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By Krause E. (ed.)

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X ]) QUERY: getDocsByBindingsAndContent: Single-Var-Triples-list x PowerSet(STRINGS) -> PowerSet(DN) ABBREVIATION FOR: getDocsByBindingsAndContent(vtl, kws) = getBindings (vtl) ∧ getDocsByKeywords(kws) where vtl ∈ Single-Var-Triples-list, kws ∈ PowerSet(STRINGS). SEMANTICS: This query retrieves document nodes that match the keywords and contain the matching triples. x] “Semantic Grid”) 5 Implementation and Results We have implemented an Apache Lucene [11] based retrieval system called SITAR (Semantic InformaTion Analysis and Retrieval system) based upon our model [6].

Data Retrieval techniques are typically used to retrieve data from the Semantic Web while Information Retrieval techniques are used to retrieve documents from the Hypertext Web. We present a Unified Web model that integrates the two webs and formalizes connection between them. We then present an approach to retrieving documents and data that captures best of both the worlds. Specifically, it improves recall for legacy documents and provides keyword-based search capability for the Semantic Web. We specify the Hybrid Query Language that embodies this approach, and the prototype system SITAR that implements it.

Id_db=20 QUERY: Nodes-ref::=ss, where ss ∈ PowerSet(STRINGS). ANSWER: Result(ss) = { n in N | ss ⊆ IndexWords(n) } SEMANTICS: The wordset query, ss, usually written as a set of strings delimited using angular brackets, returns the set of nodes whose IndexWords contain ss. EXAMPLE: 34 K. Thirunarayan and T. Immaneni Nodes-ref ::= pp::ss, where pp, ss ∈ PowerSet(STRINGS). Result(pp::ss) = { n ∈ N | ss ⊆ IndexWords(n) ∧ ∃m : n ISA m ∧ pp ⊆ IndexWords(m) } SEMANTICS: The wordset-pair query, pp::ss, usually written as two wordsets delimited using colon, returns the set of nodes such that each node has IndexWords that contains ss and has an ISA ancestor whose IndexWords contains pp.

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