scholarly journals Разрешение стрелочной омонимии в конструкциях с сирконстантами средствами онтологической семантики

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.

Rancang Bangun Aplikasi Chatbot Sebagai Media Pencarian Informasi Anime Menggunakan Regular Expression Pattern Matching

2017 ◽  
Vol 9 (1) ◽  
pp. 19-24 ◽  
Author(s):  
David Domarco ◽  
Ni Made Satvika Iswari
Keyword(s):  
Information Retrieval ◽  
Expression Pattern ◽  
Pattern Matching ◽  
Language Processing ◽  
Regular Expression ◽  
Technology Development ◽  
Data Retrieval ◽  
Index Terms ◽  
Retrieval Engine ◽  
Behavioral Intention To Use

Technology development has affected many areas of life, especially the entertainment field. One of the fastest growing entertainment industry is anime. Anime has evolved as a trend and a hobby, especially for the population in the regions of Asia. The number of anime fans grow every year and trying to dig up as much information about their favorite anime. Therefore, a chatbot application was developed in this study as anime information retrieval media using regular expression pattern matching method. This application is intended to facilitate the anime fans in searching for information about the anime they like. By using this application, user can gain a convenience and interactive anime data retrieval that can’t be found when searching for information via search engines. Chatbot application has successfully met the standards of information retrieval engine with a very good results, the value of 72% precision and 100% recall showing the harmonic mean of 83.7%. As the application of hedonic, chatbot already influencing Behavioral Intention to Use by 83% and Immersion by 82%. Index Terms—anime, chatbot, information retrieval, Natural Language Processing (NLP), Regular Expression Pattern Matching

Information Retrieval and Artificial Intelligence

2020 ◽  
pp. 147-180
Author(s):  
Mohand Boughanem ◽  
Imen Akermi ◽  
Gabriella Pasi ◽  
Karam Abdulahhad
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval

A New Method of Calculating the Concept Similarity

2013 ◽  
Vol 321-324 ◽  
pp. 1951-1956
Author(s):  
Guo Wei Yang ◽  
Min Chen ◽  
Xiao Feng Zhang
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Knowledge Representation ◽  
Influencing Factor ◽  
Domain Ontology ◽  
New Method ◽  
Area Density ◽  
Concept Similarity

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.

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

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.

scholarly journals Learning in multi-agent systems

2001 ◽  
Vol 16 (3) ◽  
pp. 277-284 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document