Search Engines going beyond Keyword Search: A Survey
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paper_title: Searching a file system using inferred semantic links

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We describe Eureka, a file system search engine that takes into account the inherent relationships among files in order to improve the rankings of search results. The key idea is to automatically infer semantic links within the file system, and use the structure of the links to determine the importance of different files and essentially bias the result rankings. We discuss the inference of semantic links and describe the design of the Eureka search engine.
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paper_title: A search engine for 3D models

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As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245p better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).
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paper_title: Avatar semantic search: a database approach to information retrieval

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We present Avatar Semantic Search, a prototype search engine that exploits annotations in the context of classical keyword search. The process of annotations is accomplished offline by using high-precision information extraction techniques to extract facts, con-cepts, and relationships from text. These facts and concepts are represented and indexed in a structured data store. At runtime, keyword queries are interpreted in the context of these extracted facts and converted into one or more precise queries over the structured store. In this demonstration we describe the overall architecture of the Avatar Semantic Search engine. We also demonstrate the superiority of the AVATAR approach over traditional keyword search engines using Enron email data set and a blog corpus.
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paper_title: An interactive agent-based system for concept-based web search

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Abstract Search engines are useful tools in looking for information from the Internet. However, due to the difficulties of specifying appropriate queries and the problems of keyword-based similarity ranking presently encountered by search engines, general users are still not satisfied with the results retrieved. To remedy the above difficulties and problems, in this paper we present a multi-agent framework in which an interactive approach is proposed to iteratively collect a user's feedback from the pages he has identified. By analyzing the pages gathered, the system can then gradually formulate queries to efficiently describe the content a user is looking for. In our framework, the evolution strategies are employed to evolve critical feature words for concept modeling in query formulation. The experimental results show that the framework developed is efficient and useful to enhance the quality of web search, and the concept-based semantic search can thus be achieved.
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paper_title: An Ontology-Based Methodology for Semantic Expansion Search

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Matching search technology based on query keyword has been widely used by traditional search way. It still belongs to pure keyword matching and can not acquire satisfactory search results. The essential reason is that traditional Web search lacks semantic understanding to user's search behaviors. In this study, we propose a novel ontology-based framework for semantic expansion search. Based on constructed domain ontology, semantic annotation algorithm and semantic expansion reasoning algorithm are presented in detail, which are associated with semantic annotation unit and semantic expansion reasoning engine respectively. Then a semantic search prototype system is designed and implemented. The experimental results show that semantic expansion search by proposed methodology can overcome limitations in comparison with traditional keyword search mode, and achieve higher recall ratio and precision ratio.
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paper_title: A smart web query method for semantic retrieval of web data

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Abstract The efficient query and extraction of web data is often difficult, because web data does not conform to any data organization standard. In addition, the development of web search technology is still at a relatively early stage. Search engines provide only primitive data query capabilities, and require a detailed syntactic specification to retrieve relevant data. Furthermore, web data exists in a myriad of formats including PDF documents, images, and sound clips that are difficult to be queried. This research proposes a smart web query (SWQ) method for the semantic retrieval of web data. The SWQ method uses domain semantics represented as context ontologies to specify and formulate appropriate web queries to search. This method also relies on semantic search filters to identify and rank relevant web pages semi-automatically. Unlike traditional ontologies that are structured in a hierarchy, terms and their relationships that pertain to a particular domain are organized with a flexible structure by the context ontologies. An SWQ engine is being developed to test the proposed method. Financial trading (e.g. stocks, bonds, unit trusts) is adapted as an example domain (i.e., context) to test and validate the SWQ method and engine.
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paper_title: Hybrid search: effectively combining keywords and semantic searches

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This paper describes hybrid search, a search method supporting both document and knowledge retrieval via the flexible combination of ontology-based search and keyword-based matching. Hybrid search smoothly copes with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the method outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a collection of about 18.000 technical documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to two applications released at Rolls-Royce plc for searching technical documentation about jet engines.
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paper_title: From Keyword Search to Exploration: Designing Future Search Interfaces for the Web

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This monograph is directed at researchers and developers who are designing the next generation of web search user interfaces, by focusing on the techniques and visualizations that allow users to interact with and have control over their findings. Search is one of the keys to the Web's success. The elegant way in which search results are returned has been well researched and is usually remarkably effective. However, the body of work produced by decades of research into information retrieval continues to grow rapidly and so it has become hard to synthesize the current state-of-the-art to produce a search interface that is both highly functional, but not cluttered and distracting. Further, recent work has shown that there is substantial room for improving the support provided to users who are exhibiting more exploratory forms of search, including when users may need to learn, discover, and understand novel or complex topics. Overall, there is a recognized need for search systems to provide effective user experiences that do more than simply return results. ::: ::: With the aim of producing more effective search interfaces, human computer interaction researchers and web designers have been developing novel interactions and features that enable users to conveniently visualize, parse, manipulate, and organize their Web search results. For instance, while a simple set of results may produce specific information (e.g., the capital of Peru), other methods may let users see and explore the contexts of their requests for information (more about the country, city, and nearby attractions), or the properties that associate groups of information assets (grouping hotels, restaurants, and attractions by their type, district, or price). Other techniques support information-seeking processes that may last weeks or months or may even require collaboration between multiple searchers. The choice of relevant result visualization strategies in new search systems should reflect the searchers and the higher-level information needs that motivate their searches. These examples provide further motivation for supporting designers, who are challenged to synthesize and understand the breadth of advances in search, so that they can determine the benefits of varied strategies and apply them appropriately to build better systems. ::: ::: To support researchers and designers in synthesizing and understanding the advances in search, this monograph offers a structured means to think about web search result visualization, based on an inclusive model of search that integrates information retrieval, information seeking and a higher-level context of tasks and goals. We examine each of these levels of search in a survey of advances in browsers and related tools by defining search-related cognitive processes and analyzing innovative design approaches. We then discuss evaluations at each of these levels of search, presenting significant results and identifying both the traditional and novel means used to produce them. Based on this examination, we propose a taxonomy of search result visualization techniques that can be used to identify gaps for future research and as a reference for designers of next generation web search systems.
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paper_title: Automated Concept Discovery from Web Resources

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The task of researching information on a particular topic using the Web is mainly accomplished by using keyword-based search engines. Although this approach provides a good starting point, it remains a tedious task to collect additional information that puts this topic in greater context. In this paper we present ConceptWorld, an instrument to automatically discover various facets of a topic of interest by extracting concepts from Web documents. The result materializes as a network of semantic concepts with their various contextual interrelations and provides a holistic view on the topic of interest.
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paper_title: Query by image and video content: the QBIC system

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Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >
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paper_title: XIRS : an XML-based Image Retrieval System

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This paper presents a formalization of an image retrieval system based on a notion of similarity between images in a multimedia database (namely XML-Enabled Database) and where a user request can be an image file or a keyword. The CBIR (Content Based Image Retrieval) system and the current search engines (e.g. Google, Yahoo....) make image search possible only when the query is a keyword. This type of search is limited because keywords are not expressive enough to describe all important characteristics of an image. For example, an exact match request cannot be formulated in such systems. Thus, we propose a search system in which a request might be an image file or a keyword. The MPEG-7 standard is used for describing an image as an XML document. A similarity distance between images is defined which is used to compare the request image with the images of a database. We also propose an algorithm to calculate a similarity distance between two XML nodes with a given precision 'k' (k is defined by the user: he can fix 'k' at 100% for the exact match retrieval of features) so as to be able to provide accurate information in response to a user request. The statistics show that our system is more efficient than leading content based image retrieval systems such as ERIC7 and current search engines.
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paper_title: Query by image and video content: the QBIC system

paper_content:

Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >
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paper_title: Evaluation and evolution of a browse and search interface: Relation Browser

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We present in this paper the design and an evaluation of a novel interface called the Relation Browser++ (RB++) for searching and browsing large information collections. RB++ provides visualized category overviews of an information space and allows dynamic filtering and exploration of the result set by tightly coupling the browsing and searching functions. A user study was conducted to compare the effectiveness, efficiency and user satisfaction of completing various types of searching and browsing using the RB++ interface and a traditional form-fillin interface for a video library. An exploration set of tasks was also included to examine the effectiveness of and user satisfaction with the RB++ when applied to a large federal statistics website. The comparison study strongly supported that RB++ was more effective, efficient, and satisfying for completing data exploration tasks. Based on the results, efforts to automatically populate the underlying database using machine learning techniques are underway. Preliminary implementations for two large-scale federal statistical websites have been installed on government servers for internal evaluation.
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paper_title: Exploratory search: from finding to understanding

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Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.
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paper_title: Exhibit: lightweight structured data publishing

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The early Web was hailed for giving individuals the same publishing power as large content providers. But over time, large content providers learned to exploit the structure in their data, leveraging databases and server side technologies to provide rich browsing and visualization. Individual authors fall behind once more: neither old-fashioned static pages nor domain-specific publishing frameworks supporting limited customization can match custom database-backed web applications. In this paper, we propose Exhibit, a lightweight framework for publishing structured data on standard web servers that requires no installation, database administration, or programming. Exhibit lets authors with relatively limited skills-those same enthusiasts who could write HTML pages for the early Web-publish richly interactive pages that exploit the structure of their data for better browsing and visualization. Such structured publishing in turn makes that data more useful to all of its consumers: individual readers get more powerful interfaces, mashup creators can more easily repurpose the data, and Semantic Web enthusiasts can feed the data to the nascent Semantic Web.
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paper_title: Faceted metadata for image search and browsing

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There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.
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paper_title: Collaborative multi-paradigm exploratory search

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New challenges for advanced web search interfaces and visualization tools arise as user needs shift from traditional lookup tasks towards more open ended search activities collectively described as exploratory search. Exploratory search opens new possibilities for employing social aspects for effective information retrieval. We facilitate exploratory search by providing users with an integrated search and navigation interface combining three search paradigms - full text search, view-based (faceted) search and content-based (query-by-example) search. Full text search is used for both domain data and metadata lookup, view-based search allows users to further refine/filter the respective result set, while content-based search orders or biases the results based on their similarity to a given set of sample results.
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paper_title: Columbia University's semantic video search engine

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This paper describes the newest revisions of "CuVid", Columbia University's video search engine, a system that enables semantic multimodal search over video broadcast news collections that first evaluated in the NIST TRECVID2005 benchmark and later expanded to include a large number (374) of visual concept detectors. In this work we start with concept classifiers trained with LSCOM (Large Scale Concept Ontology for Multimedia) and propose a new real-time system that interacts with large sets of video data. Extending prior work that identifies dominant concepts from a set of query results, we hope to further remove the requirement of full lexicon knowledge prior to use and introduce instant revision of concept influence as the user explores their result space.
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Title: Search Engines Going Beyond Keyword Search: A Survey

Section 1: Introduction
Description 1: Write about the importance of search engines, the limitations of keyword search, and the growing demand for more advanced search paradigms.

Section 2: Keyword Searching
Description 2: Explain the traditional keyword search method, its working, and how it indexes and retrieves information.

Section 3: The Problem with Keyword Searching
Description 3: Discuss the challenges and limitations associated with keyword-based searches such as ambiguity, relevance issues, and the lack of contextual understanding.

Section 4: Related Works
Description 4: Review existing research and developments in search engine technologies, focusing on content-based search, visual search, and semantic search.

Section 5: Search Classification
Description 5: Provide an overview of different types of search strategies including vertical search, enterprise search, desktop search, and peer-to-peer search.

Section 6: Crawler-based Search Engines
Description 6: Describe crawler-based search engines, how they function and provide listings, and discuss the issues they face.

Section 7: Human-powered Directories
Description 7: Explain the concept of human-powered directories, their reliance on human editors, and how they differ from crawler-based search engines.

Section 8: Hybrid Search Engines
Description 8: Discuss hybrid search engines that combine crawler-based and human-powered results for more comprehensive retrieval.

Section 9: Challenges for Today's Search Engine
Description 9: Outline the major challenges faced by current search engines including dealing with large volumes of data, improving findability, and retrieving multimedia content.

Section 10: Search Engines Going Beyond Keyword Search
Description 10: Classify and discuss various non-keyword based search approaches like content-based search, question-answering systems, computational knowledge engines, and semantic web search.

Section 11: Content-Based Search
Description 11: Detail the approach of content-based search engines, focusing on image and video retrieval and their importance in modern applications.

Section 12: Question-Answering (QA) System
Description 12: Explain how QA systems work to provide direct answers to questions rather than a list of documents, and mention examples like Yahoo! Answers and Wolfram-Alpha.

Section 13: Computational Knowledge Engine
Description 13: Describe computational knowledge engines, with a focus on Wolfram-Alpha, and explain how they dynamically compute answers instead of searching static web content.

Section 14: Semantic Web Search
Description 14: Discuss semantic search engines that analyze context and relationships between terms, providing examples like Hakia, SenseBot, Powerset, and DeepDyve.

Section 15: Exploratory Search
Description 15: Explain exploratory search and its support for discovery through querying and browsing, including types of searches and examples of exploratory search systems.

Section 16: Discussions
Description 16: Discuss the future trends of search engine technologies, the increasing importance of content, and the integration of multiple search paradigms.

Section 17: Conclusions
Description 17: Summarize the various search paradigms discussed, addressing issues with keyword-based searches, and emphasize the need for further research and standardization in advanced search technologies.