The Recent Pace of Ontology Development Appears to Have Waned
Some of the leading methodologies, presented in rough order from the oldest to newest, are as follows:
Cyc – this oldest of knowledge bases and ontologies has been mapped to many separate ontologies. See the separate document on the Cyc mapping methodology for an overview of this approach [9]
TOVE (Toronto Virtual Enterprise) – a first-order logic approach to representing activities, states, time, resources, and cost in an enterprise integration architecture [10]
IDEF5 (Integrated Definition for Ontology Description Capture Method) – is part of a broader set of methodologies developed by Knowledge Based Systems, Inc. [11]
ONIONS (ONtologic Integration Of Naive Sources) – a set of methods especially geared to integrating multiple information sources [12], with a particular emphasis on domain ontologies
COINS (COntext INterchange System) – a long-running series of efforts from MIT’s Sloan School of Management [13]
METHONTOLOGY – one of the better known ontology building methodologies; however, not many known uses [14]
OTK (On-To-Knowledge) was a methodology that came from the major EU effort at the beginning of last decade; it is a common sense approach reflected in many ways in other methodologies [15]
UPON (United Process for ONtologies) – is a UML-based approach that is based on use cases, and is incremental and iterative [16].
http://www.mkbergman.com/906/a-brief-survey-of-ontology-development-methodologies/
2017年4月24日 星期一
2017年4月23日 星期日
Some Ongoing KBS/Ontology Projects and Groups
useful Ontology and KR collections
http://www.cs.utexas.edu/users/mfkb/related.html
http://www.cs.utexas.edu/users/mfkb/related.html
- Ontology Building Tools - For building and managing ontologies. See Michael Denny's survey of ontology editors (2002), and also the following specific tools:
- Ontology Learning Tools - Automated/assisted techniques for building an ontology. Also see a good survey of ontology learning methods and techniques (OntoWeb deliverable 1.5, A. Gomez-Perez, D. Manzano-Macho).
- The New Ontology of the Mental Causation Debate - an AHRC (Arts & Humanities Research Council) funded research project, attempting to frame the debate with more metaphysical precision, and explore the consequences of that reframing (Univ Durham, UK).
- Ontology Merging Tools - See Chimera and PROMPT. Also see Carter, a tool for helping experts build a consensus KB.
- Ontologies - Dealing with multiple ontologies - See InfoQuilt.
Web Data Semantics and Integration
7 Ontologies, RDF, and OWL
7.1 Introduction
7.2 Ontologies by example
7.3 RDF, RDFS, and OWL
7.4 Ontologies and (Description) Logics
7.5 Further reading
7.6 Exercises
8 Querying Data through Ontologies
8.1 Introduction
8.2 Querying RDF data: notation and semantics
8.3 Querying through RDFS ontologies
8.4 Answering queries through DL-LITE ontologies
8.5 Further reading
8.6 Exercises
9 Data Integration
9.1 Introduction
9.2 Containment of conjunctive queries
9.3 Global-as-view mediation
9.4 Local-as-view mediation
9.5 Ontology-based mediators
9.6 Peer-to-Peer Data Management Systems
9.7 Further reading
9.8 Exercices
10 Putting into Practice: Wrappers and Data Extraction with XSLT
10.1 Extracting Data from Web Pages
10.2 Restructuring Data
11 Putting into Practice: Ontologies in Practice (by Fabian M. Suchanek)
11.1 Exploring and installing YAGO
11.2 Querying YAGO
11.3 Web access to ontologies
12 Putting into Practice: Mashups with YAHOO! PIPES and XProc
12.1 YAHOO! PIPES: A Graphical Mashup Editor
12.2 XProc: An XML Pipeline Language
7.1 Introduction
7.2 Ontologies by example
7.3 RDF, RDFS, and OWL
7.4 Ontologies and (Description) Logics
7.5 Further reading
7.6 Exercises
8 Querying Data through Ontologies
8.1 Introduction
8.2 Querying RDF data: notation and semantics
8.3 Querying through RDFS ontologies
8.4 Answering queries through DL-LITE ontologies
8.5 Further reading
8.6 Exercises
9 Data Integration
9.1 Introduction
9.2 Containment of conjunctive queries
9.3 Global-as-view mediation
9.4 Local-as-view mediation
9.5 Ontology-based mediators
9.6 Peer-to-Peer Data Management Systems
9.7 Further reading
9.8 Exercices
10 Putting into Practice: Wrappers and Data Extraction with XSLT
10.1 Extracting Data from Web Pages
10.2 Restructuring Data
11 Putting into Practice: Ontologies in Practice (by Fabian M. Suchanek)
11.1 Exploring and installing YAGO
11.2 Querying YAGO
11.3 Web access to ontologies
12 Putting into Practice: Mashups with YAHOO! PIPES and XProc
12.1 YAHOO! PIPES: A Graphical Mashup Editor
12.2 XProc: An XML Pipeline Language
Agile Knowledge Engineering and Semantic Web (AKSW)
一個有關semantic web和knowledge engineering的研究群,http://aksw.org/About.html
hosted by the Chair of Business Information Systems (BIS) of the Institute of Computer Science (IfI) / University of Leipzig as well as the Institute for Applied Informatics (InfAI).
Goals
AKSW is committed to the free software, open source, open access and open knowledge movements.
Groups
The following subgroups belong to AKSW
hosted by the Chair of Business Information Systems (BIS) of the Institute of Computer Science (IfI) / University of Leipzig as well as the Institute for Applied Informatics (InfAI).
Goals
- Development of methods, tools and applications for adaptive Knowledge Engineering in the context of the Semantic Web
- Research of underlying Semantic Web technologies and development of fundamental Semantic Web tools and applications
- Maturation of strategies for fruitfully combining the Social Web paradigms with semantic knowledge representation techniques
AKSW is committed to the free software, open source, open access and open knowledge movements.
Groups
The following subgroups belong to AKSW
- Adaptive Information and Knowledge Engineering
- Agile collaborative requirements engineering
- Creation and evolution of knowledge bases from legacy databases
- Software product-line engineering
- Vocabulary alignment
- Emergent Semantics
- Agile Knowledge Engineering
- Distributed / Federated Social Networks
- Linked Data
- Semantic Software Engineering
- Semantic Web Infrastructure
- Knowledge Integration and Linked Data Technologies
- Data Engineering
- Data Integration
- Data-driven Artificial Intelligence
- DBpedia
- Knowledge Engineering
- Language Technology
- Machine Learning and Ontology Engineering
- Creating knowledge bases from weakly structured data
- Quality assurance and enhancement in ontologies
- Semi-automatic instance matching
- Supervised Machine Learning in OWL/RDF knowledge bases
- Semantic Abstraction
- Knowledge Access, e.g., keyword-based search, question answering, and interfaces
- Knowledge Extraction, e.g., extraction of RDF and OWL from unstructured data
- Knowledge Integration, e.g., link discovery and linked data fusion
- Knowledge Storage, e.g., federated queries, triple stores
- Knowledge-Driven applications, e.g., industry 4.0, big data, benchmark
2017年4月17日 星期一
Creating a virtual service desk agent – a DevOps experiment
https://blogs.dxc.technology/2016/05/23/creating-a-virtual-service-desk-agent-a-devops-experiment/
Slightly less rough idea
a final overview of the solution
Slightly less rough idea
a final overview of the solution
訂閱:
文章 (Atom)
Types of Bots: An Overview
Learn more about all the different varieties of bots, and what they can do for you http://botnerds.com/types-of-bots/ In this articl...
-
Knowledge-based Artificial Intelligence http://www.mkbergman.com/1816/knowledge-based-artificial-intelligence/ A recent interview with a n...
-
Just like any technical or business IT capability, one pre-requisite for adoption is understanding the WHAT and the WHY; and a clear definit...
-
http://resources.narrativescience.com/h/i/124944227-what-is-natural-language-generation