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Outline

Title
Abstract
Problem Overview
Effectiveness Study
Results and Analysis
Future Work
Keyword Search Results
Metadata Search
An Overview
Combining the Results
Experimental Sessions
Conclusions and Future Work
Background
Material: Queries, Test Corpus and Gold Standard
Conclusion and Perspectives
Related Works
Proposed Method
Parsing and Structure Tree Analysis
Experiments and Discussion
Technical Background
Discussion
Resultset and Ranking
Conclusion
Search Results for Aguayo
Introduction
Related Work
Conclusions
References
All Topics
Computer Science
Information Systems

Enhancing semantic search using n-levels document representation

Profile image of Giovanni SemeraroGiovanni Semeraro

2008, Semantic Search

Abstract
sparkles

AI

This research explores the limitations of traditional keyword-centric search technologies and presents an approach to enhance semantic search through n-level documents representation. It examines the potential of semantic technologies to match complex user information needs with expressive resource descriptions, outlining key challenges such as user query translation, metadata extraction, and the handling of vague information. Insights from ongoing projects at Cycorp highlight the importance of semantic indexing in improving access to knowledge in extensive document collections, particularly addressing the difficulties encountered in rare search scenarios.

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Semantic Web Search: Perspectives and Key Technologies
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Current keyword-based Web search engines (e.g. Google i) provide access to thousands of people for billions of indexed Web pages. Although the amount of irrelevant results returned due to polysemy (one word with several meanings) and synonymy (several words with one meaning) linguistic phenomena tends to be reduced (e.g. by narrowing the search using human-directed topic hierarchies as in Yahoo ii), still the uncontrolled publication of Web pages requires an alternative to the way Web information is authored and retrieved today. This alternative can be the technologies of the new era of the Semantic Web. The Semantic Web, currently using OWL language to describe content, is an extension and an alternative at the same time to the traditional Web. A Semantic Web Document (SWD) describes its content with semantics, i.e. domain-specific tags related to a specific conceptualization of a domain, adding meaning to the document's (annotated) content. Ontologies play a key role to providing such description since they provide a standard way for explicit and formal conceptualizations of domains. Since traditional Web search engines cannot easily take advantage of documents' semantics, e.g. they cannot find documents that describe similar concepts and not just similar words, semantic search engines (e.g. SWOOGLE iii , OntoSearch iv) and several other semantic search technologies have been proposed (e.g. Semantic Portals (Zhang et al, 2005), Semantic Wikis (Völkel et al, 2006), multi-agent P2P ontology-based semantic routing (of queries) systems (Tamma et al, 2004), and ontology mapping-based query/answering systems (Lopez et al, 2006; Kotis & Vouros, 2006, Bouquet et al, 2004). Within these technologies, queries can be placed as formally KEY TERMS AND THEIR DEFINITIONS Keyword-based Web search: Searching for Web documents that their content can be lexically matched (string similarity) with the content of a NL query Semantic Search: Searching for SWDs with content that can be semantically matched (semantic similarity) with the content of a formal query Semantic Web Document: Is a document represented as an RDF graph, written in RDF/XML syntax, allowing its content to be processable from machines. An ontology can be a special type of SWD (written in OWL syntax) that extends the semantics of RDF(S) language. Query Ontology: An ontology automatically created by the mapping of NL query terms to a lexicon. The aim is to check its similarity against SWDs using ontology mapping techniques.

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A modular approach to document indexing and semantic search
Krishnaprasad Thirunarayan

2005

This paper develops a modular approach to improving effectiveness of searching documents for information by reusing and integrating mature software components such as Lucene APIs, WORDNET, LSA techniques, and domain-specific controlled vocabulary. To evaluate the practical benefits, the prototype was used to query MEDLINE database, and to locate domain-specific controlled vocabulary terms in Materials and Process Specifications. Its extensibility has been demonstrated by incorporating a spell-checker for the input query, and by structuring the retrieved output into hierarchical collections for quicker assimilation. It is also being used to experimentally explore the relationship between LSA and document clustering using 20-mini-newsgroups and Reuters data. In future, this prototype will be used as experimental testbed for expressive, context-aware and scalable searches.

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Bridging THE Gap: From Traditional Information Retrieval TO THE Semantic Web
Morgan Benton

AMCIS 2002 Proceedings, 2002

Web is the nature of information search. The Semantic Web vision reveals a radical departure from the traditional theories of Information Retrieval (IR) upon which current search engine technology is built. Semantic Web researchers are very articulate about how the pillars of the Semantic Web-semantically aware, intelligent agents, ontologies, and markup languages-will revolutionize the way that we interact with information on the web. They are less articulate about how we will get there from here. While it's true that the traditional assumptions of IR-small, static, homogeneous, centrally located, monolingual document collections-don't hold for the Web, still it is important to note the success of search engines built on IR theory. This paper calls attention to the gap between traditional IR and the more visionary Semantic Web research. We describe a preliminary roadmap bridging the two areas focusing on the concrete contributions and also calling attention to the weak points of both fields.

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Using semantic components to search for domain-specific documents: An evaluation from the system perspective and the user perspective
Peter Vedsted

Information Systems, 2009

We seek to leverage an expert user's knowledge about how information is organized in a domain and how information is presented in typical documents within a particular domain-specific collection, to effectively and efficiently meet the expert's targeted information needs. We have developed the semantic components model to describe important semantic content within documents. The semantic components model for a given collection (based on a general understanding of the type of information needs expected) consists of a set of document classes, where each class has an associated set of semantic components. Each semantic component instance consists of segments of text about a particular aspect of the main topic of the document and may not correspond to structural elements in the document. The semantic components model represents document content in a manner that is complementary to full text and keyword indexing. This paper describes how the semantic components model can be used to improve an information retrieval system. We present experimental evidence from a large interactive searching study that compared the use of semantic components in a system with full text and keyword indexing, where we extended the query language to allow users to search using semantic components, to a base system that did not have semantic components. We evaluate the systems from a system perspective, where semantic components were shown to improve document ranking for precision-oriented searches, and from a user perspective. We also evaluate the systems from a sessionbased perspective, evaluating not only the results of individual queries but also the results of multiple queries during a single interactive query session.

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Karbala International Journal of Modern Science, 2019

Traditional search mechanisms are based on the keyword search, which does not consider the semantic links between different concepts. This leads to the loss of relevant documents due to inaccurate query formulation or using contextually close words and concepts in the query. To solve the problems of formulating user queries and interdisciplinarity of concepts, it is suggested to use semantic search. The proposed method for implementing semantic search is applicable to large scopes of text data and is based on using a genetic algorithm. Unlike standard methods for information search, the suggested method allows us to consider the semantics of interrelationships between concepts and to handle interdisciplinary concepts correctly. By the aid of semantic tagging, documents contain concepts that are not present in the user's initial query but are semantically close to the requested concepts. Semantic tagging is performed for each document separately, which provides parallel tagging in several subject areas. By the time of the document ontological profile formation is completed, all semantic distances between pairs of distinguished concepts are calculated. Concepts are considered contextually close if their semantic proximity value is above a certain threshold value that is specified in the search parameters. Building a document ontological profile is a multicriteria task, since it depends on a lot of characteristics, so genetic algorithms can be used to solve it effectively. The developed genetic algorithm is intended for more accurate distribution of weight coefficients and estimation of semantic proximity of concepts.

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Hybrid Search: Effectively Combining Keywords and Semantic Searches
Ravish Bhagdev, Daniela Petrelli

2008

This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontology-based search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines.

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Integrating Keywords and Semantics on Document Annotation and Search
Giorgos Giannopoulos, Nikos Bikakis

9th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE '10)

This paper describes GoNTogle, a framework for document annotation and retrieval, built on top of Semantic Web and IR technologies. GoNTogle supports ontology-based annotation for documents of several formats, in a fully collaborative environment. It provides both manual and automatic annotation mechanisms. Automatic annotation is based on a learning method that exploits user annotation history and textual information to automatically suggest annotations for new documents. GoNTogle also provides search facilities beyond the traditional keyword-based search. A flexible combination of keyword-based and semantic-based search over documents is proposed in conjunction with advanced ontology-based search operations. The proposed methods are implemented in a fully functional tool and their effectiveness is experimentally validated.

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Web-Based Information Search System Development Using a Semantic Network
Primardiana Wijayati

2021

Finding information from a large collection of documents is a complicated task; therefore, we need a method called an information retrieval system. Several models that have been used in information retrieval systems include the Vector Space Model (VSM), DICE Similarity, Latent Semantic Indexing (LSI), Generalized Vector Space Model (GVSM), and semantic-based information retrieval systems. The purpose of this study was to develop a semantic network-based search system that will find information based on keywords and the semantic relationship of keywords provided by users. This cannot be done by most search systems that only work based on keyword matching or similarities. The Waterfall development model was used, which divides the development stages into five steps, namely: (1) requirements analysis and definition; (2) system and software design; (3) implementation and unit testing; (4) integration and system testing; and (5) operation and maintenance. The developed system/application...

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