Computing Similarities between Natural Language Descriptions of Knowledge and Skills (original) (raw)
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When evaluating job applications, recruiters and employers try to determine whether the information that is provided by a job seeker is accurate and whether it describes an individual that possesses sufficient skills. These questions are related to the hidden skills and skills resolution problems. In this paper we argue that using a skills taxonomy to identify and resolve unknown relationships between text that describe an applicant and job descriptions is the best way for addressing these problems. Unfortunately, no comprehensive , publicly available taxonomy exists. To this end, this work proposes an automated process for creating a skills taxonomy. Effective and efficient methods for bootstrapping a taxon-omy are critical to any process that names and characterizes the properties and interrelationships of entities. To this end, we present three potential methods for bootstrapping and extending our skills taxonomy. We propose a hybrid scheme that combines the beneficial features of those methods. Our hybrid approach seeds the bootstrapping process with publicly available resources and identifies new skill terms and corresponding entity relationships. In this paper, we focus specifically on using Wikipedia as our corpus and exploiting its structure to populate the taxonomy. We begin by constructing a relationship graph of possible skill terms from Wikipedia. We then use a data mining methodology to identify skill terms. Our results are promising, and we are able to achieve a 98% classification rate.
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Proceedings of the TextGraphs-8 Workshop , pages 79–87, Seattle, Washington, USA, 18 October 2013. 2013 Association for Computational Linguistics , 2013
This paper presents a system that performs skill extraction from text documents. It outputs a list of professional skills that are relevant to a given input text. We argue that the system can be practical for hiring and management of personnel in an organization. We make use of the texts and the hyperlink graph of Wikipedia, as well as a list of professional skills obtained from the LinkedIn social network. The system is based on first computing similarities between an input document and the texts of Wikipedia pages and then using a biased, hub-avoiding version of the Spreading Activation algorithm on the Wikipedia graph in order to associate the input document with skills.
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In current organizations, the importance of knowledge and competence is unquestionable. In Information Technology (IT) companies, which are, by definition, knowledge intensive, this importance is critical. In such organizations, the models of knowledge exploitation include specific processes and elements that drive the production of knowledge aimed at satisfying organizational objectives. However, competence evidence recollection is a highly intensive and time consuming task, which is the key point for this system. SeCEC-IT is a tool based on software artifacts that extracts relevant information using natural language processing techniques. It enables competence evidence detection by deducing competence facts from documents in an automated way. SeCEC-IT includes within its technological components such items as semantic technologies, natural language processing, and human resource communication standards (HR-XML).
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Synthesis Lectures on Human Language Technologies is edited by Graeme Hirst of the University of Toronto. e series consists of 50-to 150-page monographs on topics relating to natural language processing, computational linguistics, information retrieval, and spoken language understanding. Emphasis is on important new techniques, on new applications, and on topics that combine two or more HLT subfields.
Finding Skills through ranked semantic match of descriptions
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Abstract: We propose a formal approach to Ontology-Based Semantic Matchmaking between Skills request and offer, devised as a virtual marketplace of knowledge. In such a knowledge market metaphor, skills are a peculiar kind of goods that have distinguishing characteristics with respect to traditional assets. Buyers are entities that need the skills of people, such as projects, departments and organizations; sellers are workers that offer their own skills.
159. Concept Similarity Measures the Understanding Between Two Agents
Lecture Notes in Computer Science 3136, 2004
"When knowledge in each agent is represented by an ontology of concepts and relations, concept communication can not be fulfilled through exchanging concepts (ontology nodes). Instead, agents try to communicate with each other through a common language, which is often ambiguous (such as a natural language), to share knowledge. This ambiguous language, and the different concepts they master, give rise to imperfect understanding among them: How well concepts in ontology OA map1 to which of OB? Using a method sim that finds the most similar concept in OB corresponding to another concept in OA, we present two algorithms, one to measure the similarity between both concepts; another to gauge du, the degree of understanding that agent A has about B’s ontology. The procedures use word comparison, since no agent can measure du directly. Method sim is also compared with conf, a method that finds the confusion among words in a hierarchy. Examples follow.""""
Similarity Intelligence: Similarity Based Reasoning, Computing, and Analytics
Journal of Computer Science Research
Similarity has been playing an important role in computer science, artificial intelligence (AI) and data science. However, similarity intelligence has been ignored in these disciplines. Similarity intelligence is a process of discovering intelligence through similarity. This article will explore similarity intelligence, similarity-based reasoning, similarity computing and analytics. More specifically, this article looks at the similarity as an intelligence and its impact on a few areas in the real world. It explores similarity intelligence accompanying experience-based intelligence, knowledge-based intelligence, and data-based intelligence to play an important role in computer science, AI, and data science. This article explores similarity-based reasoning (SBR) and proposes three similarity-based inference rules. It then examines similarity computing and analytics, and a multiagent SBR system. The main contributions of this article are: 1) Similarity intelligence is discovered from ...