On August 16, 2012 the SIGIR 2012 Workshop on Open Source Information Retrieval was held as part of the SIGIR 2012 conference in Portland, Oregon, USA. There were 2 invited talks, one from industry and one from academia. There were 6 full papers and 6 short papers presented as well as demonstrations of 4 open source tools. Finally there was a lively discussion on future directions for the open source Information Retrieval community. This contribution discusses the events of the workshop and outlines future directions for the community.

Open source information retrieval : a report on the SIGIR 2012 workshop

Big Data Now, 2015 Edition

Software requirements are described in some form of natural language (NL) text so that stakeholders with limited experience can also comprehend them easily. However, the NL text written document is inherently ambiguous, and this makes it hard to examine requirements manually to find inconsistencies, duplicates, and/or missing requirements. Use Case Analysis is a graphical depiction used to explain the interaction between the user and the system for the given user’s task. Additionally, it denotes the extension/dependency of one use case to another to understand the system flow. It is often used to identify, clarify, and categorize system requirements. However, generating use cases from a textual written description of requirements is an arduous task involving a significant manual work, which can be automated using data-driven techniques. In this poster paper, we present an initial approach for the automated identification of use case names and actor names from the textual requirements specification using machine learning techniques.

Identifying Use Case Elements from Textual Specification: A Preliminary Study

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

Semantic approaches for query expansion

Attention and meditation influence the students while receiving knowledge in teaching and learning process. This research focuses on recognition of human brainwave electroencephalogram (EEG) signals, to detect the changes of attention and meditation level signal. A non-invasive brain-computer interfaces (BCI) is used to record the brain signals. These signals are acquired directly using an Android software (EEG Analyzer) and transmitted to a computer for further data processing and analysis. The objectives of this paper are to measure and identify brainwave patterns related to respondent’s attention and
meditation level while perceive learning. The experiment has been done to eight primary school student aged seven to twelve years old. The result shows that the EEG can be measured and the pattern of EEG can be identified attention and meditation changes in students learning acceptance.

Assessing attention and meditation levels in learning process using brain computer interface

The information has evolved rapidly over the World Wide Web in the past few years. To satisfy information needs, users mostly submit a query via traditional search engines, which retrieve results on the basis of keyword matching principle. However, a keyword-based search cannot recognize the meanings of keywords and the semantic relationship among the terms in the user’s query; thus, this technique cannot retrieve satisfactory results. The expansion of an initial query with relevant meaningful terms can solve this issue and enhance information retrieval. Generally, query expansion methods consider concepts that are semantically related to query terms within the ontology as candidates in expanding the initial query. An analysis of the correct sense of query terms, rather than only considering semantic relations, is necessary to overcome language ambiguity problems. In this work, we proposed a query expansion framework on the basis of query sense analysis and semantics mining using computer science domain ontology, followed by working prototype of the system. The experts analyzed the results of system prototype over test dataset and Web data, and found a remarkable improvement in the overall search performance. Furthermore, the proposed framework demonstrated better mean average precision and recall values than the baseline method.

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Sensual Semantic Analysis for Effective Query Expansion

Software requirement specification or documentation is the expected main output from requirement engineering process that specifies the agreed requirements at appropriate level of detail; this document is expected to be understandable by both the development team and system stakeholders. The purpose of this study is to review existing human involvement in requirement specification validation studies using the formulated research question. Relevant keywords were used for the search term in various type of journal articles, conference papers, workshop, book chapters and IEEE bulletins; to identify main study on the related human factor and requirement specification validation. 39 primary studies were selected from the systematic literature review. The review identified current trend of human factor involvement in specification validation and the most widely used techniques for validation purpose. The finding suggest human factor involvement in this area are still relevant, and more focus is needed on informal specification presentation (natural language). It is also found that visual form is the most popular kind of presentation in assisting human factor in this issue.

Reviewing the challenge and practices of human factor involvement in requirement specification validation

Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data. Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology. This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model. Firstly, a detailed analysis of intended domain is performed to develop the ER model. In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology. The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology. The experts’ evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.

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A Methodology for Engineering Domain Ontology using Entity Relationship Model

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.

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Applications of Ontology in the Internet of Things: A Systematic Analysis

Nowadays, dealing with big data is a major challenge for application developers and researchers in several domains like storage, processing, indexing, integration, governance and semantic search. For decision-making and analysis purpose, semantic retrieval of information from big data is gaining more attention with the need of extracting accurate, meaningful and relevant results. Several semantic information retrieval techniques alternatively have been developed by researchers for retrieval of valuable information in big data environment. This article classifies literature and presents an analysis of five recent semantic information retrieval systems in terms of their methodologies, strengths and limitations. In addition, we evaluate these schemes on the basis of specific datasets and performance measures such as precision, recall and f-measure metrics. A comparative analysis of performance measures shows that IBRI-CASONTO achieves best f-measure value of 97.6 over other information retrieval systems.

Mokhtar Rahmah

Papers

Sensual Semantic Analysis for Effective Query Expansion

Reviewing the challenge and practices of human factor involvement in requirement specification validation

A Methodology for Engineering Domain Ontology using Entity Relationship Model

Applications of Ontology in the Internet of Things: A Systematic Analysis

Semantic Information Retrieval Systems Costing in Big Data Environment