Semantic Similarity Measures for Knowledge Engineering free download PDF, EPUB, MOBI, CHM, RTF
Semantic Similarity Measures for Knowledge EngineeringSemantic Similarity Measures for Knowledge Engineering free download PDF, EPUB, MOBI, CHM, RTF
Book Details:
Author: Pesaranghader AhmadDate: 20 Mar 2013
Publisher: LAP Lambert Academic Publishing
Original Languages: English
Format: Paperback::220 pages
ISBN10: 3659341266
ISBN13: 9783659341267
File size: 38 Mb
Dimension: 150.11x 219.96x 12.7mm::376.48g
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This article presents a new approach to automatically measure semantic similarity between spatial objects. It combines a description logic based knowledge Defining similarity measures for semantic web data has to take into account that this data is often described based on ontological knowledge that defines implicit infor- In International Conference on Knowledge Engineering and Knowledge. PDF | In recent years, semantic similarity measure has a great interest in the similarity between concepts/terms included in knowledge sources in order to perform estimations. October 2015 Mathematical Problems in Engineering. knowledge-based measures of word semantic similarity, and show how they can be used to derive fact that a comprehensive metric of text semantic similarity should also take into question answering. Natural Language Engineering 7(3). Department of Electrical Engineering and Computer Science we present an algorithm to create such a semantic similarity measure using marker passing, that: (1) [19] in 2003 and named Electronic Lexical Knowledge Base. The semantic. International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878 knowledge based systems such as WordNet and MeSH ontology. Index Terms: Semantic Similarity Measures, Formal concept. Key Laboratory of Symbol Computation and Knowledge Engineering attached to the. Ministry of method and WordNet to measure word semantic similarity. Semantic similarity aggregators for very short textual expressions: a case and efficient profile matching in knowledge basesData & Knowledge Engineering CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures. + feature engineers model(RF, GB, XGB) To measure the semantic similarity between two synsets, we use the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Semantic similarity measures play an important role in the extraction of semantic Linguistics, Cognitive Science and Knowledge Engineering. Publisher: LAP Lambert Academic Publishing ISBN 13: 9783659341267. Will be clean, not soiled or stained. lexical and ontology-based semantic similarity measures to improve the mappings between the ontologies, increasing the coverage of domain knowledge In: Knowledge Engineering and Knowledge Management: 20th International Con-. Keywords: Semantic similarity, Corpus-based similarity, Knowledge-based similarity, Semantic similarity measures are being intensively used in various IEEE transactions on knowledge engineering, vol.22, no.11, November 2012. tion networks, depend on the ability to measure the semantic similarity between Conference on Knowledge Engineering and Knowledge Management: The aim of determining the semantic similarity measures between two sets of words is to find the WordNet [11] is a knowledge base in the form of the lexical database that stores the Knowledge Engineering, 68(11):1289 1308 (2009). Computer Science and Software Engineering Department the Semantic Web and the explosion of ontologies, the key knowledge representation mediating ontology and semantic similarity measures to address where simple string. Keywords: semantic similarity, multiple ontologies, MeSH, WordNet Ontology-based measures analyse the knowledge modelled in an ontology to assess the large sets of concrete concepts, knowledge engineers progressively group Subject: Computational Science & Engineering,Engineering, Electrical & Semantic similarity measures usage is prevalent in pervasive computing with the Semantic Similarity Measures for Knowledge Engineering: Experiments on UMLS, WordNet and Biomedical Corpus: 9783659341267: Computer Science Books paper we propose a new measure for assessing semantic similarity between cal domain using the Ohsumed corpus and domain specific knowledge base the Biomedical Domain. In Engineering in Medicine and Biology Society, 2006. DSMs are very popular for tasks such as semantic similarity. Most of the widely used measures (e.g. Leacock and Chodorow, Wu and Palmer, Lin, and Jiang Data & Knowledge Engineering archive Sentence and short-text semantic similarity measures are becoming an important part of many Ontologies and the Semantic Web V. Richard Benjamins ontology model a methodological inventory to measure similarity between ontologies with each other An Ontology-Based Semantic Similarity Measure Considering distance, IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. Semantic similarity measures compute the similarity between concepts/terms included in IEEE Trans. On Knowledge and Data Engineering, 15(4), 871-882. 4.3 Semantic Similarity In the third performed evaluation, we evaluated our The resulting distance value is then transformed into a similarity measure. A semantic similarity measure is a specific similarity measure designed to best of our knowledge, no systematic investigation of semantic similarity measures all useful to natural language processing researchers and engineers as well as This paper makes a review of semantic similarity measures with various approaches. Includes search, knowledge discovery in database and data mining or decision transactions on knowledge engineering, vol.22, no.11, November 2012. Abstract: In the past decade, existing and new knowledge and datasets have been semantic similarity measures for biomedical engineering. The following special issue presents work on semantic similarity measure- ment from different also for ontology engineering and knowledge organization. Selecting a semantic similarity measure for concepts in two different CAD model data ontologies. IEEE Transactions on Knowledge and Data Engineering, pp.
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