## 4 The Use of Personalized Ontologies for Re-ranking Search Results
### Future Directions for Re-ranking Based on Personalized Ontologies  
In the presented approaches to re-rank search results, the trend line is to adopt an external reference knowledge as a semantic support to define an ontological user profile based on the concepts extracted from the relevant Web pages. The ODP is the knowledge used for this aim. Although the ODP is constantly updated and maintained by a community of volunteer editors, the subsumption relation (_i.e._, the taxonomic IS-A hierarchy) between categories is the only semantic information which is provided. A further step could be to use a real general purpose ontology as reference knowledge where a richer semantic expressiveness is given. YAGO is a large ontology, automatically obtained from Wikipedia and WordNet with high coverage and precision [72, 73]. YAGO has been defined in the last two years and could be a good candidate as a support to personalized searches. Currently YAGO contains more than 1.7 million entities and 15 million facts, grouped by 99 relations. In YAGO a fact is an instantiated relation between entities. Among the several relations, the IS-A is included as well as additional semantic relations between entities. In the literature, some works that make use of YAGO are proposed. For example, in [74], YAGO is used in order to classify the search results into appropriate categories. The goal of this approach is not to re-rank the search results according to the user profile, but given a user's query, the goal is to provide all the categories for the corresponding search results. This way a user is helped in locating the Web results by navigating the obtained categories' list.  
A promising research direction can address the use of NAGA [75, 76]. NAGA is a new semantic search engine, and it can operate on knowledge bases organized as graphs. NAGA searches the sub-graphs that match the user's query in the knowledge base, and these sub-graphs are the results obtained by the user as response. NAGA's goal is to rank the retrieved answers in such way that the most important answers are ranked before. YAGO is the knowledge used to perform preliminary experiments in order to compare the quality of the search results provided by NAGA with the ones obtained by a search engine, such as Google, and the question answering systems, such as Yahoo! Answers [77] and START [78]. In [79] the authors have proposed an approach to personalize the user's search in NAGA. The objective is to establish a user profile as several sub-graphs into NAGA and to define a personalized scoring model into the NAGA's ranking function that makes use of the user's interests. A user's profile is directly elicitedby the user himself/herself by querying YAGO's knowledge with NAGA for discovering his/her interests from the obtained results (_i.e._, the related sub-graphs). To facilitate this phase, an ad-hoc interface has been developed, but this limits the interoperability of the approach where a user can interact with the system only by formulating queries with an appropriate interface and a specific query language. As previously stated, YAGO has a richer semantic than the ODP, but YAGO is a static general purpose ontology, whereas ODP is always updated from the community of volunteers. Future investigations can be made in two directions: (1) to define a user profile based on YAGO, and (2) to study new strategies for updating YAGO's knowledge.