5,572 research outputs found
Identifying meaningful return information for XML keyword search
Keyword search enables web users to easily access XML data with-out the need to learn a structured query language and to study pos-sibly complex data schemas. Existing work has addressed the prob-lem of selecting qualied data nodes that match keywords and con-necting them in a meaningful way, in the spirit of inferring a where clause in XQuery. However, how to infer the return clause for key-word search is an open problem. To address this challenge, we present an XML keyword search en-gine, XSeek, to infer the semantics of the search and identify return nodes effectively. XSeek recognizes possible entities and attributes inherently represented in the data. It also distinguishes betwee
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Finding Patterns in a Knowledge Base using Keywords to Compose Table Answers
We aim to provide table answers to keyword queries against knowledge bases.
For queries referring to multiple entities, like "Washington cities population"
and "Mel Gibson movies", it is better to represent each relevant answer as a
table which aggregates a set of entities or entity-joins within the same table
scheme or pattern. In this paper, we study how to find highly relevant patterns
in a knowledge base for user-given keyword queries to compose table answers. A
knowledge base can be modeled as a directed graph called knowledge graph, where
nodes represent entities in the knowledge base and edges represent the
relationships among them. Each node/edge is labeled with type and text. A
pattern is an aggregation of subtrees which contain all keywords in the texts
and have the same structure and types on node/edges. We propose efficient
algorithms to find patterns that are relevant to the query for a class of
scoring functions. We show the hardness of the problem in theory, and propose
path-based indexes that are affordable in memory. Two query-processing
algorithms are proposed: one is fast in practice for small queries (with small
patterns as answers) by utilizing the indexes; and the other one is better in
theory, with running time linear in the sizes of indexes and answers, which can
handle large queries better. We also conduct extensive experimental study to
compare our approaches with a naive adaption of known techniques.Comment: VLDB 201
Efficient XML Keyword Search based on DAG-Compression
In contrast to XML query languages as e.g. XPath which require knowledge on
the query language as well as on the document structure, keyword search is open
to anybody. As the size of XML sources grows rapidly, the need for efficient
search indices on XML data that support keyword search increases. In this
paper, we present an approach of XML keyword search which is based on the DAG
of the XML data, where repeated substructures are considered only once, and
therefore, have to be searched only once. As our performance evaluation shows,
this DAG-based extension of the set intersection search algorithm[1], [2], can
lead to search times that are on large documents more than twice as fast as the
search times of the XML-based approach. Additionally, we utilize a smaller
index, i.e., we consume less main memory to compute the results
Extending Yioop! With Geographical Location Local Search
It is often useful when doing an internet search to get results based on our current location. For example, we might want such results when we search on restaurants, car service center, or hospitals. Current open source search engines like those based on Nutch do not provide this facility. Commercial engines like Google and Yahoo! provide this facility so it would be useful to incorporate it in an open source alternative. The goal of this project is to include location aware search in Yioop!(Pollett, 2012) by using geographical data from OpenStreetMap(“Open Street map wiki”, 2012) and hostip.info (“DMOZ”, n.d.) database to geolocate IP addresses
Towards improving web service repositories through semantic web techniques
The success of the Web services technology has brought topicsas software reuse and discovery once again on the agenda of software engineers. While there are several efforts towards automating Web service discovery and composition, many developers still search for services
via online Web service repositories and then combine them manually. However, from our analysis of these repositories, it yields that, unlike traditional software libraries, they rely on little metadata to support
service discovery. We believe that the major cause is the difficulty of automatically deriving metadata that would describe rapidly changing Web service collections. In this paper, we discuss the major shortcomings of state of the art Web service repositories and, as a solution, we
report on ongoing work and ideas on how to use techniques developed in the context of the Semantic Web (ontology learning, mapping, metadata based presentation) to improve the current situation
XKMis: Effective and efficient keyword search in XML databases
We present XKMis, a system for keyword search in xml documents. Unlike previous work, our method is not based on the lowest common ancestor (LCA) or its variant, rather we divide the nodes into meaningful and self-containing information segments, called minimal information segments (MISs), and return MIS-subtrees which consist of MISs that are logically connected by the keywords. The MIS-subtrees are closer to what the user wants. The MIS-subtrees enable us to use the region code of xml trees to develop an algorithm for the search which is more efficient especially for large xml trees. We report our experiment results, which verify the better effectiveness and efficiency of our system. Copyright ©2009 ACM
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