Adaptive Semantic Annotation of Entity and Concept Mentions in Text

The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases. These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a vocabulary that is unambiguously understood across applications. Tagging textual documents according to these knowledge bases is a challenging task. It involves recognizing the entities and concepts that have been mentioned in a particular passage and attempting to resolve eventual ambiguity of language in order to choose one of many possible meanings for a phrase. There has been substantial work on recognizing and disambiguating entities for specialized applications, or constrained to limited entity types and particular types of text. In the context of shared knowledge bases, since each application has potentially very different needs, systems must have unprecedented breadth and flexibility to ensure their usefulness across applications. Documents may exhibit different language and discourse characteristics, discuss very diverse topics, or require the focus on parts of the knowledge repository that are inherently harder to disambiguate. In practice, for developers looking for a system to support their use case, is often unclear if an existing solution is applicable, leading those developers to trial-and-error and ad hoc usage of multiple systems in an attempt to achieve their objective. In this dissertation, I propose a conceptual model that unifies related techniques in this space under a common multi-dimensional framework that enables the elucidation of strengths and limitations of each technique, supporting developers in their search for a suitable tool for their needs. Moreover, the model serves as the basis for the development of flexible systems that have the ability of supporting document tagging for different use cases. I describe such an implementation, DBpedia Spotlight, along with extensions that we performed to the knowledge base DBpedia to support this implementation. I report evaluations of this tool on several well known data sets, and demonstrate applications to diverse use cases for further validation

Effectively Grouping Named Entities From Click- Through Data Into Clusters Of Generated Keywords1

Many studies show that named entities are closely related to users\u27 search behaviors, which brings increasing interest in studying named entities in search logs recently. This paper addresses the problem of forming fine grained semantic clusters of named entities within a broad domain such as “company”, and generating keywords for each cluster, which help users to interpret the embedded semantic information in the cluster. By exploring contexts, URLs and session IDs as features of named entities, a three-phase approach proposed in this paper first disambiguates named entities according to the features. Then it properly weights the features with a novel measurement, calculates the semantic similarity between named entities with the weighted feature space, and clusters named entities accordingly. After that, keywords for the clusters are generated using a text-oriented graph ranking algorithm. Each phase of the proposed approach solves problems that are not addressed in existing works, and experimental results obtained from a real click through data demonstrate the effectiveness of the proposed approach

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the surface of water, Ripple Network stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that Ripple Network achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.Comment: CIKM 201

Entity recommendation and search in heterogeneous information networks

With the rapid development of social media and information network-based web services, data mining studies on network analysis have gained increasing attention in recent years. Many early studies focus on homogeneous network mining, with the assumption that the network nodes and links are of the same type (e.g., social networks). However, real-world data in many domains and applications are often multi-typed and interconnected, forming heterogeneous information networks. The objective of my thesis is to study effective and scalable approaches to help users explore and discover useful information and knowledge in heterogeneous information networks. I also aim to advance the principles and methodologies of mining heterogeneous information networks through these studies. Specifically, I study and focus on entity recommendation and search related problems in heterogeneous information networks. I investigate and propose data mining methodologies to facilitate the construction of entity recommender systems and search engines for heterogeneous networks. In this thesis, I first propose to study entity recommendation problem in heterogeneous information network scope with implicit feedback. Second, I study a real-world large-scale entity recommendation application with commercial search engine user logs and a web-scale entity graph. Third, I combine text information and heterogeneous relationships between entities to study citation prediction and search problem in bibliographical networks. Fourth, I introduce a user-guided entity similarity search framework in information networks to integrate users' guidance into entity search process, which helps alleviate entity similarity ambiguity problem in heterogeneous networks. The methodologies proposed in this thesis are critically important for information exploration in heterogeneous information networks. The principles and theoretical findings in these studies have potential impact in other information network related research fields and can be applied in a wide range of real-world applications

Constraint-Aware Approach to Web Service Composition

To improve the quality and composite the services with specific constraints using web services, Constraint aware approach based web service composition machines can automatically select, integrate and invoke different web services in-order to achieve the user specified task according to the user constraints. To implement this, first user has to register and get a unique username and password. Before that we have to compose all different web services together into one using web service composition method. The composition system has two kinds of participants, service provider and service requestor. The service providers propose web services for use. The service requestors consume information or services offered by service providers. If the service requestor requests for a service, then the provider search for a service that composes the available services in the service repository. Requestor can get the correct result as output if the input satisfies the constraints of the service. DOI: 10.17762/ijritcc2321-8169.16040