No abstract

Semantic Computing extends Semantic Web both in breadth and depth. It bridges, and integrates, technologies such as software engineering, user interface, natural language processing, artificial intelligence, programming language, grid computing and pervasive computing, among others, into a complete and unified theme. Cloud Computing, the dream of computing as a utility, shifts user programs and data from personal computers to the clouds, providing all kinds of resources over the Internet as services to customers and letting customers pay for them in a way much like they pay for traditional utilities such as electricity. This paper analyzes both Semantic Computing and Cloud Computing, and introduces the Semantic Search Engine, an infrastructure and implementation of Semantic Computing, that demonstrates how Semantic Computing can benefit Cloud Computing.

While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet 1 or Wikitionary 2 , the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform the lexicon-based methods. However, word senses are not disambiguated in the training phase of both Word2Vec and GloVe, two famous word embedding algorithms, and the path length between any two senses of words in lexical databases cannot reflect their true semantic relatedness. In this paper, a novel approach that linearly combines Word2Vec and GloVe with the lexical database WordNet is proposed for measuring semantic relatedness. The experiments show that the simple method outperforms the state-of-the-art model SensEmbed.

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state-of-the-art approaches. The experimental results show that our method can outperform state-of-the-art approaches in all the selected English benchmark data sets.