Encyclopaedic question answering

Open-domain question answering (QA) is an established NLP task which enables users to search for speciVc pieces of information in large collections of texts. Instead of using keyword-based queries and a standard information retrieval engine, QA systems allow the use of natural language questions and return the exact answer (or a list of plausible answers) with supporting snippets of text. In the past decade, open-domain QA research has been dominated by evaluation fora such as TREC and CLEF, where shallow techniques relying on information redundancy have achieved very good performance. However, this performance is generally limited to simple factoid and deVnition questions because the answer is usually explicitly present in the document collection. Current approaches are much less successful in Vnding implicit answers and are diXcult to adapt to more complex question types which are likely to be posed by users. In order to advance the Veld of QA, this thesis proposes a shift in focus from simple factoid questions to encyclopaedic questions: list questions composed of several constraints. These questions have more than one correct answer which usually cannot be extracted from one small snippet of text. To correctly interpret the question, systems need to combine classic knowledge-based approaches with advanced NLP techniques. To Vnd and extract answers, systems need to aggregate atomic facts from heterogeneous sources as opposed to simply relying on keyword-based similarity. Encyclopaedic questions promote QA systems which use basic reasoning, making them more robust and easier to extend with new types of constraints and new types of questions. A novel semantic architecture is proposed which represents a paradigm shift in open-domain QA system design, using semantic concepts and knowledge representation instead of words and information retrieval. The architecture consists of two phases, analysis – responsible for interpreting questions and Vnding answers, and feedback – responsible for interacting with the user. This architecture provides the basis for EQUAL, a semantic QA system developed as part of the thesis, which uses Wikipedia as a source of world knowledge and iii employs simple forms of open-domain inference to answer encyclopaedic questions. EQUAL combines the output of a syntactic parser with semantic information from Wikipedia to analyse questions. To address natural language ambiguity, the system builds several formal interpretations containing the constraints speciVed by the user and addresses each interpretation in parallel. To Vnd answers, the system then tests these constraints individually for each candidate answer, considering information from diUerent documents and/or sources. The correctness of an answer is not proved using a logical formalism, instead a conVdence-based measure is employed. This measure reWects the validation of constraints from raw natural language, automatically extracted entities, relations and available structured and semi-structured knowledge from Wikipedia and the Semantic Web. When searching for and validating answers, EQUAL uses the Wikipedia link graph to Vnd relevant information. This method achieves good precision and allows only pages of a certain type to be considered, but is aUected by the incompleteness of the existing markup targeted towards human readers. In order to address this, a semantic analysis module which disambiguates entities is developed to enrich Wikipedia articles with additional links to other pages. The module increases recall, enabling the system to rely more on the link structure of Wikipedia than on word-based similarity between pages. It also allows authoritative information from diUerent sources to be linked to the encyclopaedia, further enhancing the coverage of the system. The viability of the proposed approach was evaluated in an independent setting by participating in two competitions at CLEF 2008 and 2009. In both competitions, EQUAL outperformed standard textual QA systems as well as semi-automatic approaches. Having established a feasible way forward for the design of open-domain QA systems, future work will attempt to further improve performance to take advantage of recent advances in information extraction and knowledge representation, as well as by experimenting with formal reasoning and inferencing capabilities.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Supporting Scholarly Research Ideation through Web Semantics

We develop new methods and technologies for supporting scholarly research ideation, the tasks in which researchers develop new ideas for their work, through web semantics, computational representations of information found on the web, capturing meaning involving people’s experiences of things of interest. To do so, we first conducted a qualitative study with established researchers on their practices, using sensitizing concepts from information science, creative cognition, and art as a basis for framing and deriving findings. We found that participants engage in and combine a wide range of activities, including citation chaining, exploratory browsing, and curation, to achieve their goals of creative ideation. We derived a new, interdisciplinary model to depict their practices. Our study and findings address a gap in existing research: the creative nature of what researchers do has been insufficiently investigated. The model is expected to guide future investigations. We then use in-context presentations of dynamically extracted semantic information to (1) address the issues of digression and disorientation, which arise in citation chaining and exploratory browsing, and (2) provide contextual information in researchers’ prior work curation. The implemented interface, Metadata In-Context Explorer (MICE), maintains context while allowing new information to be brought into and integrated with the current context, reducing the needs for switching between documents and webpages. Study shows that MICE supports participants in their citation chaining processes, thus supports scholarly research ideation. MICE is implemented with BigSemantics, a metadata type system and runtime integrating data models, extraction rules, and presentation hints into types. BigSemantics operationalizes type-specific, dynamic extraction and rich presentation of semantic information (a.k.a. metadata) found on the web. The metadata type system, runtime, and MICE are expected to help build interfaces supporting dynamic exploratory search, browsing, and other creative tasks involving complex and interlinked semantics

Adaptive user interfaces for the Semantic Web

The Semantic Web aims to democratise data by making data open, shareable and recombinable (Berners-Lee, Hendler, & Lassila, 2001). To achieve these characteristics, Semantic Web data, different to data on the web, is structured. However, unlike traditional databases, the structure of Semantic Web data does not have to follow a single predefined schema. we believe that approaches for building user interfaces for data with a known structure are inadequate for the Semantic Web. In this thesis, we propose an adaptive user interface that categorises the Semantic Web data into groups and orders data according to user preference. Placing similar data items close together may make it easier for users to locate data items

Role of semantic indexing for text classification.

The Vector Space Model (VSM) of text representation suffers a number of limitations for text classification. Firstly, the VSM is based on the Bag-Of-Words (BOW) assumption where terms from the indexing vocabulary are treated independently of one another. However, the expressiveness of natural language means that lexically different terms often have related or even identical meanings. Thus, failure to take into account the semantic relatedness between terms means that document similarity is not properly captured in the VSM. To address this problem, semantic indexing approaches have been proposed for modelling the semantic relatedness between terms in document representations. Accordingly, in this thesis, we empirically review the impact of semantic indexing on text classification. This empirical review allows us to answer one important question: how beneficial is semantic indexing to text classification performance. We also carry out a detailed analysis of the semantic indexing process which allows us to identify reasons why semantic indexing may lead to poor text classification performance. Based on our findings, we propose a semantic indexing framework called Relevance Weighted Semantic Indexing (RWSI) that addresses the limitations identified in our analysis. RWSI uses relevance weights of terms to improve the semantic indexing of documents. A second problem with the VSM is the lack of supervision in the process of creating document representations. This arises from the fact that the VSM was originally designed for unsupervised document retrieval. An important feature of effective document representations is the ability to discriminate between relevant and non-relevant documents. For text classification, relevance information is explicitly available in the form of document class labels. Thus, more effective document vectors can be derived in a supervised manner by taking advantage of available class knowledge. Accordingly, we investigate approaches for utilising class knowledge for supervised indexing of documents. Firstly, we demonstrate how the RWSI framework can be utilised for assigning supervised weights to terms for supervised document indexing. Secondly, we present an approach called Supervised Sub-Spacing (S3) for supervised semantic indexing of documents. A further limitation of the standard VSM is that an indexing vocabulary that consists only of terms from the document collection is used for document representation. This is based on the assumption that terms alone are sufficient to model the meaning of text documents. However for certain classification tasks, terms are insufficient to adequately model the semantics needed for accurate document classification. A solution is to index documents using semantically rich concepts. Accordingly, we present an event extraction framework called Rule-Based Event Extractor (RUBEE) for identifying and utilising event information for concept-based indexing of incident reports. We also demonstrate how certain attributes of these events e.g. negation, can be taken into consideration to distinguish between documents that describe the occurrence of an event, and those that mention the non-occurrence of that event

On Two Web IR Boosting Tools: Clustering and Ranking

This thesis investigates several research problems which arise in modern Web Information Retrieval (WebIR). The Holy Grail of modern WebIR is to find a way to organize and to rank results so that the most ``relevant' come first. The first break-through technique was the exploitation of the link structure of the Web graph in order to rank the result pages, using the well-known Hits and Pagerank algorithms. This link-analysis approaches have been improved and extended, but yet they seem to be insufficient in providing a satisfying search experience. In a number of situations a flat list of search results is not enough, and the users might desire to have search results grouped on-the-fly in folders of similar topics. In addition, the folders should be annotated with meaningful labels for rapid identification of the desired group of results. In other situations, users may have different search goals even when they express them with the same query. In this case the search results should be personalized according to the users' on-line activities. In order to address this need, we will discuss the algorithmic ideas behind SnakeT, a hierarchical clustering meta-search engine which personalizes searches according to the clusters selected by users on-the-fly. There are also situations where users might desire to access fresh information. In these cases, traditional link analysis could not be suitable. In fact, it is possible that there is not enough time to have many links pointing to a recently produced piece of information. In order to address this need, we will discuss the algorithmic and numerical ideas behind a new ranking algorithm suitable for ranking fresh type of information, such as news articles or blogs. When link analysis suffices to produce good quality search results, the huge amount of Web information asks for fast ranking methodologies. We will discuss numerical methodologies for accelerating the eingenvector-like computation, commonly used by link analysis. An important result of this thesis is that we show how to address the above predominant issues of Web Information Retrieval by using clustering and ranking methodologies. We will demonstrate that both clustering and ranking have a mutual reinforcement propriety which has not yet been studied intensively. This propriety can be exploited to boost the precision of both the two methodologies

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond