Building an Event Ontology for Historical Domain to Support Semantic Document Retrieval

Ramli, Fatihah; Noah, Shahrul Azman Mohd

doi:10.18517/ijaseit.6.6.1634

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…We particularly interested on looking into the role of LOD in supporting serendipitous recommendations. The LOD has shown to be beneficial in information retrieval [32], but its capability to support serendipitous recommendation is yet to be experimented. Serendipitous items prioritize the novelty and unexpected value in recommending an item to the user.…”

Section: Discussionmentioning

confidence: 99%

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Towards Serendipity for Content–Based Recommender Systems

Saat

Noah

Mohd

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Recommender systems are intelligent applications build to predict the rating or preference that a user would give to an item. One of the fundamental recommendation methods in the content-based method that predict ratings by exploiting attributes about the users and items such as users' profile and textual content of items. A current issue faces by recommender systems based on this method is that the systems seem to recommend too similar items to what users have known. Thus, creating over-specialisation issues, in which a self-referential loop is created that leaves user in their own circle of finding and never get expose to new items. In order for these systems to be of significance used, it is important that not only relevant items been recommender, but the items must be also interesting and serendipitous. Having a serendipitous recommendation let users explore new items that they least expect. This has resulted in the issues of serendipity in recommender systems. However, it is difficult to define serendipity because in recommender system, there is no consensus definition for this term. Most of researchers define serendipity based on their research purposes. From the reviews, majority shows that unexpected as the important aspect in defining serendipity. Thus, in this paper, we aim to formally define the concept of serendipity in recommender systems based on the literature work done. We also reviewed few approaches that apply serendipity in the content-based methods in recommendation. Techniques that used Linked Open Data (LOD) approaches seems to be a good candidate to find relevant, unexpected and novel item in a large dataset.

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Section: Discussionmentioning

confidence: 99%

“…Thus, a semantic analysis is seen as the best method to encounter the problem faced. One of the key idea is to adopt the knowledge bases such lexicon or ontologies [4,32] as explicit output that can be used to annotate other items related and represent the profiles in order to provide a semantic interpretation of user preferences.…”

Section: A Item Representationmentioning

confidence: 99%

Towards Serendipity for Content–Based Recommender Systems

Saat

Noah

Mohd

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The total numbers of concepts that are involved in event and stuff ontologies are 22 concepts. While it is intended for news documents, it is found to be appropriate in our case as it contains the detailed representation of events, people, organizations, locations, tangible and intangible things as well as documents [20]. SNaP ontology is accessible to the public and can be considered as an expandable ontology.…”

Section: Elements Examplesmentioning

confidence: 99%

Using Ontology-Based Approach to Improved Information Retrieval Semantically for Historical Domain

Ramli¹,

Noah²,

Kurniawan³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Searching and retrieving documents from large historical archives prove to be challenging for the information retrieval (IR) field as historians typically employ their knowledge, experience, and intuition. There are several works done on the application of IR in historical documents. As such, the conventional IR model is mostly used a simple Bag-of-Word (BOW) approach and usually unable to support precise document retrieval for the domain of history. We proposed an ontology-based approach to semantically index and ranked rich historical documents. The historical documents relating to the Vietnam War were chosen for this study. Several existing ontologies have been reviewed to identify the most suitable concepts and properties which contain rich information pertaining to relevant entities such as an event, time, and people. The domain ontology was developed by utilizing the existing Simple News and Press (SNaP) ontology and extended with concepts related to the Vietnam War. The ontology was then semantically mapped with concepts found in a collection of 133 documents relating to the Vietnam war. In this paper, we also proposed a simple ontology-based weighting mechanism derived from the classic tf-idf scoring scheme. Finally, 20 SPARQL queries are implemented to do the evaluation. The evaluation shows that the proposed ontological-based approach achieved better results as compared to the base-line BM-25 probabilistic retrieval model in terms of precision and recall metrics. The use of the ontology-based approach in document retrieval can compete with the keyword-based approach.

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“…It is evident from previous works that, the application of NLP on enormous text document is incomplete and inefficient without identifying meaningful information (semantics) from the documents [6]- [9], [30]- [31]. This paper focuses on the use of NLP techniques to analyse and represent text semantics due to its vast applicability on resolving text issues [10].…”

Section: Introductionmentioning

confidence: 99%

Representing Semantics of Text by Acquiring its Canonical Form

Taiye

Kamaruddin

Ahmad

2017

International Journal on Advanced Science, Engineering and Information Technology

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Canonical form is a notion stating that related idea should have the same meaning representation. It is a notion that greatly simplifies the task by dealing with a single meaning representation for a wide range of expression. The issue in text representation is to generate a formal approach of capturing meaning or semantics in sentences. This issue includes heterogeneity and inconsistency in the text. Polysemous, synonymous, morphemes and homonymous word pose serious drawbacks when trying to capture senses in sentences. This calls for a need to capture and represent senses in order to resolve vagueness and improve understanding of senses in documents for knowledge creation purposes. We introduce a simple and straightforward method to capture the canonical form of sentences. The proposed method first identifies the canonical forms using the Word Sense Disambiguation (WSD) technique and later applies the First Order Predicate Logic (FOPL) scheme to represent the identified canonical forms. We adopted two algorithms in WSD, which are Lesk and Selectional Preference Restriction. These algorithms concentrate mainly on disambiguating senses in words, phrases, and sentences. In addition, we adopted the First Order Predicate Logic scheme to analyse argument predicate in sentences, employing the consequence logic theorem to test for satisfiability, validity, and completeness of information in sentences.

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Building an Event Ontology for Historical Domain to Support Semantic Document Retrieval

Cited by 13 publications

References 14 publications

Towards Serendipity for Content–Based Recommender Systems

Towards Serendipity for Content–Based Recommender Systems

Using Ontology-Based Approach to Improved Information Retrieval Semantically for Historical Domain

Representing Semantics of Text by Acquiring its Canonical Form

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