Semantics
New semantic information management schemes enable companies to make better use of their information. What exactly is semantics? And how can semantics technology help your development efforts? Juhnyoung Lee, a researcher at the IBM T. J. Watson Research Center, reviews the basics in order to get you started. More >
Related technologies
Semantics technologies:
Anatomy Lens: A search engine that helps scientists hone in on PubMed articles most relevant to their research.
Text Analytics Tools and Runtime for IBM LanguageWare: Use an Eclipse-based development environment and run-time libraries to build custom text analyzers into IBM LanguageWare resources and associated UIMA annotators.
Text Analysis Perspective for DB2 Warehouse: A set of Eclipse plug-ins that allows you to configure and test text analysis engines and use them in warehouse and mining flows created by DB2 Warehouse Edition 9.5.
IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks: A unified API that helps in creating solutions for social computing, semantic search, and activity computing.
IBM Web Ontology Manager: A Web-based system for managing Web Ontology Language (OWL) ontologies.
Unstructured Information Management Architecture SDK: A Java SDK that supports the implementation, composition, and deployment of applications working with unstructured information.
IBM Integrated Ontology Development Toolkit: A toolkit designed for storage, manipulation, query, and inference of ontologies and corresponding instances.
Emerging Technologies Toolkit: The ETTK is a software development kit for designing, developing, and executing emerging autonomic and Web service technologies.
ETTK for Web Services: A software development kit for designing, developing, and executing emerging technologies and Web services. (This is an ETTK technology.)
Learn about semantics
Ontology management:
Towards Enterprise-Scale Ontology Management: Increasingly, semantic mark-up languages are being used to externalize meta-data that can be used for purposes varying from search and retrieval to composition and life cycle management. This use could significantly reduce the costs of deploying, integrating, and maintaining enterprise scale systems.
Leveraging Relational Database Systems for Large-Scale Ontology Management: Current ontology management systems usually rely on in-memory representation and manipulation of ontological data. This article explores database-centered architecture for storing and manipulating ontologies.
The role of ontologies in autonomic computing systems: The goal of IBM's autonomic computing strategy is to deliver information technology environments with improved self-management capabilities. Data correlation and inference technologies can be used as core components in building autonomic computing systems.
Unstructured Information Management: Unstructured information represents the vast majority of the data collected and accessible to enterprises. Exploiting this information requires systems for managing and extracting knowledge from large collections of unstructured data and applications for discovering patterns and relationships.
Workflow composition:
Executing Abstract Web Process Flows: This paper presents a prototype workflow engine that accepts abstract BPEL4WS flows augmented with semantic annotations in DAML-S and performs run-time discovery, composition, binding, and execution of Web services.
On Accommodating Inter-Service Dependencies in Web Process Flow Composition: This paper presents a system for dynamic binding of Web services for abstract specifications of business integration flows using a constraint-based semantic discovery mechanism.
Dynamic Workflow Composition using Markov Decision Processes:The advent of Web services has made automated workflow composition possible. This paper proposes the use of Markov decision processes (MDPs), an efficient stochastic optimization framework, to model workflow composition.
Semantic Web:
SOA, Glial, and the Autonomic Semantic Web Machine - Tools for Handling Complexity: This paper investigates the use of semantic Web technologies as a means of reducing ambiguity in the design and implementation of automatic solutions for addressing complexity at a number of points in the software life cycle.
A Method for Semantically Enhancing the Service Discovery Capabilities of UDDI: This paper expands on previous work done on combining the semantic Web with UDDI by presenting a method of improving the effectiveness of service discovery in UDDI , an industry-initiated Web service directory.
External Matching in UDDI: As the set of available Web services expands, it becomes increasingly important to have automated tools that help identify services matching a requester's requirements. This paper presents a flexible mechanism for enhancing UDD's search function.
Glossary extraction:
Automatic Glossary Extraction: Beyond Terminology Identification: This paper describes a method for automatically extracting domain-specific glossaries from large document collections – more successfully supporting applications requiring knowledge of domain concepts.
GlossOnt: A Concept-Focused Ontology Building Tool: The demand for ontologies is rapidly growing, especially due to developments in knowledge management, e-commerce, and the semantic Web. This paper presents a concept-focused ontology-building method based on text-mining technology.
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