Combining Query Terms Extension and Weight Correlative for Expert Finding

Yang, Kai Hsiang; Yu, Lin; Wang, Jenq-Haur

doi:10.1109/wi-iat.2014.51

Cited by 7 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variation of TFIDF was also applied for expert finding in an organization's ERP system [21]. The work [4] also used TFIDF to identify experts given a topic using a topic extension method (finding interrelated terms of a given topic from the corpus), where TFIDF was used to estimate relevance between extended terms and each expert's documents. TFIDF was also used to estimate the weights of topics indicating the interests of an expert, and this information is used with fuzzy logics for expert finding [5].…”

Section: Related Workmentioning

confidence: 99%

“…ufmg.br/lbd/collections. [7] used two datasets with seven topics, two datasets with 13 and 203 topics and one dataset with 14 topics, respectively 4 . Note that our evaluation have been done using the larger numbers of the topics on the four datasets as seen in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

“…Most existing approaches for expert finding are based on vector space models (VSM), document language models (DLM), or graph-based models (GM). In VSM, expert finding is often solved by modeling the weights of topics, associated with the documents produced by experts, using Term Frequency-Inverse Document Frequency (TFIDF) or its variation [4,5]. In DLM, expert finding is achieved by estimating the probability that a topic would be observed in the documents of an expert [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

View full text Add to dashboard Cite

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose ExpFinder, a new ensemble model for expert finding, that integrates a novel N -gram vector space model, denoted as nVSM, and a graph-based model, denoted as µCO-HITS, that is a proposed variation of the CO-HITS algorithm. The key of nVSM is to exploit recent inverse document frequency weighting method for N -gram words, and ExpFinder incorporates nVSM into µCO-HITS to achieve expert finding. We comprehensively evaluate ExpFinder on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that ExpFinder is an highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Kang¹,

Du²,

Forkan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Their experimental results demonstrate that using this method outperforms most of the image retrieval systems based on the unimodal retrieval. Chuang et al 31 also use the terms correlation for expanding the query for higher retrieval precision. In the work by Safadi et al, 32 a single-level fusion is used to overcome the well-known semantic gap problem.…”

Section: Related Work Smentioning

confidence: 99%

Multimodal query-level fusion for efficient multimedia information retrieval

Sattari

Yazıcı

2018

Int. J. Intell. Syst.

View full text Add to dashboard Cite

Managing a large volume of multimedia data containing various modalities such as visual, audio, and text reveals the necessity for efficient methods for modeling, processing, storing, and retrieving complex data. In this paper, we propose a fusion‐based approach at the query level to improve query retrieval performance of multimedia data. We discuss various flexible query types including the combination of content as well as concept‐based queries that provide users with the ability to efficiently perform multimodal querying. We have carried out a number of experiments on a video database to show the efficiency of our approach for various types of queries. Our experimental results show that our query‐level fusion approach presents a notable improvement in retrieval performance especially for the concept‐based queries.

show abstract

“…Identifying experts given a query topic, known as expert finding, is a crucial task that accelerates rapid team formation for research innovations or business growth. Existing expert finding models can be classified into three categories such as vector space models (VSM) [2,3], document language models (DLM) [4,5,6], or graph-based models (GM) [7,8,9]. ExpFinder [1] is an ensemble model for expert finding which integrates a novel N -gram VSM (nVSM) with a GM (µCO-HITS)-a variant of the generalised CO-HITS algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

An open-source framework for ExpFinder integrating $N$-gram Vector Space Model and $μ$CO-HITS

Du,

Kang

2021

Preprint

View full text Add to dashboard Cite

Finding experts drives successful collaborations and high-quality product development in academic and research domains. To contribute to the expert finding research community, we have developed ExpFinder which is a novel ensemble model for expert finding by integrating an N -gram vector space model (nVSM) and a graph-based model (µCO-HITS). This paper provides descriptions of ExpFinder's architecture, key components, functionalities, and illustrative examples. ExpFinder is an effective and competitive model for expert finding, significantly outperforming a number of expert finding models as presented in [1].

show abstract

Combining Query Terms Extension and Weight Correlative for Expert Finding

Cited by 7 publications

References 12 publications

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS

Multimodal query-level fusion for efficient multimedia information retrieval

An open-source framework for ExpFinder integrating $N$-gram Vector Space Model and $μ$CO-HITS

Contact Info

Product

Resources

About