Contelog: A declarative language for modeling and reasoning with contextual knowledge

Alsaig, Ammar; Alagar, Vangalur S.; Shiri, Nematollaah

doi:10.1016/j.knosys.2020.106403

Cited by 9 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Context plays a significant role in DialogueCRN [11], context reasoning [13], contextual query refinement [27], and contextual knowledge based query answering [9] systems. Only in the recently proposed ConteLog [12] was context modeled by giving it a syntax and a direct semantic. However, the model and the semantics assigned can only capture limited capabilities with a significant degree of user involvement.…”

Section: Context As a First-class Citizen For Query Answeringmentioning

confidence: 99%

“…Early research in database (DB) and artificial intelligence (AI) context has been modeled sometimes indirectly without an explicit treatment. For example, cooperative query answering [1,2], indefinite and maybe queries [3], incomplete databases [4], null values [5,6], disjunctive databases [7,8], contextual query answering [9,10] and knowledge representation and reasoning [11][12][13]. Except for ConteLog [12], most of these systems treat context indirectly and at times, as a by product.…”

Section: Introductionmentioning

confidence: 99%

“…For example, cooperative query answering [ 1 , 2 ], indefinite and maybe queries [ 3 ], incomplete databases [ 4 ], null values [ 5 , 6 ], disjunctive databases [ 7 , 8 ], contextual query answering [ 9 , 10 ] and knowledge representation and reasoning [ 11 – 13 ]. Except for ConteLog [ 12 ], most of these systems treat context indirectly and at times, as a by product.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

Jamil

2023

Preprint

View full text Add to dashboard Cite

In conversational query answering systems, context plays a significant role in accurately and meaningfully carrying it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries. In this paper, we introduce a novel way of viewing contexts as a distance function via the concept of query relaxation. We demonstrate that a typed domain distance function is sufficient to model context in a conversation. Our approach is based on the idea of non-monotonic constraint inheritance in a context hierarchy.

show abstract

Section: Context As a First-class Citizen For Query Answeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

Jamil

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Bağlamı dahil eden yöntemin, vakaların %69'unda insan yanıtlayıcılarla aynı geometrik konfigürasyonu seçerek test edilen dört yöntem arasında en başarılı yöntem olduğu ifade edilmektedir. Bilgi çizgelerinde bağlam konulu farklı bir araştırmada, mevcut bağlam farkındalıklı sistemlerde bilgi ve bağlamın resmi olarak entegre edilmemiş olmasından yola çıkılarak, bağlamsal muhakemeye ulaşmak için biçimsel bir yaklaşım geliştirilmiştir [1].…”

Section: İlgi̇li̇ çAlişmalarunclassified

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

TAKAN

2023

International Journal of Advances in Engineering and Pure Sciences

View full text Add to dashboard Cite

Bilginin doğasına ilişkin, onu şekillendiren çok çeşitli unsurlar bulunmaktadır. Örneğin güvenirlik, tutarlılık, değişmezlik ve bağlam gibi mekanizmalar bunların başında gelir. Ancak söz konusu mekanizmaların bilgi çizgesinde temsil edilmesi oldukça yaygın bir problemdir. Çalışmamızda bu problemin çözümüne katkıda bulunmak amacıyla, bilginin karmaşık doğasına ilişkin güven, tutarlılık, değişmezlik ve bağlam gibi temel mekanizmalar, hashing teknolojisi kullanılarak bilgi çizgesine entegre edilmiştir. Çalışmamızda bu eklentiler, bilgi çizgesinden ayrı tutularak, yapıların işlevselliklerinin bozulmaması sağlanmıştır. Geliştirdiğimiz eklentiler sayesinde bir bilgi değiştiğinde onu etkileyen tüm bilgilerin otomatik güncellenmesi, belirsizlik, bilgiler arasında sıralama yapılamaması, bazı bilgilerin değişmez olarak tutulamaması ve bilgiler arasında hızlı bir karşılaştırmanın yapılamaması gibi yaygın bilgi çizgesi problemleri, örnek senaryolar üzerinden test edilerek çözüme kavuşturulmuştur. Çalışmamızın, bilgi çizgesinin iyileştirilmesine yönelik literatüre ve bilgi çizgesini kullanan yapay zeka yazılımlarının geliştirilmesine katkı sunması beklenmektedir.

show abstract

“…The method that incorporates context is said to be the most successful of the four methods tested, selecting the same geometric configuration as human respondents in 69% of cases. In another study on the context in KGs, a formal approach to achieve contextual reasoning was developed based on the lack of formal integration of knowledge and context in existing context-aware systems ( Alsaig, Alagar & Nematollaah, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Knowledge graph augmentation: consistency, immutability, reliability, and context

Takan¹

2023

PeerJ Computer Science

View full text Add to dashboard Cite

A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved. These shortcomings can be summarised as the inability to automatically update all the knowledge affecting a piece of knowledge when it changes, ambiguity, inability to sort the knowledge, inability to keep some knowledge immutable, and inability to make a quick comparison between knowledge. In our work, reliability, consistency, immutability, and context mechanisms are integrated into the knowledge graph to solve these deficiencies and improve the knowledge graph’s performance. Hash technology is used in the design of these mechanisms. In addition, the mechanisms we have developed are kept separate from the knowledge graph to ensure that the functionality of the knowledge graph is not impaired. The mechanisms we developed within the scope of the study were tested by comparing them with the traditional knowledge graph. It was shown graphically and with t-test methods that our proposed structures have higher performance in terms of update and comparison. It is expected that the mechanisms we have developed will contribute to improving the performance of artificial intelligence software using knowledge graphs.

show abstract

Contelog: A declarative language for modeling and reasoning with contextual knowledge

Cited by 9 publications

References 13 publications

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

Knowledge graph augmentation: consistency, immutability, reliability, and context

Contact Info

Product

Resources

About