it was before: a dynamic domain observed at different time-points can present different representations and can be reasonably assimilated to a series of distinct static domains. In this work, we investigate the task of linking concepts from a dynamic domain through the discovery of bisociations which link concepts over time. This provides us with a means to unearth linkages which have not been discovered when observing the domain as static, but which may have developed over time, when considering the dynamic nature. We propose a computational solution which, assuming a time interval-based discretization of the domain, explores the spaces of association rules mined in the intervals and chains the rules on the basis of the concept generalization and information theory criteria. The application to the literature-based discovery shows how the method can re-discover known connections in biomedical terminology. Experiments and comparisons using alternative techniques highlight the additional peculiarities of this work.

show abstract

Improving Knowledge Discovery in Document Collections through Combining Text Retrieval and Link Analysis Techniques

Abstract: Abstract

Cited by 29 publications

References 12 publications

Improving Literature-Based Discovery with Advanced Text Mining

Improving Literature-Based Discovery with Advanced Text Mining

A Personalized Ontology Model for Web Information Gathering

Discovering Temporal Bisociations for Linking Concepts over Time

Contact Info

Product

Resources

About