An ontology-based semantic similarity metric to empower semantic search

Heterogeneity in documents is a challenge for information Retrieval. The keyword search focuses on matching the keywords with web repositories. It does not consider the synonyms or semantically similar words. The heterogeneity of the content makes retrieval inadequate. Semantic search helps to capture more appropriate results using domain ontology. Keyword search is extended with the help of similar concepts of ontology. Similarity between the ontological concepts is recognized to get appropriate search results. Once the semantic similarity among the concepts is known, more relevant documents can be retrieved. In this paper, we propose a metric based on traditional methods, combined with computational techniques to measure the similarity between concepts. The paper gives the concept of DOT (Domain Ontology Tree). It uses conventional definitions of the Tree (Data Structure) for ontology and proposes a method of partitioning to calculate the similarity. The method is based on IS-A hierarchical relationship. We have implemented a prototype system for the support of the proposed method, and also compared it with existing methods, the results are encouraging.

Ontology Based Arabic Opinion Mining

In Arabic language, studies in the area of opinion mining are still limited compared to that being carried out in other languages. In this paper, we highlight the problem for Arabic opinion mining techniques when analysing reviews having different features with different opinion strengths. The traditional works of opinion mining consider all features extracted from the reviews to be equally important, so they fail to determine the correct opinion of the review and make the review's sentiment classification less accurate. This research presents a technique based on an ontology that uses feature level classification to classify Arabic user-generated reviews by identifying the relevant features from the review based on the degree of these features in the ontology tree. Then, we exploit the important features extracted to determine the overall polarity of the review. Moreover, summarisation for each feature is done to determine which feature has satisfied or dissatisfied customers. To evaluate our work, we use public datasets which are hotels and books datasets. We used [Formula: see text]-measure metrics to assess the performance and compare the results with other supervised and unsupervised techniques. Also, subjective evaluation is used in our method to demonstrate the effectiveness of feature and opinion extraction process and summarisation. We show that our method improves the performance compared with other opinion mining classification approaches, obtaining 78.83% [Formula: see text]-measure in hotels domain and 79.18% in books domain. Furthermore, the subjective evaluation shows the effectiveness of our method by getting an average [Formula: see text]-measure of 84.62% in hotels dataset and 86.31% in books dataset.

Granular Computing Based Ontology Learning Model and Its Applications

The cores of granular computing are the granule, the granule layer and the granule structure. In this paper the concepts “Ontology granule” and “Compatible granule” were defined, applying the granular computing ideas and an ontology model, an Ontology granular set; and thus ontology tree generation algorithms were proposed. These algorithms produce an initial ontology granular set with a compatible class, and extend the other ontology granules by the vector of intension IG, building a lattice hierarchy and a conception tree model of ontology with the vector of relation RG. The empirical research of the traditional Chinese medicine ontology shows that these algorithms are correct and efficient, and provide a good technical way for ontology learning.

Study of Topic Life Cycle Based on Hierarchical HMM

This paper presents a topic ontology tree based hierarchical HMM (hHMM), and the generation of topic property based life cycle curve is studied. Compared with the traditional methods, this model can describe the hierarchy relations of topics.

Object-Oriented Method of Constructing Electric Power System Domain Ontology Based on Protégé

This paper presents a method for domain ontology of constructing power system based on Protégé . This paper describes how to package Interoperability Portlet into semantic Portlet, then create semantic data association between ontology-based semantic Portlet and using semantic inference rules to realize Portlet interoperability. The main task of this paper is to construct and deploy (service release) ontology, find out the concept of the power of public information model CIM about our project, create an XML format file with tools such as Protégé . This paper will elaborate on the use of object-oriented constructed method for constructing the ontology. The construction of the ontology tree provides the foundation of Web services’ semantic annotation.