Search results

Large collections of patent documents disclosing novel, non-obvious technologies are publicly available and beneficial to academia and industries. To maximally exploit its potential, searching these patent documents has increasingly become an important topic. Although much research has processed a large size of collections, a few studies have attempted to integrate both patent classifications and specifications for analyzing user queries. Consequently, the queries are often insufficiently analyzed for improving the accuracy of search results. This paper aims to address such limitation by exploiting semantic relationships between patent contents and their classification.

The contributions are fourfold. First, the authors enhance similarity measurement between two short sentences and make it 20 per cent more accurate. Second, the Graph-embedded Tree ontology is enriched by integrating both patent documents and classification scheme. Third, the ontology does not rely on rule-based method or text matching; instead, an heuristic meaning comparison to extract semantic relationships between concepts is applied. Finally, the patent search approach uses the ontology effectively with the results sorted based on their most common order.

The experiment on searching for 600 patent documents in the field of Logistics brings better 15 per cent in terms of F-Measure when compared with traditional approaches.

The research, however, still requires improvement in which the terms and phrases extracted by Noun and Noun phrases making less sense in some aspect and thus might not result in high accuracy. The large collection of extracted relationships could be further optimized for its conciseness. In addition, parallel processing such as Map-Reduce could be further used to improve the search processing performance.

The experimental results could be used for scientists and technologists to search for novel, non-obvious technologies in the patents.

High quality of patent search results will reduce the patent infringement.

The proposed ontology is semantically enriched by integrating both patent documents and their classification. This ontology facilitates the analysis of the user queries for enhancing the accuracy of the patent search results.

This paper presents a systematic XML Schema design approach which conceptually captures semantics of the problem domain at conceptual level and represents such semantics in XML Schema at schema level. At the conceptual level, objects, their inter‐relationships and constraints are semantically powered by object‐oriented models. At the schema level, these conceptual semantics are comprehensively represented in textbased representation of XML Schema using various schema components and design styles, each of which offers different quality characteristics. Two primary design styles in use are nesting and linking. The nesting design styles are developed based on the choice of schema components and their definition/declaration scopes (global vs. local), whereas the linking design styles use referencing facilities provided by XML Schema and other XML technologies such as XLink and XPointer. With an in‐depth analysis of outstanding problems of existing approaches, the proposed design approach is motivated to help improve the quality and robustness of the XML documents in large‐scale XML‐based applications.

This paper aims to solve the stated limitations through (i) exploring usages of scenarios and (ii) proposing a lightweight iterative requirement exploration process that utilizes scenarios to transform the vague ideas of the developers into concrete application’s vision. The explosive growth in pervasiveness, programmability and computational capability of smartphones and tablets unlocks the possibility for many uses of technology, including end-user development (EUD) where end-users create applications as pragmatic tools for their work and hobbies. An extensive amount of research effort has been done to solve EUD challenges; however, the focus of such research lies mainly on designing and implementing software applications.

Based on review on existing application of usage scenario in software application development, we proposed SRE – a scenario-based requirement exploration method and MobiPSE – a lightweight software process that applies SRE. Two EUD projects performed by undergraduate students were used as case studies to test and fine-tune methods.

This paper proposes MobiPSE – a lightweight iterative software process that resolves around our proposed SRE method. Through case studies on MobiPSE, we confirmed that MobiPSE works in real-life scenarios and emphasized two points: ad hoc training has limited effect and lack of technical expertise can still leave a negative impact on requirement exploration process.

Because of the limited number of case studies performed for testing and tuning purpose, the proposed methods may not be able to satisfy all cases of EUD. MobiPSE can be fine-tuned further with additional case studies of different scale and team structure.

This paper fulfils the need for a lightweight method for exploring requirements and ideas in a EUD project based on creating and analyzing possible usage scenarios.

This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities.

A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity.

An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches.

The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.