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

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.

/pdf/modeling-relational-data-with-graph-convolutional-networks-81d86mzfgx.pdf

Modeling Relational Data with Graph Convolutional Networks

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

While it is easy to find statistics on almost every topic, coming up with an explanation about those statistics is a much more difficult task. This demo showcases the prototype tool Explain-a-LOD, which uses background knowledge from DBpedia for generating possible explanations for a statistic.

Nobody Wants to Live in a Cold City Where No Music Has Been Recorded - Analyzing Statistics with Explain-a-LOD.

When processing information from unstructured sources, numbers have to be parsed in many cases to do useful reasoning on that information. However, since numbers can be expressed in different ways, a robust number parser that can cope with number representations in different shapes is required in those cases. In this paper, we show how to train such a parser based on Conditional Random Fields. As training data, we use pairs of Wikipedia infobox entries and numbers from public knowledge graphs. We show that it is possible to parse numbers at an accuracy of more than 90%.

/pdf/a-robust-number-parser-based-on-conditional-random-fields-42kl5rfrli.pdf

A Robust Number Parser Based on Conditional Random Fields

. Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks. Most approaches are evaluated on a single task or a single group of tasks to determine their overall performance. The evaluation is then assessed in terms of how well the embedding approach performs on the task at hand. Still, it is hardly evaluated (and often not even deeply under-stood) what information the embedding approaches are actually learning to represent. Toﬁllthis gap, we present the DLCC (Description Logic Class Constructors) benchmark, a resource to analyze embedding approaches in terms of which kinds of classes they can represent. Two gold standards are presented, one based on the real-world knowledge graph DBpedia and one synthetic gold standard. In addition, an evaluation framework is provided that implements an experiment protocol so that researchers can directly use the gold standard. To demonstrate the use of DLCC, we compare multiple embedding approaches using the gold standards. We ﬁnd that many DL constructors on DBpedia are actually learned by recognizing diﬀerent correlated patterns rather than those deﬁned in the gold standard; we further ﬁnd that speciﬁc DL constructors, such as cardinality constraints, are particularly hard to be learned for most embedding approaches.

The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings

The WebIsALOD dataset provides a linked data endpoint to
the WebIsA database, which harvests millions of subsumption relations
from a large scale Web crawl using text patterns. For each of the relations,
the dataset also contains rich provenance data, such as the text pattern
used, the original sentence in which the pattern was found, and the source
on the Web. In this paper, we describe several alternatives and design
decisions for providing statement-level provenance information at large
scale for the WebIsALOD dataset. Furthermore, we show the practical
impact of that provenance information for computing confidence scores
approximating the correctness of each subsumption relation.

/pdf/provisioning-and-usage-of-provenance-data-in-the-webisalod-4zv4cxyt0v.pdf

Provisioning and Usage of Provenance Data in the WebIsALOD Knowledge Graph.

Roberto Garcia a, Heiko Paulheim b and Paola Di Maio c a Computer Science and Engineering Department, Universitat de Lleida, C / Jaume II, 69, 25001 Lleida, Spain E-mail: roberto.garcia@udl.cat b Data and Web Science Group, University of Mannheim, B6 26, 68159 Mannheim, Germany E-mail: heiko@informatik.uni-mannheim.de c Universal Interfaces Research Lab, ISTCS, 1 Summerhall, Edinburgh EH9 1PL, UK E-mail: paola.dimaio@gmail.com

http://www.semantic-web-journal.net/system/files/swj1033.pdf

Heiko Paulheim

Papers

Nobody Wants to Live in a Cold City Where No Music Has Been Recorded - Analyzing Statistics with Explain-a-LOD.

A Robust Number Parser Based on Conditional Random Fields

The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings

Provisioning and Usage of Provenance Data in the WebIsALOD Knowledge Graph.

Special Issue on Semantic Web Interfaces