Shared Ontology for Knowledge Management

Semantic web works on producing machine readable data and aims to deal with large amount of data. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that consists in the existing web pages. Semantic search engines are extensions to traditional engines and overcome the difficulties faced by them. This paper summarizes semantic web, concept of traditional and semantic search engines and infrastructure. Also semantic search approaches are detailed. A summary of the literature is provided by touching on the trends. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging. Multimedia retrieval is a newly scope of semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia retrieval is analyzed within semantic search.

Typical electronic business applications need to make queries about products and services made available by sellers and service providers through the Web. Unfortunately, it can be a very time consuming task to discover information as simple as those, since every site has its own metaphore. The so-called search engines can facilitate such task because a client provides some keywords and obtain a list of Uniform Resource Locators (URL) that point to the target sites, but it does not prevent from interacting with each site. We propose and define a novel kind of service for the Web: the semantic search engine. Differently from traditional search engines, a semantic search engine stores semantic information about Web resources and is able to solve complex queries, considering as well the context where the Web resource is targeted. We show, through examples, how a semantic search engine may be employed in order to permit clients obtain information about commercial products and services, as well as about sellers and service providers which can be hierarchically organized. Semantic search engines may seriously contribute to the development of electronic business applications since it is based on strong theory and widely accepted standards.

Googling for Health Information

Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them. We present a novel search engine for the construction industry, HSEarch (http://www.nactem.ac.uk/hse/), which uses TM methods to provide semantically-enhanced, faceted search over a repository of workplace accident reports. Compared to previous TM-driven search engines for the construction industry, HSEarch provides a more interactive means for users to explore the contents of the repository, to review documents more systematically and to locate relevant knowledge within them.

More and more search engine users are expecting direct answers to their information needs, rather than links to documents. Semantic search and its recent applications enabled search engines to organize their wealth of information around entities. Entity linking and retrieval provide the building stones for organizing the web of entities. This tutorial aims to cover all facets of semantic search from a unified point of view and connect real-world applications with results from scientific publications. We provide a comprehensive overview of entity linking and retrieval in the context of semantic search and thoroughly explore techniques for query understanding, entity-based retrieval and ranking on unstructured text, structured knowledge repositories, and a mixture of these. We point out the connections between published approaches and applications, and provide hands-on examples on real-world use cases and datasets.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in socialization. The amount of scientific research results on motherhood related to ASD has grown exponentially; however, there are no bibliometric studies in this field. Objective: This article aimed to analyze scientific research on motherhood related to the autism spectrum published in WoS. Articles on motherhood related to ASD were retrieved from the Web of Science (WoS). The advanced search interface used was “Title of the article, , Keywords”. The analysis and visualization of the selected documents and their data were performed using a wide range of tools and software such as MS Excel (v16.0), VOS viewer (version 1.6.15), and R packages (Biblioshiny, version 2.0). A total of 1660 articles were included in this study. Most of the publications were original articles. The United States published the most significant number of articles among the countries identified. P.R. Hastings, M.M. Seltzer, and J. Van de Water were the main authors. The Journal of Autism and Developmental Disorders was the most productive and impactful journal. The main research topics were related to mental health and social support in the role of motherhood. This desk study provides researchers with a comprehensive understanding of ASD-related maternity research trends by evaluating relevant publications in recent decades. The results of this bibliometric analysis can serve as a basis and orientation for new studies.

Research and development in the area of biomedical literature analysis aims at providing life science researchers with effective means to access and exploit knowledge contained in scientific publications. Virtually all journal publications and many conference proceedings are nowadays readily available in an electronic form—for instance, as abstracts through the MEDLINE citation index or as full-text articles through PubMed Central. Nevertheless, keeping up to date with and searching for recent findings in a research domain remains a tedious task hampered by inefficient and ineffective means for access and exploitation. Biomedical text analysis aims to improve access to unstructured knowledge by alleviating searches, providing auto generated summaries of documents and topics, linking and integrating publications with structured resources, visualizing content for better understanding, and guiding researchers to novel hypotheses and into knowledge discovery. Focused research over recent years has improved fundamental solutions for biomedical text mining, such as document retrieval, named entity recognition, normalization and grounding, and extraction of relationships, with levels of accuracy that reach human annotators when considering inter annotator agreement. Consequently, more and more integrative analysis tools were put forward by the text mining community targeting a broad audience of end users: generic and task-specific search engines for life science researchers, interfaces for networks synthesis based on textual evidences, or more specialized tools searching for transcription factors, or primer sequences. This special issue of Advances in Bioinformatics presents overviews and examples of end-user-oriented biomedical text mining tools for bioinformaticians, molecular biologists, biochemists, clinicians, pharmacologists, and other researchers in life sciences. We start with A. Manconi et al. survey on “Literature retrieval and mining in bioinformatics: state of the art and challenges.” The authors introduce the major concepts that life science researchers should be familiar with getting the best out of existing text mining solutions, and survey key tools and research. In a dedicated second part of their survey, the authors address the major challenges both life science researchers and solution developers are facing at this point. The reader will find plenty of references to existing search tools, resources, and research papers. A. E. Thessen et al. focus on a particular domain, presenting an overview of “Applications of natural language processing in biodiversity science.” The authors review the application of natural processing and machine learning for biological information extraction regarding cellular processes, taxonomic names, and morphological characters. You will find detailed examples, a summary of all steps involved in information extraction, and lots of references to existing tools and resources. S. T. Ahmed et al. introduce their semantic faceted search engine, BioEve, in “A novel framework to facilitate interactive literature search.” They couple an automated extraction system with a cognitive search and navigation service, to alleviate the process of searching and browsing huge amounts of literature such as provided/delivered by MEDLINE. BioEve enables interactive query refinement and suggests concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search directions, thereby achieving semantic enrichment that improves insight gains while searching literature. S. V. Landeghem et al. present their EVEX resource in “Exploring biomolecular literature with EVEX: connecting genes through events, homology, and indirect associations.” The authors extracted more than 20 million biomolecular events involving genes and proteins, such as phosphorylation and gene regulation, from MEDLINE. The online tool generates a summary on the searched gene denoting all regulated genes, binding partners, subcellular locations, and other related data linked to the searched gene. We conclude this special issue with the paper by A. Divoli et al., discussing whether “Do Peers see more in a paper than its authors.” In a meta-analysis using automatic text analysis, they address questions such as how informative an abstract is compared to the full text; and how peers and authors might view the major contributions of a paper differently. Their analyses are comparing the information content of an abstract, as written by the paper's authors, to sentences that mention the paper as a reference, written by peers. Using this strategy, A. Divoli et al. found, for example, that citing sentences contain 20% additional concepts (likely important contributions) that were not mentioned in the abstract of the paper referred to, but maybe should have been to help attract even more peers.

Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.

In this paper, we describe theit Search Tree visual language. It is a novel methodology able to support users to build up complex queries to be run on given search engines. For using this visual language, neither parentheses nor precedence rules are needed, nor the specific ability to perform advanced search tasks. The language is proven to have the same expressive power as the expressions in Sum Of Product form. In order to prove the appropriateness of our proposal, we measured the usability of the proposed querying approach against the traditional Yahoo TM web search query language. Results show that, even if both the approaches fully support users in terms of efficacy, the Search Tree visual language significantly improves task efficiency, both in terms of the number of actions performed and the time requested with respect to the advanced search interface. Copyright © 2010 John Wiley & Sons, Ltd.

How does the credibility of vaccine information compare across traditional search engines and AI-based conversational agents?

The recent advancements in the web allow users to generate multimedia content, resulting in multimedia information proliferation. Existing search engines provide access to multimedia content via a disjoint assembly of media-specific results called verticals. However, this decentralized assembly of media contents requires manual aggregation and synthesizing efforts at the user’s end, hindering the information exploration process and subsequently may cause cognitive overload, hence, demanding innovative tools to discover multimedia content. The researchers have devised numerous state-of-the-art approaches; however, analysis to confirm the efficacy has little emphasis. This study investigates users’ complex multimedia information-seeking behavior over state-of-the-art web search systems to unveil the user’s information-seeking issues. Our research employs between-subjects study and post hoc analysis strategies to analyze participants’ information-seeking characteristics. The study design adopted statistical hypothesis testing to consolidate previous user behavioral studies, confirm existing strategies, and present recommended practices for future general-purpose web search engines. The participants were assigned Google and an advanced discovery search system using the same multimedia dataset to ensure the obtained results’ credibility. The primary behavioral parameters include search efforts, multimedia content exploration, search user interface (SUI), information management and presentation, and user cognition. This study uncovers several inadequacies of the search engines in meeting users’ complex discovery needs, including 29.6% less user engagement, 43% system and searching dissatisfaction, and 32% less knowledge acquisition with 63.9% increased clicking effort on traditional search engines. The results confirmed previous user studies and suggest novel research recommendations statistically significant in multimedia information exploration-related endeavors.

To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. While large language models (LLMs) have shown emergent reasoning skills on arbitrary tasks, existing work primarily concentrates on explored environments, typically focusing on either navigation or manipulation tasks in isolation. In this work, we propose MoMa-LLM, a novel approach that grounds language models within structured representations derived from open-vocabulary scene graphs, dynamically updated as the environment is explored. We tightly interleave these representations with an object-centric action space. Given object detections, the resulting approach is zero-shot, open-vocabulary, and readily extendable to a spectrum of mobile manipulation and household robotic tasks. We demonstrate the effectiveness of MoMa-LLM in a novel semantic interactive search task in large realistic indoor environments. In extensive experiments in both simulation and the real world, we show substantially improved search efficiency compared to conventional baselines and state-of-the-art approaches, as well as its applicability to more abstract tasks.

Search engines play important role in the success of the Web. Search engine helps the users to find the relevant information on the internet. Due to many problems in traditional search engines has led to the development of semantic web. Semantic web technologies are playing a crucial role in enhancing traditional search, as it work to create machines readable data and focus on metadata. However, it will not replace traditional search engines. In the environment of semantic web, search engine should be more useful and efficient for searching the relevant web information. It is a way to increase the accuracy of information retrieval system. This is possible because semantic web uses software agents; these agents collect the information, perform relevant transactions and interact with physical devices. This paper includes the survey on the prevalent Semantic Search Engines based on their advantages, working and disadvantages and presents a comparative study based on techniques, type of results, crawling, and indexing.

Interacting with the Web of Things

People search is an important search task where the goal is to find people instead of documents.Providing search facets in a people search system can help users better describe their search intents.Some systems provide checkboxes for the discrete values of each facet to assist users filtering search results.Some other systems in recent studies provide sliders to represent the continuous values of facets.Slider facets enable an interactive search system to handle the facets without discrete values.Yet, the ways of how users interact with slider facets are rarely studied, particularly for people search tasks.Based on a user study with 24 participants using an interactive people search interface with three slider facets, we find that users indeed utilize the slider facets consistently in their search processes to fine-tune the search results ranking.We also find that although tuning the slider facets values can bring performance boost but users are lack of abilities to locate the optimal facet-values, which indicates the necessity of providing automatic facet-value suggestion.