Diversified query expansion using conceptnet

Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

“…Table 1 summarizes the various DQE methods in literature. Drawing inspiration from recent interest in linking text with knowledge-base entities (notably, since ESA [13]), BHN [2] proposes to choose expansion terms from the names of entities in the ConceptNet ontology, thus generating expansion terms that are focused on entities. BLN [3] extends BHN to use Wikipedia and query logs in addition to ConceptNet; the Wikipedia part relies on being able to associate the query with one or more Wikipedia pages, and uses entity names and representative terms as candidate expansion terms from Wikipedia.…”

“…This work uses Wikipedia documents, differently weighted by the structure of Wikipedia documents, in a pseudorelevance feedback framework; it may be particularly noted that, unlike the approaches discussed so far, this work does not address the diversity factor. DQE Uptake Model: The suggested uptake model for DQE as used in most methods (e.g., [2]) is that the original search query (e.g., python) be appended with all the terms 5 in the result (e.g., language, monty) to form a single large query that is expected to produce a result set encompassing multiple aspects. While this may be a good model for search engines that work on a small corpus, we observe that such extended queries are not likely to be of high utility for large-scale search engines.…”

“…In our exploration, we could not find a result relating to Monty Python among the first five pages of search results for python on Bing. Such difficulties in covering long tail aspects, as noted in [2], led to research interest in a slightly different task attacking the same larger goal, that of Diversified Query Expansion (DQE). Note that techniques to ensure coverage of diverse aspects among the top results are relevant for apparently unambiguous queries too, though the need is more pronounced in inherently ambiguous ones.…”

“…For our python example, desirable top DQE expansion terms would include those relating to the programming language aspect such as language and programming as well as those relating to the reptile-aspect such as pythonidae and reptile. In existing work, the extension terms have been identified from sources such as corpus documents [26], query logs [17], external ontologies [2,3] or the results of the initial query [26]. The aspect-affinity of each term is modeled either explicitly [26,17] or implicitly [2] followed by selection of a subset of candidate words using the Maximum Marginal Relevance (MMR) principle [5].…”

“…In existing work, the extension terms have been identified from sources such as corpus documents [26], query logs [17], external ontologies [2,3] or the results of the initial query [26]. The aspect-affinity of each term is modeled either explicitly [26,17] or implicitly [2] followed by selection of a subset of candidate words using the Maximum Marginal Relevance (MMR) principle [5]. This ensures that terms related to many aspects find a place in the extended set.…”