Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

Semantically enhanced information retrieval (IR) is aimed at improving classical IR methods and goes way beyond plain Boolean keyword matching with the main goal of better serving implicit and ambiguous information needs. As a de-facto pre-requisite to semantic IR, different information extraction (IE) techniques are used to mine unstructured text for underlying knowledge. In this paper we present a method that combines both IE and IR to enable semantic search in natural language texts. First, we apply semantic role labeling (SRL) to automatically extract event-oriented information found in natural language texts to an RDF knowledge graph leveraging semantic web technology. Second, we investigate how a custom flavored graph traversal spreading activation algorithm can be employed to interpret user’s information needs on top of the prior-extracted knowledge base. Finally, we present an assessment on the applicability of our method for semantically enhanced IR. An experimental evaluation on partial WikiQA dataset shows the strengths of our approach and also unveils common pitfalls that we use as guidelines to draw further work directions in the open-domain semantic search field.

Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

Detecting Human Diseases Relatedness

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.

Detecting Human Diseases Relatedness

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

Graph-Based Abstractive Summarization

Customization of information from web documents is an immense job that involves mainly the shortening of original texts. Extractive methods use surface level and statistical features for the selection of important sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. In this paper, we propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration.

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.

Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

Abstractive Summarization

Customization of information from web documents is an immense job that involves mainly the shortening of original texts. This task is carried out using summarization techniques. In general, an automatically generated summary is of two types – extractive and abstractive. Extractive methods use surface level and statistical features for the selection of important sentences, without considering the meaning conveyed by those sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. Furthermore, a deep linguistic analysis is needed for generating summaries. However, the bottleneck behind abstractive summarization is that it requires semantic representation, inference rules and natural language generation. In this paper, The authors propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration. Summary obtained using the proposed approach is compared with extractive and template based summaries, and also evaluated using ROUGE scores.