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

KRDB-95: Reasoning about Structured Objects: Knowledge Representation Meets Databases, Proceedings of the 2nd Workshop KRDB'95, Bielefeld, Germany, Septtember 11-12, 1995

The content-based representation of information, which tries to represent the meaning of the information in a machine-understandable way, requires representation formalisms with a well-defined formal semantics. This semantics can elegantly be provided by the use of a logic-based formalism. However, in this setting there is a fundamental tradeoff between the expressivity of the representation formalism and the efficiency of reasoning with this formalism. This motivates the “engineering of logics”, i.e., the design of logical formalisms that are tailored to specific representation tasks. The talk will illustrate this approach with the example of so-called Description Logics and their application for databases and as ontology languages for the semantic web.

Engineering of Logics for the Content-Based Representation of Information

Description logics (DLs) are a successful family of logic-based knowledge representation formalisms, which can be used to represent the conceptual knowledge of an application domain in a structured and formally well-understood way. They are employed in various areas, such as natural language processing, configuration, and databases, but their most notable success so far is the adoption of the DL-based language OWL as ontology language for the Semantic Web. The most important selling point for DL systems is that they provide their users with highly optimized reasoning procedures for expressive representation languages, which – despite their high worst-case complexity – behave quite well in practice. Most DL systems employ tableau-based reasoning procedures, but several other promising approaches to reasoning in DLs are currently being investigated. The purpose of this special issue is to provide a snapshot of the current research on reasoning in DLs. Following an open call for papers, I received ten submissions to the special issue, of which five were finally accepted. The accepted papers have undergone a stringent reviewing process for inclusion in this issue, which further enhanced their quality. I thank both the anonymous reviewers and the authors for doing such a good job. The five papers collected in this special issue cover the most important topics of the current research on reasoning in description logics. They demonstrate both the breadth and the depth of the research in this field.

Preface to Special Issue on Reasoning in Description Logics

In previous work, we have investigated privacy-preserving publishing of Description Logic (DL) ontologies in a setting where the knowledge about individuals to be published is an $\mathcal {EL} $ instance store, and both the privacy policy and the possible background knowledge of an attacker are represented by concepts of the DL $\mathcal {EL}$. We have introduced the notions of compliance of a concept with a policy and of safety of a concept for a policy, and have shown how, in the context mentioned above, optimal compliant (safe) generalizations of a given $\mathcal {EL}$ concept can be computed. In the present paper, we consider a modified setting where we assume that the background knowledge of the attacker is given by a DL different from the one in which the knowledge to be published and the safety policies are formulated. In particular, we investigate the situations where the attacker’s knowledge is given by an $\mathcal {FL}_0 $ or an $\mathcal {FLE} $ concept. In both cases, we show how optimal safe generalizations can be computed. Whereas the complexity of this computation is the same (ExpTime) as in our previous results for the case of $\mathcal {FL}_0 $, it turns out to be actually lower (polynomial) for the more expressive DL $\mathcal {FLE} $.

/pdf/mixing-description-logics-in-privacy-preserving-ontology-4hzuy1cl3r.pdf

Mixing Description Logics in Privacy-Preserving Ontology Publishing


 The inexpressive Description Logic (DL) 
 
 
 
${\cal F}{{\cal L}_0}$

 
 , which has conjunction and value restriction as its only concept constructors, had fallen into disrepute when it turned out that reasoning in 
 
 
 
${\cal F}{{\cal L}_0}$

 
 w.r.t. general TBoxes is ExpTime-complete, that is, as hard as in the considerably more expressive logic 
 
 
 
${\cal A}{\cal L}{\cal C}$

 
 . In this paper, we rehabilitate 
 
 
 
${\cal F}{{\cal L}_0}$

 
 by presenting a dedicated subsumption algorithm for 
 
 
 
${\cal F}{{\cal L}_0}$

 
 , which is much simpler than the tableau-based algorithms employed by highly optimized DL reasoners. Our experiments show that the performance of our novel algorithm, as prototypically implemented in our 
 
 
 
${\cal F}{{\cal L}_0}$

 
 wer reasoner, compares very well with that of the highly optimized reasoners. 
 
 
 
${\cal F}{{\cal L}_0}$

 
 wer can also deal with ontologies written in the extension 
 
 
 
${\cal F}{{\cal L}_ \bot }$

 
 of 
 
 
 
${\cal F}{{\cal L}_0}$

 
 with the top and the bottom concept by employing a polynomial-time reduction, shown in this paper, which eliminates top and bottom. We also investigate the complexity of reasoning in DLs related to the Horn-fragments of 
 
 
 
${\cal F}{{\cal L}_0}$

 
 and 
 
 
 
${\cal F}{{\cal L}_ \bot }$

 
 .

Franz Baader

Papers

KRDB-95: Reasoning about Structured Objects: Knowledge Representation Meets Databases, Proceedings of the 2nd Workshop KRDB'95, Bielefeld, Germany, Septtember 11-12, 1995

Engineering of Logics for the Content-Based Representation of Information

Preface to Special Issue on Reasoning in Description Logics

Mixing Description Logics in Privacy-Preserving Ontology Publishing

Efficient TBox Reasoning with Value Restrictions using the wer Reasoner