Zenz, G.; Zhou, X.; Minack, E.; Siberski, W.; Nejdl, W.: Interactive query construction for keyword search on the Semantic Web (2012)
0.01
0.0058368337 = product of:
0.014592084 = sum of:
0.009010308 = weight(_text_:a in 430) [ClassicSimilarity], result of:
0.009010308 = score(doc=430,freq=14.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.1685276 = fieldWeight in 430, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=430)
0.0055817757 = product of:
0.011163551 = sum of:
0.011163551 = weight(_text_:information in 430) [ClassicSimilarity], result of:
0.011163551 = score(doc=430,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.13714671 = fieldWeight in 430, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=430)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- With the advance of the semantic Web, increasing amounts of data are available in a structured and machine-understandable form. This opens opportunities for users to employ semantic queries instead of simple keyword-based ones to accurately express the information need. However, constructing semantic queries is a demanding task for human users [11]. To compose a valid semantic query, a user has to (1) master a query language (e.g., SPARQL) and (2) acquire sufficient knowledge about the ontology or the schema of the data source. While there are systems which support this task with visual tools [21, 26] or natural language interfaces [3, 13, 14, 18], the process of query construction can still be complex and time consuming. According to [24], users prefer keyword search, and struggle with the construction of semantic queries although being supported with a natural language interface. Several keyword search approaches have already been proposed to ease information seeking on semantic data [16, 32, 35] or databases [1, 31]. However, keyword queries lack the expressivity to precisely describe the user's intent. As a result, ranking can at best put query intentions of the majority on top, making it impossible to take the intentions of all users into consideration.
