Information Retrieval and Artificial Intelligence

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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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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Information retrieval and artificial intelligence

Artificial Intelligence ◽

10.1016/s0004-3702(99)00075-2 ◽

1999 ◽

Vol 114 (1-2) ◽

pp. 257-281 ◽

Cited By ~ 34

Author(s):

Karen Sparck Jones

Keyword(s):

Artificial Intelligence ◽

Information Retrieval

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Разрешение стрелочной омонимии в конструкциях с сирконстантами средствами онтологической семантики

Компьютерная лингвистика и вычислительные онтологии ◽

10.17586/2541-9781-2020-4-73-82 ◽

2020 ◽

pp. 73-82

Author(s):

Алина Андреевна Захарова

Keyword(s):

Artificial Intelligence ◽

Information Retrieval ◽

Retrieval Engine

В статье описывается экспериментальное исследование метода разрешения синтаксической неоднозначности в конструкциях с сирконстантами с помощью онтологической семантики на основе универсального лингвистического процессора AIIRE (Artificial Intelligence Information Retrieval Engine). Выявлены четыре типа неоднозначных конструкций с сирконстантами, и составлены соответствующие поисковые запросы в Национальный корпус русского языка (НКРЯ). В результате получен список из 200 неоднозначных конструкций. Неоднозначность в конструкциях устраняется путем автоматического разбора и последующего ручного выбора его правильных вариантов. Однако на этом этапе возможны следующие проблемы: «разрывы» внутри конструкций, которые обозначают отсутствие нужных семантических связей внутри конструкции, а также большое количество вариантов синтаксического анализа, называемое комбинаторным взрывом. Эти проблемы решаются с помощью таких инструментов AIIRE, как Ontohelper и онтология. Онтология используется для обработки языковых данных и понимается как набор лексических значений или понятий и отношений между ними. Ontohelper – это вспомогательный инструмент с интерфейсом редактирования, где можно моделировать и задавать с помощью онтологическихотношенийвалентностиглаголов. В результате получаются корректные разборы для 66/200 конструкций, и обосновывается,чтоэффективностьданногометодазависитоткачестваиправильностимоделированияпонятийвонтологии.

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Artificial Intelligence for Information Retrieval

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch023 ◽

2011 ◽

pp. 151-156 ◽

Cited By ~ 2

Author(s):

Thomas Mandl

Keyword(s):

Artificial Intelligence ◽

Knowledge Management ◽

Information Retrieval ◽

User Needs ◽

Similarity Function ◽

Information Need ◽

Key Technology ◽

Search Processes ◽

Amount Of Knowledge ◽

Search For Information

This article describes the most prominent approaches to apply artificial intelligence technologies to information retrieval (IR). Information retrieval is a key technology for knowledge management. It deals with the search for information and the representation, storage and organization of knowledge. Information retrieval is concerned with search processes in which a user needs to identify a subset of information which is relevant for his information need within a large amount of knowledge. The information seeker formulates a query trying to describe his information need. The query is compared to document representations which were extracted during an indexing phase. The representations of documents and queries are typically matched by a similarity function such as the Cosine. The most similar documents are presented to the users who can evaluate the relevance with respect to their problem (Belkin, 2000). The problem to properly represent documents and to match imprecise representations has soon led to the application of techniques developed within Artificial Intelligence to information retrieval.

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