A unified approach for artificial intelligence and information retrieval

The study of Concept Similarity is a very important aspect of Knowledge Representation and Information Retrieval in Artificial Intelligence, and it is also a bottleneck that hasn’t been well solved in the Ontology Research. In this article, we take every influencing factor into account, especially the area density, a new method of concept similarity based-on Domain Ontology is suggested. The experiment results show that: the new method we proposed in this article can more reasonably describe the concept similarity.

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Bridging the Mapping between Ontology and Relation Schemas Based on ORMapping Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1523 ◽

2011 ◽

Vol 58-60 ◽

pp. 1523-1528

Author(s):

Hai Zhong Qian ◽

Su Bin Shen

Keyword(s):

Artificial Intelligence ◽

Information Retrieval ◽

Semantic Web ◽

Web Service ◽

Relational Database ◽

Knowledge Engineering ◽

Object Relation ◽

Learning Method ◽

Ontology Learning ◽

Class Hierarchies

Ontology plays a key role in such areas: knowledge engineering, artificial intelligence, information retrieval, semantic web and web service. It is important to recover knowledge associated with specific domains in relational database to semantics, especially, in Ontology learning field. Previous works showed that ontologies can learn from relational database. However, the presented approaches still have some limits. In this paper, we present an ontology learning method based on Object Relation Mapping (ORM) that presents how the source of the databases can be exploited to ontology and the details of object can be generated, such as class hierarchies, relationship and properties.

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A unified approach to artificial intelligence, pattern recognition, image processing and computer vision in fifth-generation computer systems

Information Sciences ◽

10.1016/0020-0255(88)90013-8 ◽

1988 ◽

Vol 45 (3) ◽

pp. 391-431 ◽

Cited By ~ 5

Author(s):

D. Dutta Majumder

Keyword(s):

Artificial Intelligence ◽

Image Processing ◽

Computer Vision ◽

Pattern Recognition ◽

Computer Systems ◽

Unified Approach ◽

Fifth Generation ◽

Generation Computer

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Learning in multi-agent systems

The Knowledge Engineering Review ◽

10.1017/s0269888901000170 ◽

2001 ◽

Vol 16 (3) ◽

pp. 277-284 ◽

Cited By ~ 67

Author(s):

EDUARDO ALONSO ◽

MARK D'INVERNO ◽

DANIEL KUDENKO ◽

MICHAEL LUCK ◽

JASON NOBLE

Keyword(s):

Artificial Intelligence ◽

Information Retrieval ◽

Community Research ◽

Intelligence Community ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

Software Programs

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

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