Measuring the Semantic Similarity of Texts

This paper discusses measuring the semantic similarity of texts

Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition

The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts

SEMI AUTOMATIC DATA CLEANING FROM MULTISOURCES BASED ON SEMANTIC HETEROGENOUS

In the business world, incorrect data can be costly. Many companies use customer information databases that record data like contact information, addresses, and preferences. If for instance the addresses are inconsistent, the company will suffer the cost of resending mail or even losing customers

Kerangka Pembersihan Data dari Sumber Data Heterogen Menggunakan Ontologi

Data adalah aset kunci untuk meningkatkan efisiensi dalam lingkungan bisnis yang dinamis dan kompetitif saat ini. data dalam suatu organisasi dapat dalam bentuk terdistribus

A Review on Computing Semantic Similarity of Concepts in Knowledge Graphs

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content as opposed to similarity which can be estimated regarding their syntactical representation (e.g. their string format). One of the drawbacks of conventional knowledge-based approaches (e.g. path or lch) in addressing such task is that the semantic similarity of any two concepts with the same path length is the same (uniform distance problem).To propose a weighted path length (wpath) method to combine both path length and IC in measuring the semantic similarity between concepts. The IC of two concepts� LCS is used to weight their shortest path length so that those concept pairs having same path length can have different semantic similarity score if they have different LCS

Technique of Semantic Unambiguity for a Concept Selection of Terms in Focused Contexts with Reinforcement Learning Integration

Nowadays, there have been many developments of learning processes for computers to understand the meaning of words and their semantic similarities in order for the computers to better communicate, interact and exchange information with humans. Semantic learning development is a major issue because computers cannot comprehend the suitable meaning of words in the concerning concept. As a result, this research is proposing and exploring the efficiency of the technique of semantic unambiguity in order to clarify the Term Concepts in the focused contexts. From the case study with 22 contexts, 62 term, and 475 synsets, it was shown that Reinforcement Learning could accurately select the suitable term concepts for the focused contexts, with Precision = 0.7756, Recall = 0.7756 and F-Measure = 0.7735. Therefore, it can be concluded that the Technique of Semantic Unambiguity for a Concept Selection of Terms in Focused Contexts has high accuracy when applying the Reinforcement Learning