A knowledge and reasoning toolkit for cognitive applications

Canim, Mustafa; Cornelio, Cristina; Farrell, Robert; Fokoue, Achille; Gao, Kyle; Gunnels, John A.; Iyengar, Arun; Musa, Ryan; Rodríguez-Muro, Mariano; Uceda-Sosa, Rosario

doi:10.1145/3132465.3132478

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…In contrast, the names of the entities and relation in a schema-free knowledge base are stated directly in the triple and no dictionary is required. Despite the ease and speed of interacting with triples in schema-free knowledge bases, synonyms cannot be recognized as the same entity or relation, as they have different names in their corresponding triples (Canim et al, 2017;Etzioni et al, 2011).…”

Section: Open and Fixed Knowledge Base Schemasmentioning

confidence: 99%

A Review of Machine Learning Applications in Semantic Web

Albannai¹,

Gültepe²,

Najih³

2019

JSTR

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In recent years, the rapid growth of the semantic web has encouraged the use of knowledge graphs to store and exchange data among computers. The change in data storage and retrieval has imposed the need to integrate other techniques that interact with these data. Machine Learning (ML) is the most important fields of study that interacts with data, to extract the patterns and relations that can be used in future applications. However, these techniques are implemented to be used with structured data, where each data instance is characterized using a set of predefined features. Thus, to apply machine learning techniques to knowledge graphs, it is important to convert the formation of the information in these graphs. The knowledge graph represents a collection of interconnected descriptions of entities (objects, events, situations, or abstract concepts in the real world). This work reviews the knowledge graphs and how they are structured and stored. Then, the techniques being used to employ machine learning technique to improve the knowledge graphs are illustrated. Additionally, the integration of the information in the knowledge graphs, so that, they can be used in machine learning techniques are also reviewed. This review can provide a rigid overview for future researchers to provide more attention on the main drawbacks in the existing techniques. Moreover, the review shows that the existing integration approaches limit the performance of the machine learning techniques, as the knowledge extracted from the knowledge graph is trimmed to match the requirements of the ML technique. In conclusion, new machine learning techniques that can extract knowledge directly from the knowledge graph can provide significantly better performance.

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Section: Open and Fixed Knowledge Base Schemasmentioning

confidence: 99%

A Review of Machine Learning Applications in Semantic Web

Albannai¹,

Gültepe²,

Najih³

2019

JSTR

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“…Tabular data format is a commonly used layout in domain specific enterprise documents as well as open domain webpages to store structured information in a compact form (Pasupat and Liang, 2015;Canim et al, 2019). In order to make use of these resources, many techniques have been proposed for the retrieval of tables (Cafarella et al, 2008;Zhang and Balog, 2018;Venetis et al, 2011;Shraga et al, 2020;Sun et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

Glaß¹,

Canim²,

Gliozzo³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches demonstrating that one can achieve superior performance on table QA task without even using any of these specialized pre-training techniques. The first model, called RCI interaction, leverages a transformer based architecture that independently classifies rows and columns to identify relevant cells. While this model yields extremely high accuracy at finding cell values on recent benchmarks, a second model we propose, called RCI representation, provides a significant efficiency advantage for online QA systems over tables by materializing embeddings for existing tables. Experiments on recent benchmarks prove that the proposed methods can effectively locate cell values on tables (up to ∼98% Hit@1 accuracy on WikiSQL lookup questions). Also, the interaction model outperforms the state-of-the-art transformer based approaches, pre-trained on very large table corpora (TAPAS and TABERT), achieving ∼3.4% and ∼18.86% additional precision improvement on the standard WikiSQL benchmark 1 .

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“…Text messages are associated with non-formal writing styles, including colloquialism, misspellings and homophonic abbreviations. This needs to be parsed into formal language, for example English, for it to be amenable to information search retrieval [12][13] and other text processing works under natural language processing.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Based Approach to Short Message Service (SMS) Normalization System

Adesina

2018

JRRS

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SMS language is characterized by fashion and user’s creativity but needs a transformation to proper English words or spelling to formulate natural language and text processing activities. The proposed application, Web Information Retrieval System Architecture Based on SMS (WIRSABoSMS), normalizes SMS language to retain its original syntactic structure. The concept of mobile agents in web technology was introduced as a medium to achieving Short Message Service (SMS) normalization. SMS normalization was carried out with the adoption of multi-agent technology, as agents are involved in character search, sort, and compare of the strings written in SMS form into its parental orthography. This architecture was designed based on web information retrieval system (IRS) in order to achieve SMS normalization. BLEU (bilingual evaluation understudy) was used to evaluate the quality of text. BLEU scores compare the human judgment with that of the machine translation using two set of corpora. The outcome of syntactic text message normalization recorded an average of 90% performance (for the corpus collected from researchers) when compared with the similar test conducted with human judgment using BLEU scores metric in an health-related domain.

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A knowledge and reasoning toolkit for cognitive applications

Cited by 3 publications

References 44 publications

A Review of Machine Learning Applications in Semantic Web

A Review of Machine Learning Applications in Semantic Web

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

A Multi-Agent Based Approach to Short Message Service (SMS) Normalization System

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