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Fuzzy-Aggregation-Semantic-Similarity 100%

For example, it is possible to automatically identify that specific terms (e.g., Finance) yields matches on similar terms (e.g., Economics, Economic Affairs, Financial Affairs, etc.) or an expert on the treatment of cancer could also be considered as an expert on oncology or tumor treatment.

https://www.pdf-archive.com/2018/05/22/fuzzy-aggregation-semantic-similarity/

22/05/2018 www.pdf-archive.com

Word-Co-Occurrence-Literature 99%

For example, automobile and car are similar because both are means of transport.

https://www.pdf-archive.com/2018/05/28/word-co-occurrence-literature/

28/05/2018 www.pdf-archive.com

Biomedical-Semantic-Similarity 99%

Feature based Measures which measure the similarity between terms as a function of their properties or based on their relationships to other similar terms in a dictionary.

https://www.pdf-archive.com/2018/05/07/biomedical-semantic-similarity/

07/05/2018 www.pdf-archive.com

semantic-similarity 99%

at An overview of textual semantic similarity measures based on web intelligence Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields.

https://www.pdf-archive.com/2019/01/03/semantic-similarity/

03/01/2019 www.pdf-archive.com

Semantic-Similarity-Web-Intelligence 98%

date Abstract Computing the textual similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields.

https://www.pdf-archive.com/2018/05/22/semantic-similarity-web-intelligence/

22/05/2018 www.pdf-archive.com

Gas grill Ratings20150523011806 98%

Model Price Ratings and Test Results Midsize gas grills (room for 18 to 28 burgers) Gas grills Gas grill Ratings This chart includes ratings for similar and tested models.

https://www.pdf-archive.com/2015/05/23/gas-grill-ratings20150523011806/

23/05/2015 www.pdf-archive.com

Semantic-Similarity-Using-Google 98%

date Abstract Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field.

https://www.pdf-archive.com/2018/05/22/semantic-similarity-using-google/

22/05/2018 www.pdf-archive.com

Tag-Cloud-Refactoring 97%

1 shows, where tags with similar means have been grouped.

https://www.pdf-archive.com/2018/05/28/tag-cloud-refactoring/

28/05/2018 www.pdf-archive.com

ResearchPaper 97%

In an earlier work we have proposed deep learning based recommendation algorithm for recovering resolutions for incoming tickets through identification of similar tickets.

https://www.pdf-archive.com/2017/10/11/researchpaper/

11/10/2017 www.pdf-archive.com

V9I5-5 96%

Two to communicate with each other and exchange objects must have similar characteristics to be knowledge, it becomes essential for ontologies to be comparable.

https://www.pdf-archive.com/2018/05/09/v9i5-5/

09/05/2018 www.pdf-archive.com

Ontology-Matching-Heuristic 96%

Text similarity is a string based method for identifying similar entity names.

https://www.pdf-archive.com/2018/05/22/ontology-matching-heuristic/

22/05/2018 www.pdf-archive.com

Semantic-Similarity-Using-Search-Engines 96%

The idea behind this measure is that keywords with similar meanings from a natural language point of view tend to be close according to the Google distance, while words with dissimilar meanings tend to be farther apart.

https://www.pdf-archive.com/2018/05/22/semantic-similarity-using-search-engines/

22/05/2018 www.pdf-archive.com

Ontology-Matching-Genetic-Algorithms 95%

Text similarity is a string based method for identifying similar entity names.

https://www.pdf-archive.com/2018/05/22/ontology-matching-genetic-algorithms/

22/05/2018 www.pdf-archive.com

Semantic-Similarity-Human-Literature 94%

For example, it is possible to automatically identify that specific terms (e.g., Finance) yields matches on similar terms (e.g., Economics, Economic Affairs, Financial Affairs, etc.).

https://www.pdf-archive.com/2018/05/22/semantic-similarity-human-literature/

22/05/2018 www.pdf-archive.com

Validation-Semantic-Correspondences 94%

Text similarity is a string based method for identifying similar elements.

https://www.pdf-archive.com/2018/05/28/validation-semantic-correspondences/

28/05/2018 www.pdf-archive.com

Biomedical-Fuzzy-Logics 93%

For example, it could be possible for a computer to identify that specific terms (e.g., headache) yields matches on similar terms (e.g., cephalalgia) or an expert on the treatment of cancer could also be considered (to some extent) as an expert on oncology, tumor treatment, and so on.

https://www.pdf-archive.com/2018/05/07/biomedical-fuzzy-logics/

07/05/2018 www.pdf-archive.com

fdata-03-00012 92%

• FoodKG utilizes the GEMSEC (Rozemberczki et al., 2019) model that was retrained on AGROVOC with fine-tuning to produce AGROVEC to provide the semantic similarity scores between the similar and linked concepts.

https://www.pdf-archive.com/2020/06/05/fdata-03-00012/

05/06/2020 www.pdf-archive.com

Collaborative recommendation 91%

to recommend items  Basic assumption and idea – Users give ratings to catalog items (implicitly or explicitly) – Customers who had similar tastes in the past, will have similar tastes in the future -3- Pure CF Approaches  Input – Only a matrix of given user–item ratings  Output types – A (numerical) prediction indicating to what degree the current user will like or dislike a certain item – A top-N list of recommended items -4- User-based nearest-neighbor collaborative filtering (1)  The basic technique – Given an "active user"

https://www.pdf-archive.com/2015/11/05/collaborative-recommendation/

05/11/2015 www.pdf-archive.com

Crossroads-Fall-StyleGuide 91%

Find this and other similar styles at Stephan Cori.

https://www.pdf-archive.com/2017/09/21/crossroads-fall-styleguide/

20/09/2017 www.pdf-archive.com

supplementary 90%

Average pairwise similarity of vectors containing words that are distributionally similar to words in the two mentions.

https://www.pdf-archive.com/2015/02/12/supplementary/

12/02/2015 www.pdf-archive.com

EYWH self report 89%

Significantly above the average American of similar age b.

https://www.pdf-archive.com/2016/02/01/eywh-self-report/

01/02/2016 www.pdf-archive.com

KMeansRE 89%

In particular, we’ll thoroughly discuss about the essentials of the k-means clustering procedure as well as K-Means Clustering Algorithm Fundamentals In general, k-means algorithm provides a solution to the trivial classification problem by splitting up a certain dataset into k - clusters, each one containing a number of the most similar data items (or just “observations”) arranged into a cluster based on a minima distance to the nearest “mean”, which, in turn, is being a “prototype” of the following cluster.

https://www.pdf-archive.com/2016/12/13/kmeansre/

13/12/2016 www.pdf-archive.com

SAWSDL-Web-Services 86%

In this way, we can get a high degree of success when obtaining ontologies similar to an initial ontology.

https://www.pdf-archive.com/2018/05/30/sawsdl-web-services/

30/05/2018 www.pdf-archive.com

introduction to chemical engineering ch (10) 86%

(Incidentally, the similarity implies that the physical processes that cause the transfer are also similar.) Chapter 10 – Answer Key, Introduction to Chemical Engineering:

https://www.pdf-archive.com/2017/02/21/introduction-to-chemical-engineering-ch-10/

21/02/2017 www.pdf-archive.com