Semantic similarity in the area of natural language processing, aka semantic proximity (and some people also use the term semantic relatedness) is an assessment that can be estimated by defining a topological similarity, using, for example, dictionaries to define the distance between terms or concepts belonging to those dictionaries. For example, a naïve metric for the comparison of concepts ordered in a partially ordered set and represented as nodes of a directed acyclic graph (for example, a taxonomy), would be the shorter path that joins the two concept nodes. Currently, there are many methods to estimate this similarity. The problem emerges when there is no dictionary to calculate the number of nodes that one term is from each...
Today there are many lexical tools for establishing linguistic relationships between words, and therefore calculating the degree of semantic similarity between these words in automatic way. Most of these tools can be considered as a kind of dictionary or even ontology, both general and domain-specific. Among the most popular electronic dictionaries are the general-purpose meta-thesaurus WordNet and the UMLS lexical toolkit in the medical domain.
Legal question answering is the discipline that tries to design software solutions being able to correctly answers form the legal domain by means of computers. It is currently one of the hottest topic for research both in academia and industry. The reason is that there are a huge corpora of national and international laws that make the task of lawyers very difficult. However, the new solutions that are being proposed are able to reply difficult questions in a correct way in just a few seconds. Therefore, it is asumed the legal question answering will change the legal industry in a near future.
Nowadays, technology advancements such as deep learning are extremely productive in the study, creation, and production of applications for artificial intelligence. Many artificial intelligence sectors, such as the natural language processing and the information retrieval fields, have greatly benefited from these latest developments.
At present, the labour market has become somewhat too complex. On the one hand we have employers who are always looking for workers who can meet the business needs of their companies and on the other hand we have the workers, who offer their skills and competences in the hope of finding an employer who will offer them a job.
Semantic similarity measures can help computers to distinguish one object form another, group them based on the similarity, classify a new object into the group, or even predict the behavior of the new object. During recent years several attempts have been made in using different semantic similarity measures for information retrieval purposes. And the truth is that there are a substantial number of distance/similarity measures encountered in many different fields such as anthropology, biology, chemistry, computer science, ecology, information theory, geology, mathematics, physics, psychology, statistics, etc.
Semantic similarity measures are very important in many computer-related fields. Previous works in applications such as data integration, query expansion, tag refactoring, automatic question answering or text clustering have used some semantic similarity measures to implement a solution. Despite the usefulness of semantic similarity measures in these applications, the problem of measuring the similarity between two text expressions remains a key challenge. And the community strives every day to find satisfactory solutions that can solve the problem once and for all.
Ontology matching (also known as ontology alignment or ontology mapping) is a research field for finding semantic correspondences between ontologies belonging to the same domain but that have been developed separately. There are several problems with current methods and systems for matching ontologies. For example, the existing interfaces are not very attractive. In addition, the current matching systems do not allow us to discover complex correspondences. Last, but not least, most of the existing similarity measures cannot be understood easily. Our solution for ontology alignment helps greatly to solve the problems mentioned problems by allowing a more pleasant interface or the customization of matching through the use of simple...
In recent times, the smart village concept has managed to attract the attention of both the general public and the authorities. The truth is that the smart village concept includes the use of the latest technological advances to achieve a greater degree of digitalization of rural areas. If you consider that the analogous concept of Smart City is currently very developed, very little work has been done even with the concept of Smart Village.
Semantic Similarity is one of the hottest topics and most interesting research challenges that researchers in the A.I. community have to deal with. The truth is that positive results in this context can have a great impact on a wide range of disciplines, both academic and business. This is because a multitude of computational methods require the calculation, even if basic, of the degree of similarity between pieces of textual information.