Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

/pdf/yago-a-core-of-semantic-knowledge-13k3nmj6l2.pdf

Yago: a core of semantic knowledge

https://www.cs.ox.ac.uk/people/bernardo.cuencagrau/publications/PelletDemo.pdf

Pellet: A practical OWL-DL reasoner

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

/pdf/data-integration-a-theoretical-perspective-58aesjvhuk.pdf

Data integration: a theoretical perspective

Formal Concept Analysis

An extension of the concept description language ACC used in KL-ONE-like terminological reasoning is presented. The extension includes multi-modal operators that can either stand for the usual role quantifications or for modalities such as belief, time etc. The modal operators can be used at all levels of the concept terms, and they can be used to modify both concepts and roles. This is an instance of a new kind of combination of modal logics where the modal operators of one logic may operate directly on the operators of the other logic.

/pdf/a-multi-dimensional-terminological-knowledge-representation-4oijtis1ba.pdf

A multi-dimensional terminological knowledge representation language

The Description Logic $\mathcal{EL}$ has recently drawn considerable attention since, on the one hand, important inference problems such as the subsumption problem are polynomial. On the other hand, $\mathcal{EL}$ is used to define large biomedical ontologies. Unification in Description Logics has been proposed as a novel inference service that can, for example, be used to detect redundancies in ontologies. The main result of this paper is that unification in $\mathcal{EL}$ is decidable. More precisely, $\mathcal{EL}$-unification is NP-complete, and thus has the same complexity as $\mathcal{EL}$-matching. We also show that, w.r.t. the unification type, $\mathcal{EL}$ is less well-behaved: it is of type zero, which in particular implies that there are unification problems that have no finite complete set of unifiers.

Unification in the Description Logic $\mathcal{EL}$

Given a finite set $\mathcal{C} := \{ C_1, \ldots, C_n\}$ of description logic concepts, we are interested in computing the subsumption hierarchy of all least common subsumers of subsets of $\mathcal{C}$. This hierarchy can be used to support the bottom-up construction and the structuring of description logic knowledge bases. The point is to compute this hierarchy without having to compute the least common subsumer for all subsets of $\mathcal{C}$. We will show that methods from formal concept analysis developed for computing concept lattices can be employed for this purpose.

Building and Structuring Description Logic Knowledge Bases Using Least Common Subsumers and Concept Analysis

The purpose of this appendix is to introduce (in a compact manner) the syntax and semantics of the most prominent DLs occurring in this handbook. More information and explanations as well as some less familiar DLs can be found in the respective chapters. For DL constructors whose semantics cannot be described in a compact manner, we will only introduce the syntax and refer the reader to the respective chapter for the semantics. Following Chapter 2 on Basic Description Logics, we will first introduce the basic DL AL, and then describe several of its extensions. Thereby, we will also fix the notation employed in this handbook. Finally, we will comment on the naming schemes for DLs that are employed in the literature and in this handbook. Before starting with the definitions, let us introduce some notational conventions. The letters A, B will often be used for atomic concepts, and C, D for concept descriptions. For roles, we often use the letters R, S, and for functional roles (features, attributes) the letters f, g. Nonnegative integers (in number restrictions) are often denoted by n, m, and individuals by a, b. In all cases, we may also use subscripts. This convention is followed when defining syntax and semantics and in abstract examples. In concrete examples, the following conventions are used: concept names start with an uppercase letter followed by lowercase letters (e.g., Human, Male), role names (also functional ones) start with a lowercase letter (e.g., hasChild, marriedTo), and individual names are all uppercase (e.g., CHARLES, MARY).

Description Logic Terminology

https://lat.inf.tu-dresden.de/research/papers/2012/BaKP-JWS12.pdf

Franz Baader

Papers

A multi-dimensional terminological knowledge representation language

Unification in the Description Logic $\mathcal{EL}$

Building and Structuring Description Logic Knowledge Bases Using Least Common Subsumers and Concept Analysis

Description Logic Terminology

Context-dependent views to axioms and consequences of Semantic Web ontologies