Just for Me: Topic Maps and Ontologies

Park, Jack; Cheyer, Adam

doi:10.1007/11676904_13

Cited by 9 publications

(6 citation statements)

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“…Scopes allow you to qualify a statement and can be attached to any topic, association or occurrence in a topic map (Pepper and Moore, 2010). Thus, a topic map can describe concepts and relations, not just types, properties and relationship types, which are described by ontology (Park and Cheyer, 2006). The applications of topic maps fall into four broad categories: information integration (II), knowledge management (KM), e-learning and Web publishing.…”

Section: Motivation For the Approachmentioning

confidence: 99%

Research on feature-based opinion mining using topic maps

Xia

Wang

Chen

et al. 2016

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Purpose Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems. Design/methodology/approach Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words. Findings In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions. Research limitations/implications To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines. Originality/value This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

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Section: Motivation For the Approachmentioning

confidence: 99%

Research on feature-based opinion mining using topic maps

Xia

Wang

Chen

et al. 2016

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show abstract

“…A similar graph structure is suggest by (Park and Cheyer, 2006). However, this work separates the users' views, represented as topic maps, from the ontological description of a domain.…”

Section: Related Workmentioning

confidence: 99%

“…The topics are named according to the users' views and are mapped to the terms with corresponding names in the ontology. However, (Park and Cheyer, 2006) fails do provide a detailed description of how to use these topic maps. Several questions are left unanswered, such as details of the topic maps structure (e.g., how homonym topics are stored and differentiated), their mapping to the ontology (e.g., how to know that different topics are synonyms), the criteria used to define browsing options in a topic map (i.e, which correlated topic is more important when there are several edges leaving a topic) and the creation and maintenence of topic maps.…”

Section: Related Workmentioning

confidence: 99%

“…The solution proposed in (Michlmayr et al, 2007) individually represents the context from different users, but with no relation with a commonly accepted ontology. However, the ideias from (Michlmayr et al, 2007) might help to solve the issues left in (Park and Cheyer, 2006), by providinge suitable mappings between users' views and ontologies.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, allowing different users' views characterizes just the stage of Semantic Coexistence in the Web (Naeve, 2005). In order to reach the stage of Semantic Collaboration (Naeve, 2005) it is also necessary to establish bridges between individual users' views and standardized ontological descriptions (Park and Cheyer, 2006).…”

Section: Introductionmentioning

confidence: 99%

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CONTEXTUAL SEMANTIC SEARCH - Capturing and using the User’s Context to Direct Semantic Search

D'Agostini

Fileto²,

Dantas³

et al. 2008

Proceedings of the Tenth International Conference on Enterprise Information Systems

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One orthodox perspective of the semantic Web depends on establishing a unified representation of ontology for an universe of discourse. However this expectation is unrealistic, because different people have different views of the world, making it impossible to devise a unified knowledge view that satisfies everyone in certain cases. One solution for this problem is to allow different users' views of consensual knowledge formalized in an ontology, and keep track of the mappings between these views and the underlying ontology. This work proposes to collect context information from the users interactions with a semantic search system, in order to gradually build individual users' views mapped to an ontology. This approach allows the user to pose queries based on keywords or his personal knowledge view. In addition, each personalized knowledge view captures the preferences of a single, specific user, enabling the system to provide better search results, based on its previous experience with that user.

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Topic Mapping: A View of the Road Ahead

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2006

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Just for Me: Topic Maps and Ontologies

Cited by 9 publications

References 4 publications

Research on feature-based opinion mining using topic maps

Research on feature-based opinion mining using topic maps

CONTEXTUAL SEMANTIC SEARCH - Capturing and using the User’s Context to Direct Semantic Search

Topic Mapping: A View of the Road Ahead

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