Introducing Machine Learning (original) (raw)

A review of the state of the art in Machine Learning on the Semantic Web Technical Report

2003

A Review of the State of the Art of Machine Learning on the Semantic Web

2003

Towards Machine Learning on the Semantic Web

Lecture Notes in Computer Science, 2008

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

A Review of Machine Learning Applications in Semantic Web

International Journal of Scientific and Technological Research, 2019

In recent years, the rapid growth of the semantic web has encouraged the use of knowledge graphs to store and exchange data among computers. The change in data storage and retrieval has imposed the need to integrate other techniques that interact with these data. Machine Learning (ML) is the most important fields of study that interacts with data, to extract the patterns and relations that can be used in future applications. However, these techniques are implemented to be used with structured data, where each data instance is characterized using a set of predefined features. Thus, to apply machine learning techniques to knowledge graphs, it is important to convert the formation of the information in these graphs. The knowledge graph represents a collection of interconnected descriptions of entities (objects, events, situations, or abstract concepts in the real world). This work reviews the knowledge graphs and how they are structured and stored. Then, the techniques being used to em...

Machine Learning: A very quick introduction

2013

Machine learning [1] is concerned with algorithmically finding patterns and relationships in data, and using these to perform tasks such as classification and prediction in various domains. We now introduce some relevant terminology and provide an overview of a few sorts of machine learning approaches.

A review of the state of the art in Machine Learning on the Semantic Web: Technical Report CSTR-05-003

2004

This paper reviews the current state of the art of machine learning applied to the Semantic Web. It looks at the Semantic Web and its languages, including RDF and OWL, from a machine learning perspective. Trends in the Semantic Web are mentioned throughout and the relationship with Web Services is examined. Applications are discussed with recent examples and pointers to data sets. Finally, the emerging field of Semantic Web Mining is introduced. 1 Introduction This paper reviews the current state of the art of machine learning applied to the Semantic Web. The intended readership is researchers and practitioners in the machine learning and computational intelligence community. No substantial prior knowledge of the Semantic Web is assumed as the paper includes a brief tutorial introduction to the Semantic Web, given from a machine learning perspective. In terms of focus, the review exhibits a distinct bias towards my own personal interest in logical approaches to machine learning and, in particular, learning from structured data. However, I believe that this bias is appropriate given the structured nature of the Semantic Web and its own inbuilt logics bias. The rest of this paper is divided into three parts: an introduction to the Semantic Web with occasional comments relating to machine learning; a review of existing machine learning applications with discussion of potential applications; and finally, some concluding remarks. Although the design for the Semantic Web is based on RDF and URIs, there are also a series of other language layers that sit on top of this foundation layer. A brief introduction to each of these is given below along with observations from the perspective of machine learning. 2.1 Uniform Resource Identifier (URI) The Uniform Resource Identifier (URI) addressing scheme is well known as the means of locating documents on the Web. Typical URIs are short strings that start with scheme names like "http:", "mailto:" or "ftp:". In their traditional usage, each URI refers to a resource, or a specific point within a resource. Most users would expect this resource to be located somewhere on the Web. In fact, the URI specification [2] does not require this and most Web users would be surprised to discover that URIs support references to entities that are not even network retrievable. The Semantic Web makes good use of this global referencing feature of URIs to allow statements to be made about anything that has an identity. So, as well as continuing to support references to Web resources, such as HTML pages and other online documents, URIs also permit references to entities such as human beings, corporations, bound books in a library or more ethereal entities such as concepts and relations. URI's observe a syntax that is familiar to any Web user and, while that syntax is governed by [2], the ownership and creation is delegated: anyone can create a URI. To allow decentralised growth of the Web, there is no central repository or clearing house for URIs. Consequently, multiple URIs can refer to a single entity. This clearly poses some interesting problems in testing for equality (or equivalence) between URIs. 2.2 Resource Description Framework (RDF) Resource Description Framework (RDF) [3][4] is a language that utilises triples of URIs. An RDF statement is a {subject, predicate, object} triple of URIs. As a graph model, this corresponds to a directed graph with subject and object as labelled nodes connected by the labelled directed arc predicate. Literal values may be used in an RDF triple in place of the object; any literal values are treated by an RDF parser as an anonymous URI. An example triple for the statement, "http://www.example.org/index.html has a creator whose value is the literal John Smith" could be represented as the following informal plain text triple.

A review of the state of the art in Machine Learning on the Semantic Web

Machine Learning & Associated Algorithms -A Review

Journal of Advances in Mathematical & Computational Science. Vol 10, No.3. Pp 1 – 14., 2022

Machine learning and associated algorithms occupies a pride of place in the execution of automation in the field of computing and its application to addressing contemporary and human-centred problems such as predictions, evaluations, deductions, analytics and analysis. This paper presents types of data and machine learning algorithms in a broader sense. We briefly discuss and explain different machine learning algorithms and real-world application areas based on machine learning. We highlight several research issues and potential future directions

Introduction to Machine Learning and Its Applications: A Survey

2020

Machine learning is the fastest growing areas of computer science. It has the ability to lets the computer to create the program. It is a subset of Artificial Intelligence (AI), and consists of the more advanced techniques and models that enable computers to figure things out from the data and deliver. It is a field of learning and broadly divided into supervised learning, unsupervised learning, and reinforcement learning. There are many fields where the Machine learning algorithms are used. The objective of the paper is to represent the ML objectives, explore the various ML techniques and algorithms with its applications in the various fields from published papers, workshop materials & material collected from books and material available online on the World Wide Web.

MACHINE LEARNING: A SURVEY

Machine learning [1], a branch of artificial intelligence, that gives computers the ability to learn without being explicitly programmed, means it gives system the ability to learn from data. There are two types of learning techniques: supervised learning and unsupervised learning [2]. This paper summarizes the recent trends of machine learning research.

Introducing Machine Learning (original) (raw)

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