Ontology Based Semantic Web Information Retrieval Enhancing Search Significance

The web contain huge amount of structured as well as unstructured data/information. This varying nature of data may yield a retrieval response that is expected to contain relevant response that is expected to contain relevant as well as irrelevant data while directing search. In order to filter out irrelevance in the search result, numerous methodologies have been used to extract more and more relevant search responses in retrieval. This work has adopted semantic search dealing directly with the knowledge base. The approach incorporates Query pattern evolution and semantic keyword matching with final detail to enhance significance of relevant data retrieval. The proposed method is implemented in open source computing tool environment and the result obtained thereof are compared with that of earlier used methodologies

A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance

QUERY PARADIGM TO EXTRACT MOST RELEVANT OBJECTS BY GIVEN SET OF TAGS

Nonetheless, in lots of application scenarios, users cannot precisely formulate their keywords and rather choose to choose them from some candidate keyword sets. Existing studies mainly focus regarding how to efficiently discover the top-k result set given a spatio-textual query. Spatio-textual queries retrieve probably the most similar objects regarding confirmed location along with a keyword set. Driven by these applications, we advise a manuscript query paradigm, namely reverse keyword look for spatio-textual top-k queries (RST Q). Furthermore, in information browsing applications, it's helpful to focus on the objects using the tags to which the objects have higher rankings. It returns the keywords to which a target object is a spatio-textual top-k result. Extensive experimental evaluation demonstrates the efficiency in our suggested query techniques when it comes to both computational cost and that to cost. By being able to access our prime-level nodes of KcR-tree, we are able to estimate the rankings from the target object without being able to access the particular objects. To efficiently process the brand new query, we devise a manuscript hybrid index KcR-tree to keep and summarize the spatial and textual information of objects

Accelerating Spatio-Textual Queries with Learned Indices

Efficiently computing spatio-textual queries has become increasingly important in various applications that need to quickly retrieve geolocated entities associated with textual information, such as in location-based services and social networks. To accelerate such queries, several works have proposed combining spatial and textual indices into hybrid index structures. Recently, the novel idea of replacing traditional indices with ML models has attracted a lot of attention. This includes works on learned spatial indices, where the main challenge is to address the lack of a total ordering among objects in a multidimensional space. In this work, we investigate how to extend this novel type of index design to the case of spatio-textual data. We study different design choices, based on either loose or tight coupling between the spatial and textual part, as well as a hybrid index that combines a traditional and a learned component. We also perform an experimental evaluation using several real-world datasets to assess the potential benefits of using a learned index for evaluating spatio-textual queries

Textually Relevant Spatial Skylines

postprin

Spatiotemporal information extraction from a historic expedition gazetteer

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volume historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners’ site visits (which took place between 1910 and 1926) and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework