LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval

Gonçalves, Alexandre Leopoldo; Zhu, Jianhan; Song, Dawei; Uren, Victoria; Pacheco, Roberto Carlos dos Santos

doi:10.1007/11775300_11

Cited by 11 publications

(13 citation statements)

References 10 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If we use some technologies [8] related to textual representation, we can also recover subjects similar to most cited posts in the presentation. This make it easier to access and search old records.…”

Section: Figur Figur Figurmentioning

confidence: 99%

“…To use resources of text extraction technology to link texts and representation, we need clean codes. An idea about how to extract these emails in a cleaner and organized form is creating XML archives, with defined tags, such as author, dates, subject and text of each post of the discussion list or the communities of practice, as proposed by Gonçalves [8], as illustrated in Figure 2.…”

Section: Figur Figur Figurmentioning

confidence: 99%

See 1 more Smart Citation

Graphical Representations of Message Exchange in Online Collaborative Environments: Uncovering Implicit Knowledge

Paulino

Kern

2007

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

View full text Add to dashboard Cite

Communicationin online collaborative environments such as communities of practice, discussion lists, or institutional e-mail repositories hide information and knowledge that is imperceptible to the individual, despite the effort made in the storage of messages. This paper presents and ongoing work on the graphical representation of aspects of communication in online collaborative environments, aimed at uncovering the implicit knowledge hidden in the archived messages. We review the literature on graphical representation of information in online collaborative environments, followed by a pilot study on the extraction and filtering of messages of a community of practice, with rules for the graphical representation of frequent terms used in the messages and of the evolution of those frequencies in time. The graphical representation allows for the understanding of qualitative aspects of communication, such as the identification of hot topics and trends, and the forming of social networks among the participants.

show abstract

Section: Figur Figur Figurmentioning

confidence: 99%

Section: Figur Figur Figurmentioning

confidence: 99%

Graphical Representations of Message Exchange in Online Collaborative Environments: Uncovering Implicit Knowledge

Paulino

Kern

2007

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

View full text Add to dashboard Cite

show abstract

“…Closely related works to ours are [21], [6] and [10]. As an early proposal, [21] enriched queries and texts with NE tags, which were used together with usual 15 keywords for text retrieval.…”

Section: Related Workmentioning

confidence: 99%

Semantic Search by Latent Ontological Features

Cao

Ngo

2012

New Gener. Comput.

View full text Add to dashboard Cite

Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.The usefulness and explosion of information on the WWW have been challenging research on information retrieval, regarding how that rich and huge resource of information should be exploited efficiently. Information retrieval is not a new area but still attracts much research effort, social and industrial interests, because, on the one hand, it is important for searching required information and, on the other hand, there are still many open problems to be solved to enhance search performance. Retrieval precision and recall could be improved by developing appropriate models, typically as similarity-based, 9, 23) probabilistic relevance, 27) or probabilistic inference 28) ones. Semantic annotation, representation, and processing of documents and queries are another way to obtain better search quality. 6,10,12,29)

show abstract

“…Besides studies focusing on term proximity, there were works in semantic search considering ontological features of named entities to enhance document retrieval effectiveness ( [3], [4], [5]). Named entities (NE) are those that are referred to by names such as people, organizations, and locations ( [10]).…”

Section: Introductionmentioning

confidence: 99%

Ontology-based proximity search

Le¹,

Cao²,

Hoang

et al. 2011

Proceedings of the 13th International Conference on Information Integration and Web-Based Applications and Services

View full text Add to dashboard Cite

This paper presents our developed general open source for ontology-based information retrieval to answer queries that involve named entities with their ontological features, namely, aliases, classes, and identifiers. We propose a novel approach for semantic search engines that exploit the ontology features of named entities in proximity search and develop an algorithm for computing dynamic distances between named entities and keywords in queries and documents. In particular, it deals with phrase and proximity queries for which the token-based lengths and positions of the queried named entities in a document may vary. The result provides a platform and library for implementing semantic search engines.

show abstract

LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval

Cited by 11 publications

References 10 publications

Graphical Representations of Message Exchange in Online Collaborative Environments: Uncovering Implicit Knowledge

Graphical Representations of Message Exchange in Online Collaborative Environments: Uncovering Implicit Knowledge

Semantic Search by Latent Ontological Features

Ontology-based proximity search

Contact Info

Product

Resources

About