Discovering the semantics of user keywords

Journal of Web Semantics, 2011

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontologybased Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.

The continued growth of the World Wide Web has made the retrieval of relevant information for a user's query increasingly difficult. A major obstacle to more accurate and semantically sound retrieval is the inability of web search systems to incorporate context in the retrieval process. This research presents a methodology to increase the semantic content of web query results by building context-aware queries. The methodology contains mechanisms that use two knowledge sources to augment a query: lexicons such as WordNet and ontologies such as the DAML library. A semantic net representation facilitates the process. The methodology has been implemented in a research prototype that can connect to publicly available search engines to execute the augmented query. An empirical test of the methodology and comparison of results against those directly obtained from the search engines shows that the proposed methodology provides more relevant results to users. The results demonstrate that lexicons and ontologies act as complementary sources of knowledge to construct the context necessary for obtaining more relevant results.

2008

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present an approach to Semantic Web search, which combines standard Web search with ontological background knowledge. In fact, we show how standard Web search engines can be used as the main inference motor for ontology-based search. To make this possible, lightweight software clients are used for annotation and query decomposition. We develop the formal model behind this approach and also provide an implementation in desktop search. Experiments show that the implementation scales quite well to very large amounts of data.

Advances in Science, Technology and Engineering Systems Journal

Day by day, the data on the web becomes very huge which makes it difficult to find relevant information. Search engines are one of the successful factors that can retrieve information from the Web. The process of seeking information by search engines helps users find information on the internet, however it is not an easy task to find the exact information from this massive data available on the Web. Semantic Web technology has an ability to focus on metadata rather than syntax, which made the semantic search engines to search for the meaning of keywords instead of the keyword syntax. Consequently, an effective role of performance in conventional search engines can be achieved by rising the accuracy of information returned by a search query. In this paper, a survey for syntactic-based search engines and semantic-based search engines are studied, a comprehensive comparison between the two is presented, finally, their technologies are compared and discussed.

Information Systems, 2011

Nowadays, people frequently use different keyword-based web search engines to find the information they need on the Web. However, many words are polysemous and, when these words are used to query a search engine, its output usually includes links to web pages referring to their different meanings. Besides, results with different meanings are mixed up, which makes the task of finding the relevant information difficult for the users, especially if the user-intended meanings behind the input keywords are not among the most popular on the Web.

2008

While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web . Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.

Artificial Neural Networks–ICANN 2006, 2006

This paper proposes an information system, which aims to bridge the semantic gap in web search. The system uses multiple domain ontological structures expanding the user's query with semantically related concepts, enhancing in parallel the quality of retrieval to a large extend. Query analyzers broaden the user's information needs from classical term-based to conceptually representations, using knowledge from relevant ontologies and theirs' properties. Besides the use of semantics, the system employs machine learning ...

Proceedings of the 6th international conference on Web engineering - ICWE '06, 2006

The lack of explicit semantics in the current Web can lead to ambiguity problems: for example, current search engines return unwanted information since they do not take into account the exact meaning given by user to the keywords used. Though disambiguation is a very well-known problem in Natural Language Processing and other domains, traditional methods are not flexible enough to work in a Webbased context. In this paper we have identified some desirable properties that a Web-oriented disambiguation method should fulfill, and make a proposal according to them. The proposed method processes a set of related keywords in order to discover and extract their implicit semantics, obtaining their most suitable senses according to their context. The possible senses are extracted from the knowledge represented by a pool of ontologies available in the Web. This method applies an iterative disambiguation algorithm that uses a semantic relatedness measure based on Google frequencies. Our proposal makes explicit the semantics of keywords by means of ontology terms; this information can be used for different purposes, such as improving the search and retrieval of underlying relevant information.

2011

Keyword search suffers from a number of issues: ambiguity, synonymy, and an inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution. In this paper we describe and evaluate HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. For displaying results, HyKSS uses a dynamic ranking algorithm. We show that over data sets of short topical documents, the HyKSS ranking algorithm outperforms both keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid approaches to ranking. 1 Introduction Keyword search for documents on the web works well-often surprisingly well. Can semantic search, added to keyword search, make the search for relevant documents even better? Clearly, the answer should be yes, and researchers are pursuing this initiative (e.g., [1]). The real question, however, is not whether adding semantic search might help, but rather how can we, in a cost-effective way, identify the semantics both in documents in the search space and in the free-form queries users wish to ask. Keyword search has a number of limitations: (1) Polysemy: Ambiguous keywords may result in the retrieval of irrelevant documents. (2) Synonymy: Document publishers may use words that are synonymous with, but not identical to, terms in user queries causing relevant documents to be missed. (3) Constraint satisfaction: Keyword search is incapable of recognizing semantic constraints. If a query specifies "Hondas for under 12 grand", a keyword search will treat each word as a keyword (or stopword) despite the fact that many, if not most, relevant documents likely do not contain any of these words-not even "Hondas" since the plural is relatively rare in relevant documents. Semantic search can resolve polysemy by placing words in context, synonymy by allowing for alternatives, and constraint satisfaction by recognizing specified conditions. Thus, for example, semantic search can interpret the query "Hondas

2015

As the amount of data in the World Wide Web grows, the Internet becomes the biggest and often the primary information resource for many individuals and organizations. To make good use of the data, it is essential to provide effective and intelligent search capabilities. The users more and more often require direct and unambiguous answers, which are often not explicitly present in any document. The Web users also are not willing to learn complex query languages and need interfaces that will be easy to use and as close as possible to natural language. The aim of this paper is to illustrate the advantages that semantics brings to the users of contemporary Web search engines. The concepts of semantic Web and semantic search engines have been described along with some issues related to their development and usage, such as linked data, semantic query interfaces and the ways of publishing semantic data. The authors also explore the semantic features of contemporary search engines and indic...

The continued growth of the World Wide Web has made the retrieval of relevant information for a user's query increasingly difficult. A major obstacle to more accurate and semantically sound retrieval is the lack of intelligence in web search systems. This research presents a methodology to increase the semantic content of web query results by building context-aware queries. The methodology contains heuristic mechanisms that use lexical sources such as WordNet and ontologies such as the DAML library to augment a query. A semantic net representation facilitates the process. The methodology has been implemented in a research prototype that connects to search engines (Google and AlltheWeb) to execute the augmented query. An empirical test of the methodology and comparison of results against those directly obtained from the search engines demonstrates that the proposed methodology provides more relevant results to users.

We all are aware of two letter word named Information Retrieval (IR) which is nothing but a process of retrieving or gathering information from a given document or a file. The concept of Information Retrieval has gained much height for many years because of large collection of information that is available in form of documents on Internet and to arrange and retrieve utilized words from them is cumbersome task. The information can be structured, unstructured or semi-structured. This paper consists of four sections. In section 1,