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.

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Data integration: a theoretical perspective

Formal Concept Analysis

Publisher Summary Modal logic is an intrinsically intensional logic, suitable only for applications such as reasoning about necessity, possibility, and knowledge. Description logics are undeniably modal logics but are extremely well suited for reasoning about ordinary individuals and the relations between them. The basic components of description logics are concept languages, terminological formalisms, and assertional formalisms. The concept language ALC is only one member of a large family of concept languages. The chapter introduces syntax and semantics of the basic description logics (DL) ALC, and shows its relationship to multi-modal K. It then discusses additional DL constructors and describes their modal logics (ML) counterparts. In addition to these constructors, DLs provide their users with a terminological formalism, which allows introducing names for complex concepts, and an assertional formalism, which allows stating facts about specific individuals/objects. The known complexity results for DLs and structural subsumption algorithms for some of them are described. The bi-simulation characterizations of the corresponding ML fragments are also considered. Some non-standard inferences in DLs and means of expressiveness that do not have immediate ML counterparts are introduced.

13 Description logic

After a critical review of the present architecture of SNOMED CT, addressing both logical and ontological issues, we present a roadmap towards an overall improvement of this terminology In particular, we recommend the following actions: Upper level categories should be rearranged according to a standard upper level ontology Meta-class like concepts should be identified and removed from the taxonomy SNOMED concepts denoting (non instantiable) individual entities (eg geographical regions) should be kept separate from those concepts that denote (instantiable) types SNOMED binary relations should be reduced to a set of canonical ones, following existing recommendations Taxonomies should be cleansed and split into disjoint partitions The number of full definitions should be increased Finally, we propose a new approach to modeling part-whole hierarchies, as well as the integration of qualifier relations into the description logic framework

/pdf/snomed-ct-s-problem-list-ontologists-and-logicians-therapy-3a5gqgp0zy.pdf

SNOMED CT's problem list: ontologists' and logicians' therapy suggestions.

Formal Concept Analysis (FCA) can be used to analyze data given in the form of a formal context In particular, FCA provides efficient algorithms for computing a minimal basis of the implications holding in the context In this paper, we extend classical FCA by considering data that are represented by relational structures rather than formal contexts, and by replacing atomic attributes by complex formulae defined in some logic After generalizing some of the FCA theory to this more general form of contexts, we instantiate the general framework with attributes defined in the Description Logic (DL) EL, and with relational structures over a signature of unary and binary predicates, ie, models for EL In this setting, an implication corresponds to a so-called general concept inclusion axiom (GCI) in EL The main technical result of this paper is that, in EL, for any finite model there is a finite set of implications (GCIs) holding in this model from which all implications (GCIs) holding in the model follow

/pdf/a-finite-basis-for-the-set-of-el-implications-holding-in-a-oddcdkel5x.pdf

A finite basis for the set of ƐL-implications holding in a finite model

Unification of concept descriptions was introduced by Baader and Narendran as a tool for detecting redundancies in knowledge bases It was shown that unification in the small description logic FL0, which allows for conjunction, value restriction, and the top concept only, is already ExpTime-complete The present paper shows that the complexity does not increase if one additionally allows for composition, union, and transitive closure of roles It also shows that matching (which is polynomial in FL0) is PSpace-complete in the extended description logic These results are proved via a reduction to linear equations over regular languages, which are then solved using automata The obtained results are also of interest in formal language theory

Unification in a Description Logic with Transitive Closure of Roles.

Terminological knowledge representation formalisms can be used to represent objective, time-independent facts about an application domain. Notions like belief, intentions, time - which are essential for the representation of multi-agent environments - can only be expressed in a very limited way. For such notions, modal logics with possible worlds semantics provides a formally well-founded and well-investigated basis. This paper presents a framework for integrating modal operators into terminological knowledge representation languages. These operators can be used both inside of concept expressions and in front of terminological and assertional axioms. The main restrictions are that all modal operators are interpreted in the basic logic K, and that we consider increasing domains instead of constant domains. We introduce syntax and semantics of the extended language, and show that satisfiability of finite sets of formulas is decidable.

/pdf/terminological-logics-with-modal-operators-1k3ghnqe44.pdf

Franz Baader

Papers

13 Description logic

SNOMED CT's problem list: ontologists' and logicians' therapy suggestions.

A finite basis for the set of ƐL-implications holding in a finite model

Unification in a Description Logic with Transitive Closure of Roles.

Terminological logics with modal operators