The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advance
s made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.
This paper presents a reference study of available algorithms for plagiarism
detection and it develops semantic plagiarism detection algorithm for plagiarism detection
in medical research papers by employing the Medical Ontologies available on the
World
Wide Web.
The issue of plagiarism detection in medical research written in natural languages is
a complex issue and related exact domain of medical research.
There are many used algorithms for plagiarism detection in natural language, which
are generally divided into two main categories, the first one is comparison algorithms
between files by using fingerprints of files, and files content comparison algorithms, which
include strings matching algorithms and text and tree matching algorithms.
Recently a lot of research in the field of semantic plagiarism detection algorithms
and semantic plagiarism detection algorithms were developed basing of citation analysis
models in scientific research.
In this research a system for plagiarism detection was developed using “Bing” search
engine, where tow type of ontologies used in this system, public ontology as wordNet and
many standard international ontologies in medical domain as Diseases ontology which
contains a descriptions about diseases and definitions of it and the derivation between
diseases.
We aimed to distinguish between them and the other research areas such as information retrieval and data mining. we tried to determine the general structure of such systems which form a part of larger systems that have a mission to answer user querie
s based on the extracted information. we reviewed the different types of these systems, used techniques with them and tried to define the current and future challenges and the consequent research problems.
Finally we tried to discuss the details of the various
implementations of these systems by explaining two platforms Gate and OpenCalais and comparing between their information
extraction systems and discuss the results.
