TOWARDS AUTOMATIC QUERY EXPANSION AND GRAPH NAVIGATION BASED RELEVANCE FEEDBACK IN OPEN SEARCH SETTINGS

Users of retrieval systems usually enter short queries which seem relevant to their topic; however, they might skip relevant keywords or use synonyms which are not prevalent in the data [1]. Moreover, when researching a new topic, they might want to get a better overview of available documents and the connections between them based on their query instead of an ordered list of results. Additionally, users exploring new topics might benefit from relevance feedback mechanisms to acquire more pertinent results. To improve the described situation, this work introduces an enhanced information retrieval (IR) process, which combines automatic query expansion (AQE) and graph navigation to mitigate the aforementioned issues. The described solution will be implemented as part of the Collaboration Spotting Cite (CSC) platform [2]. Collaboration Spotting (CS) is a platform developed at CERN to provide an environment for exploration of large datasets of connected data [3]. CS provides a multitude of supportive IR and data exploration features which could be adapted and used in open search settings. A version of CS called CSC was developed to provide an environment for exploring scientific publications and patents by combining IR, and Graph Analytics approaches [2]. The CSC retrieval process is initiated when a user performs a search by entering a query or selecting a document as the search query. The relevant documents are then selected using a ranking algorithm and returned as a ranked list. Documents are transformed into a graph using a predefined schema which defines what document metadata is used and how it is connected to the document node in the graph [2]. Before displaying the graph, a community detection algorithm is used to identify and mark communities for easier user navigation. Users can navigate the graph by switching between predefined facets or selecting a subset of the graph for further inspection. They can also perform relevance feedback each time they select a subset of a graph which will trigger a new search based on the current selection of documents. New documents will be added to the graph based on semantic similarity measures. Additionally, multiple search graphs can be combined for comparison. Even though the CSC retrieval process enables users to increase pertinence of data in many ways, it does not address a common issue of retrieval systems. When performing an initial search, users might enter short queries with keywords which are not as prominent in the data [2]. This can lead to irrelevant retrieval results and unnecessary interactions which consume the users time. In this work, we will attempt to help users avoid the aforementioned obstacles by expanding the CSC retrieval process with AQE [1]. Thus, we will perform a cooccurrence method based on the subset of retrieved documents or a selected subgraph of the presentation and rerun the search. This allows users to extend the search results as well getting and extending graph visualisation for further inspection and refinement. Hence, we expect that the effects of potentially using irrelevant terms for query expansion are reduced by the community detection algorithm, which groups documents and enables users to identify relevant results quickly. To investigate how AQE affects the initial retrieval process, we will evaluate the expanded retrieval process using standardised quantitative retrieval performance measurements. Additionally, we will perform an evaluation study which examines how many steps users perform to find a set of predefined documents. This will enable us to evaluate the CSC relevance feedback mechanism with and without the use of AQE. The CSC retrieval approach can be generalised to more heterogeneous datasets such as the future index developed by the Open Search Foundation. For example, the graph navigation approach could be used as an intermediate search step in the open search context. It would enable users to quickly identify documents which are most relevant for them and further expand their results by using the aforementioned relevance feedback mechanisms. As a future direction, we want to investigate and exploit the underlying graph structures of text; for example, by observing and matching patterns which occur in graphs.