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

Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

This paper describes the organization of extracted risk-relevant data in a relational database created at Regulatory Data Corporation for use by security professionals. The initial effort involved creating sets of data-extraction variables around a set of risk relevant keywords. The keywords clustered around events rather than entities and the data extraction variables that were developed centered on semantic roles of event participants. To facilitate future data extraction efforts in this genre, we organized events, participants, keywords and grammatical forms into an ontology. This ontology of risk-relevance (henceforth known as the ORR) is a tool for both data extraction professionals and risk-assessment professionals that allows new entries to be added easily when the need for additional information arises.

Formal Ontology in Information Systems

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.

Ontology learning from text: A look back and into the future

Geospatial Cyberinfrastructure: Past, present and future

Discovering and accessing suitable geographic information (GI) in the open and distributed environments of current Spatial Data Infrastructures (SDIs) is a crucial task. Catalogues provide searchable repositories of information descriptions, but the mechanisms to support GI retrieval are still insufficient. Problems of semantic heterogeneity caused by the ambiguity of natural language can arise during keyword‐based search in catalogues and when formulating a query to access the discovered data. In this paper, we present an approach to ontology‐based GI retrieval that contributes to solving existing problems of semantic heterogeneity and hides most of the complexity of the required procedure from the requester. A query language and graphical user interface allow a requester to intuitively formulate a query using a well‐known domain vocabulary. From this query, an ontology concept is derived, which is then used to search a catalogue for a data source that provides all the information required to answer the ...

Ontology‐based retrieval of geographic information

Ontology-based discovery of geographic information services—An application in disaster management

SUMMARY Finding and accessing suitable information in the open and distributed environments of current Spatial Data Infrastructures (SDIs) is a crucial task. Catalogues provide searchable repositories of information descriptions, but the mechanisms to support the tasks of discovery and retrieval are still insufficient. Problems of semantic heterogeneity caused by synonyms and homonyms can arise during free-text search in catalogues. Moreover, once a suitable Web Feature Service (WFS) is found and accessed, the property names of a feature are often difficult to interpret. This paper introduces an architecture for ontologybased discovery and retrieval of geographic information that solves semantic heterogeneity problems of current query capabilities. Based on a (real-world) scenario from the area of flood management, the application of our approach shows that the information requestor can be efficiently supported.

https://agile-online.org/Conference_Paper/CDs/agile_2004/papers/3-1-3_Klien.pdf

An Architecture for Ontology-Based Discovery and Retrieval of Geographic Information

The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule-based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.

A Rule-Based Strategy for the Semantic Annotation of Geodata

How can the usability of distributed and heterogeneous geographic data sets be enhanced? Semantic interoperability is a prerequisite for effectively finding and accessing relevant data in different application contexts. By using geospatial domain ontologies and semantic annotations of geodata based on these ontologies semantic interoperability can be achieved. However, since no automated methods for the semantic annotation of geodata exist this remains a laborious task, which data providers are neither willing nor capable to perform. In this paper we propose a method for automating the annotation process based on spatial relations. At the domain level, spatial relations play an important role for defining and identifying geospatial concepts. At the data level, spatial relations may be expressed through spatial processing methods, as we can calculate relations like topology, direction or distance between two spatial entities. We show how this potential can be exploited for automating the semantic annotation of geodata. The approach is illustrated by introducing a case study for annotating data containing representations of floodplains.

Eva Klien

Papers

Ontology‐based retrieval of geographic information

Ontology-based discovery of geographic information services—An application in disaster management

An Architecture for Ontology-Based Discovery and Retrieval of Geographic Information

A Rule-Based Strategy for the Semantic Annotation of Geodata

The role of spatial relations in automating the semantic annotation of geodata