Semantic search on the Web

Fazzinga, Bettina; Lukasiewicz, Thomas

doi:10.3233/sw-2010-0023

Cited by 34 publications

(18 citation statements)

References 25 publications

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“…The approach in (Fazzinga et al, 2010) combines standard Web search queries with ontological search queries. It assumes that Web pages are enriched with annotations that have unique identifiers and are relative to an underlying ontology.…”

Section: Evolution Of Semantic Search On the Web Of Datamentioning

confidence: 99%

“…We are quickly reaching the critical mass required to enable a true vision of a large scale, distributed SW with real-world datasets, leading to new research possibilities that can benefit from exploiting and reusing this vast resources, unprecedented in the history of computer science. Hence, there is now a renewed interest in the search engine market towards the introduction of semantics in order to improve over current keyword search technologies (Fazzinga et al, 2010) (Hendler, 2010) (Baeza et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The notion of introducing semantics to search on the Web is not understood in a unique way. According to (Fazzinga et al, 2010) the two most common uses of SW technology are: (1) to interpret Web queries and Web resources annotated with respect to the background knowledge described by underlying ontologies, and (2) to search in the structured large datasets and Knowledge Bases (KBs) of the SW as an alternative or a complement to the current web.…”

Section: Introductionmentioning

confidence: 99%

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Is Question Answering fit for the Semantic Web?: A survey

et al. 2011

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With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources.

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Section: Evolution Of Semantic Search On the Web Of Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Is Question Answering fit for the Semantic Web?: A survey

et al. 2011

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“…Web Search has changed radically with the advent of Semantic Web [1]. The Semantic Web technology helps in better understanding of users' information need and more complex queries by interpreting Web search queries and resources relative to one or more underlying ontologies, describing some background domain knowledge, in particular, by connecting the Web resources to semantic annotations, or by extracting semantic knowledge from Web resources [2]. However, for naïve users with no familiarity with such technologies, accessing the information in need is not a trivial job and keyword query is still the primary way.…”

Section: Introductionmentioning

confidence: 99%

“…Adding semantics to keyword query is not a new topic and there are many literatures in this aspect. Fazzinga and Lukasiewicz give a comprehensive overview of state-of-the-art approaches [2]. Generally speaking, these approaches employ the Semantic Web technologies to augment or refine the results of traditional keyword search with the help of knowledgebase outside, such as domain-specific ontologies, Wiki, Wordnet, and etc [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Semantic Keyword Expansion: A Logical Approach

Chen

2012

Intelligent Information Processing VI

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Keyword search is the primary way for ordinary users to access the Web content. It is essentially a syntax match between users' key-ins and the index structure of information systems with a relevance-ranking way to sort the hitted documents. The syntax match can rarely satisfy users' information need when the keywords and index are not syntically similar while share a lot semantically. This paper proposed a way of semantic keyword expansion with a re-ranking to handle this problem. Experimental results show that our method helps in improving the quality of keyword search and particularly in the cases of keywords with widely-used synonyms or parasynonyms.Keywords: keyword search, semantic expansion, probabilistic uncertainty, re-rank.S e m a n t i c Keyword Expansion: A Logical Approach 117which is used to expand the user-formulated keyword to some highly-overlapped keywords. With the original and the expanded keywords, our approach performs traditional keyword search and re-ranks these results to get the overall result.Paper Outline. We first give a brief overview of the formalism in Sec.2, including its syntax (Sec.2.1), semantics (Sec.2.2) and some reasoning tasks (Sec.2.3). Sec.3 details our semantic keyword expansion with experimental results. Finally, we conclude the paper in Sec.4. The FormalismPD_ALCO@ may be seemed as a PDL [8]-like extension of description logics with probabilistic uncertainty admitting dynamic reasoning under probability uncertainty. It employs conditional constraints [9] to express interval restrictions for conditional probabilities over concepts, and lexicographic entailment for probabilistic reasoning. 2.

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