A hybrid approach to concept extraction and recognition-based matching in the domain of human resources

Crow, Daniel; DeSanto, J.

doi:10.1109/ictai.2004.12

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…There are lots of keyword based search engines available that can rank the matched documents (Kasneci et al, 2008). This is incorrect (Crow and DeSanto, 2005). This approach can understand the document and will return all documents that are semantically related to the given query (Mangold, 2007).…”

Section: Resultsmentioning

confidence: 99%

“…In large organisations, it is standard to employ an expert locator system to find out candidates who are well matched to a job requirement (Crow and DeSanto, 2005) and there are several researches have been performed to mine the information from the structured/semi structured resume documents. In large organisations, it is standard to employ an expert locator system to find out candidates who are well matched to a job requirement (Crow and DeSanto, 2005) and there are several researches have been performed to mine the information from the structured/semi structured resume documents.…”

Section: Introductionmentioning

confidence: 99%

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Expert locator using concept linking

Kumaran

Ashok

2012

IJCSYSE

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A common task in many applications is to find persons who are knowledgeable about a given topic. The 'expert locator' is a tool for finding people with relevant expertise and/or experience for a given subject. The potential value of the expert locator is directly related to the size of the searchable population. This paper addresses the issue of expert finding in educational and research domain and describes a novel method for expert finding using concept linking. Finding an expert in this domain is an important issue and it is necessary for problem consulting/solving, question answering, providing more detailed information on a topic and team building. The locator is designed to provide instant searches for people based on their qualifications, teaching experience in the field, research, activities like publications, associations, colleagues, patents, awards and panels/boards. Experimental results show that this approach can outperform the baseline approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expert locator using concept linking

Kumaran

Ashok

2012

IJCSYSE

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“…Semantic relatedness was used for extracting concepts from multi-media data. Crow and DeSanto [24] described a news video retrieval technique that utilizes extracted concepts from video shots. The semantic relations between concepts are used to build a graph and the interactions between the concepts are used as features for classification.…”

Section: A General Keyphrase Extraction Approachesmentioning

confidence: 99%

Exploring concept graphs for biomedical literature mining

Song

2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Full-text publications in an electronic form become more prevalent than ever before. It is a difficult challenge to extract concepts from unstructured document collections data because different concepts and their relationships are buried in them and ample term variations make the challenge compound. Extracted concepts are useful instruments of managing and searching large document collections and play a pivotal role in indexing electronic documents and building digital libraries. In this paper we explore a biomedical concept extraction technique based on a ranking algorithm of concept graphs. The proposed technique comprises two major steps: the first step is to represent documents with graphs whose nodes and edges are created by Named Entity Recognition and UMLS Semantic Network. The second step is rank concepts with relative importance algorithms. We evaluate our technique with a set of biomedical full-texts and compare it to various different key-phrase extraction and graph ranking techniques. The experimental results show that our technique achieves the best performance over other compared algorithms. We further take a close look at the properties of the network to examine how concepts are related to each other and what concept plays a dominant role in the network. To this end, we build the network with 526 full-text articles published in PubMed Central and measure the significance of nodes by centrality.

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“…Meanwhile, the public concept and cognate concepts should maintain paternity. 22 As a result, we need to alter the ontological graph of ontology viewpoints. The altering process is shown Figure 4…”

Section: Definition 2 Ontology O Can Be Deþned As a Seven Tuplementioning

confidence: 99%

“…Extracting concepts of domain ontology from data set is the primary method to assist ontology construction at present, 24,25 and the current research mainly focuses on extracting concepts from document set. These methods mainly adopt mathematical statistics and natural language processing, but they also have deÞciencies, for example, the precision of extraction is not high, and the completeness of concept extraction 22,23,26 results cannot be calculated. For this purpose, we put forward the method of concept extraction based on uniform semantic term mining, i.e.…”

Section: Guomentioning

confidence: 99%

A novel approach for question answering and automatic diagnosis based on pervasive agent ontology in medicine

Guo

2010

Int. J. Intell. Syst.

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With question answering system in medicine, users could use sentences in daily life to raise questions. The question answering system will analyze and comprehend these questions and return answers to users directly. Aiming at the problems in automatic diagnosis for medicine, such as low precision of question answering, imperfect expression of domain knowledge, low reuse rate, and lack of reasonable theory reference models, we put forward the information integration method of semantic Web based on pervasive agent ontology (SWPAO method) in medicine, which will integrate, analyze, and process enormous Web information and extract answers on the basis of semantics. A novel approach for automatic diagnosis in medicine based on ontology and fuzzy rough set is brought forward. The data mining algorithm for automatic diagnosis rules in medicine is brought forward: (1) computing the measurement matrix of effect; (2) extracting rules; (3) computing the importance of rules; (4) shearing the rules by genetic algorithm. In this paper, rough sets method is used to take potential diagnosis rule from the decision-making table in medicine. These rules can offer effective automatic diagnosis service. With the SWPAO method as the clue, we mainly study the method of concept extraction based on uniform semantic term mining, pervasive agent ontology construction method on account of multipoints and the answer extraction in view of semantic inference. Meanwhile, we present the structural model of the question answering system applying ontology, which adopts OWL language to describe domain knowledge base from where it infers and extracts answers by Jena inference engine, thus the precision of question answering in QA system could be improved. In the system testing, the precision has reached 86% and the recalling rate is 93%. The experiment indicates that this method is feasible, and it has the signiÞcance of reference and value of further study for the question answering systems in medicine. C

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A hybrid approach to concept extraction and recognition-based matching in the domain of human resources

Cited by 11 publications

References 5 publications

Expert locator using concept linking

Expert locator using concept linking

Exploring concept graphs for biomedical literature mining

A novel approach for question answering and automatic diagnosis based on pervasive agent ontology in medicine

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