A Hybrid Approach Using Ontology Similarity and Fuzzy Logic for Semantic Question Answering

Rani, Monika; Muyeba, Maybin K.; Vyas, O. P.

doi:10.1007/978-3-319-07353-8_69

Cited by 20 publications

(13 citation statements)

References 7 publications

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“…Properties are disambiguated using predefined rewriting rules which are categorized by context. Rani et al [115] use fuzzy logic co-clustering algorithms to retrieve documents based on their ontology similarity. Possible senses for a word are assigned a probability depending on the context.…”

Section: Ambiguitymentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

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Abstract. Semantic Question Answering (SQA) removes two major access requirements to the Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a specific vocabulary. Because of the complexity of natural language, SQA presents difficult challenges and many research opportunities. Instead of a shared effort, however, many essential components are redeveloped, which is an inefficient use of researcher's time and resources. This survey analyzes 62 different SQA systems, which are systematically and manually selected using predefined inclusion and exclusion criteria, leading to 72 selected publications out of 1960 candidates. We identify common challenges, structure solutions, and provide recommendations for future systems. This work is based on publications from the end of 2010 to July 2015 and is also compared to older but similar surveys.

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Section: Ambiguitymentioning

confidence: 99%

Survey on challenges of Question Answering in the Semantic Web

et al. 2017

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“…Also, Lau et al (2009) illustrated the design and development of a fuzzy ontology-based granular information retrieval system to facilitate domain-specific search. Rani et al (2014) developed a hybrid approach for semantic question answering based on semantic fuzzy ontology for retrieval systems. Rani et al (2014) developed a hybrid approach for semantic question answering based on semantic fuzzy ontology for retrieval systems.…”

Section: Information Retrievalmentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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“…Probabilistic models such as in Bayesian nets and Markov network, models decision processes based on some measurable parameters that are for instance discrete (true or false) from state-tostate or node-to-node transition. Typically, a network is a graphical representation with a collection of nodes representing random variables and edges connecting the nodes [28]. While Bayes nets are of directed graphs, Markov are undirected.…”

Section: Introductionmentioning

confidence: 99%

A System of Serial Computation for Classified Rules Prediction in Non-Regular Ontology Trees

Ehimwenma¹,

Crowther²,

Beer³

2016

IJAIA

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Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters.

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A Hybrid Approach Using Ontology Similarity and Fuzzy Logic for Semantic Question Answering

Cited by 20 publications

References 7 publications

Survey on challenges of Question Answering in the Semantic Web

Survey on challenges of Question Answering in the Semantic Web

A survey on fuzzy ontologies for the Semantic Web

A System of Serial Computation for Classified Rules Prediction in Non-Regular Ontology Trees

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