X-ENS: Semantic Enrichment of Web Search Results at Real-Time

While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories

Hybrid Similarity Function for Big Data Entity Matching with R-Swoosh

Entity Matching (EM) is the problem of determining if two entities in a data set refer to the same real-world object. For example, it decides if two given mentions in the data, such as “Helen Hunt” and “H. M. Hunt”, refer to the same real-world entity by using different similarity functions. This problem plays a key role in information integration, natural language understanding, information processing on the World-Wide Web, and on the emerging Semantic Web. This project deals with the similarity functions and thresholds utilized in them to determine the similarity of the entities. The work contains two major parts: implementation of a hybrid similarity function, which contains three different similarity functions to determine the similarity of entities, and an efficient method to determine the optimum threshold value for similarity functions to get accurate results

Toward Entity-Aware Search

As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability

Improving Entity Retrieval on Structured Data

The increasing amount of data on the Web, in particular of Linked Data, has led to a diverse landscape of datasets, which make entity retrieval a challenging task. Explicit cross-dataset links, for instance to indicate co-references or related entities can significantly improve entity retrieval. However, only a small fraction of entities are interlinked through explicit statements. In this paper, we propose a two-fold entity retrieval approach. In a first, offline preprocessing step, we cluster entities based on the \emph{x--means} and \emph{spectral} clustering algorithms. In the second step, we propose an optimized retrieval model which takes advantage of our precomputed clusters. For a given set of entities retrieved by the BM25F retrieval approach and a given user query, we further expand the result set with relevant entities by considering features of the queries, entities and the precomputed clusters. Finally, we re-rank the expanded result set with respect to the relevance to the query. We perform a thorough experimental evaluation on the Billions Triple Challenge (BTC12) dataset. The proposed approach shows significant improvements compared to the baseline and state of the art approaches

mashpoint: browsing the web along structured lines

Large numbers of Web sites support rich data-centric features to explore and interact with data. In this paper we present mashpoint, a framework that allows distributed data-powered Web applications to linked based on similarities of the entities in their data. By linking applications in this way we allow browsing with selections of data from one application to another application. This sort of browsing allows complex queries and exploration of data to be done by average Web users using multiple applications. We additionally use this concept to surface structured information to users in Web pages. In this paper we present this concept and our initial prototyp

MeLinDa: an interlinking framework for the web of data

The web of data consists of data published on the web in such a way that they can be interpreted and connected together. It is thus critical to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We consider here the various techniques developed for that purpose and analyze their commonalities and differences. We propose a general framework and show how the diverse techniques fit in the framework. From this framework we consider the relation between data interlinking and ontology matching. Although, they can be considered similar at a certain level (they both relate formal entities), they serve different purposes, but would find a mutual benefit at collaborating. We thus present a scheme under which it is possible for data linking tools to take advantage of ontology alignments.Comment: N° RR-7691 (2011

Drag it together with Groupie: making RDF data authoring easy and fun for anyone

One of the foremost challenges towards realizing a “Read-write Web of Data” [3] is making it possible for everyday computer users to easily find, manipulate, create, and publish data back to the Web so that it can be made available for others to use. However, many aspects of Linked Data make authoring and manipulation difficult for “normal” (ie non-coder) end-users. First, data can be high-dimensional, having arbitrary many properties per “instance”, and interlinked to arbitrary many other instances in a many different ways. Second, collections of Linked Data tend to be vastly more heterogeneous than in typical structured databases, where instances are kept in uniform collections (e.g., database tables). Third, while highly flexible, the problem of having all structures reduced as a graph is verbosity: even simple structures can appear complex. Finally, many of the concepts involved in linked data authoring - for example, terms used to define ontologies are highly abstract and foreign to regular citizen-users.To counter this complexity we have devised a drag-and-drop direct manipulation interface that makes authoring Linked Data easy, fun, and accessible to a wide audience. Groupie allows users to author data simply by dragging blobs representing entities into other entities to compose relationships, establishing one relational link at a time. Since the underlying representation is RDF, Groupie facilitates the inclusion of references to entities and properties defined elsewhere on the Web through integration with popular Linked Data indexing services. Finally, to make it easy for new users to build upon others’ work, Groupie provides a communal space where all data sets created by users can be shared, cloned and modified, allowing individual users to help each other model complex domains thereby leveraging collective intelligence