6,359 research outputs found
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Semantic Query Reformulation in Social PDMS
We consider social peer-to-peer data management systems (PDMS), where each
peer maintains both semantic mappings between its schema and some
acquaintances, and social links with peer friends. In this context,
reformulating a query from a peer's schema into other peer's schemas is a hard
problem, as it may generate as many rewritings as the set of mappings from that
peer to the outside and transitively on, by eventually traversing the entire
network. However, not all the obtained rewritings are relevant to a given
query. In this paper, we address this problem by inspecting semantic mappings
and social links to find only relevant rewritings. We propose a new notion of
'relevance' of a query with respect to a mapping, and, based on this notion, a
new semantic query reformulation approach for social PDMS, which achieves great
accuracy and flexibility. To find rapidly the most interesting mappings, we
combine several techniques: (i) social links are expressed as FOAF (Friend of a
Friend) links to characterize peer's friendship and compact mapping summaries
are used to obtain mapping descriptions; (ii) local semantic views are special
views that contain information about external mappings; and (iii) gossiping
techniques improve the search of relevant mappings. Our experimental
evaluation, based on a prototype on top of PeerSim and a simulated network
demonstrate that our solution yields greater recall, compared to traditional
query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM
View Selection in Semantic Web Databases
We consider the setting of a Semantic Web database, containing both explicit
data encoded in RDF triples, and implicit data, implied by the RDF semantics.
Based on a query workload, we address the problem of selecting a set of views
to be materialized in the database, minimizing a combination of query
processing, view storage, and view maintenance costs. Starting from an existing
relational view selection method, we devise new algorithms for recommending
view sets, and show that they scale significantly beyond the existing
relational ones when adapted to the RDF context. To account for implicit
triples in query answers, we propose a novel RDF query reformulation algorithm
and an innovative way of incorporating it into view selection in order to avoid
a combinatorial explosion in the complexity of the selection process. The
interest of our techniques is demonstrated through a set of experiments.Comment: VLDB201
On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark
Querying very large RDF data sets in an efficient manner requires a
sophisticated distribution strategy. Several innovative solutions have recently
been proposed for optimizing data distribution with predefined query workloads.
This paper presents an in-depth analysis and experimental comparison of five
representative and complementary distribution approaches. For achieving fair
experimental results, we are using Apache Spark as a common parallel computing
framework by rewriting the concerned algorithms using the Spark API. Spark
provides guarantees in terms of fault tolerance, high availability and
scalability which are essential in such systems. Our different implementations
aim to highlight the fundamental implementation-independent characteristics of
each approach in terms of data preparation, load balancing, data replication
and to some extent to query answering cost and performance. The presented
measures are obtained by testing each system on one synthetic and one
real-world data set over query workloads with differing characteristics and
different partitioning constraints.Comment: 16 pages, 3 figure
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