M Owda

Conversation-Based Natural Language Interface to Relational Databases

This paper proposes a new approach for creating conversation-based natural language interfaces to relational databases by combining goal oriented conversational agents and knowledge trees. Goal oriented conversational agents have proven their capability to disambiguate the users needs and to converse within a context (i.e. specific domain). Knowledge trees used to overcome the lacking of connectivity between the conversational agent and the relational database, through organizing the domain knowledge in knowledge trees. Knowledge trees also work as a road map for the conversational agent dialogue flow. The proposed framework makes it easier for knowledge engineers to develop a reliable conversation-based NLI-RDB. The developed prototype system shows excellent performance on common queries (i. e. queries extracted from expert by a knowledge engineer). The user will have a friendly interface that can converse with the relational database.

Information extraction for SQL query generation in the conversation-based interfaces to relational databases (C-BIRD)

This paper presents a novel methodology of incorporating Information Extraction (IE) techniques into an Enhanced Conversation-Based Interface to Relational Databases (C-BIRD) in order to generate dynamic SQL queries. Conversational Agents can converse with the user in natural language about a specific problem domain. In C-BIRD, such agents allow a user to converse with a relational database in order to retrieve answers to queries without knowledge of SQL. A Knowledge Tree is used to direct the Conversational Agent towards the goal i.e. creating an SQL query to fit the users natural language enquiry. The use of IE techniques such as template filling helps in answering the users queries by processing the users dialogue and extracts understandable patterns that fills the SQL templates. The developed prototype system increases the number of answered natural language queries in comparison to hardcoded decision paths in the knowledge trees.

Experiment for Analysing the Impact of Financial Events on Twitter

Twitter, as the heart of publicly accessible Social Media, is one of the currently used platforms to share financial information and is a valuable source of information for different roles in the financial market. For all these roles, the quality analysis of Twitter as a source of financial information is essential to take decisions. The work in this paper is aligned with the ongoing work of the authors to a solution for irregularity monitoring in the financial market by harnessing data in online social media. To do so, the permeability of a variety of social media data feeders to financial irregularities should be analysed. That is the case of the experiment in this paper by putting the focus on Twitter microblogging platform and checking if this general purpose social media is permeable to a specific financial event. For this, we detail the analysis of Twitter permeability to a specific event in the past few months: the announcement about the merge of Tesco and Booker to create a UK’s Leading Food Business on the 27th January 2017. Both companies Tesco PLC and Booking Group PLC are listed in the main market of LSE (London Stock Exchange). Our findings provide promising evidences to address the problem of real-time detection of irregularities in the financial market via Twitter according to the volume (as a sign of the importance of the irregularity) and to other features (as signs of the potential origin causing the irregularity).

Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)

This paper proposes a novel approach for building a Natural Language Interface to a Relational Database (NLI-RDB) using Conversational Agent (CA), Information Extraction (IE) and Object Relational Mapping (ORM) framework. The CA will help in disambiguating the user’s queries and guiding the user interaction. IE will play an important role in named entities extraction in order to map Natural Language queries into database queries. The ORM framework i.e. the Hibernate framework resolves the impedance mismatch between the Object Oriented Paradigms (OOP) and Relational Databases (RDBs) i.e. OOP concepts differ from RDB concepts, thus it reduces the complexity in generating SQL statements. Also, by utilizing ORM framework, the RDBs entities are mapped into real world objects, which bring the RDBs a step closer to the user. In addition, the ORM framework simplify the interaction between OOP and RDBs. The developed NLI-RDB system allows the user to interact with objects directly in natural language and through navigation, rather than by using SQL statements. This direct interaction tends to be easier and more acceptable for humans whom are nor technically orientated and have no SQL knowledge. The NLI-RDB system also offers friendly and interactive user interface in order to refine the query generated automatically. The NLI-RDB system has been evaluated by a group of participants through a combination of qualitative and quantitative measures. The experimental results show good performance of the prototype and excellent user’s satisfaction.

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK’s Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the financial market. The experimental results confirm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that although Twitter is not a specific financial forum, it is permeable to financial events. Therefore it should be considered within the architecture for real-time monitoring of irregularities in the financial market.

Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards

Financial discussion boards (FDBs) have been widely used for a variety of financial knowledge exchange activities through the posting of comments. Popular public FDBs are prone to being used as a medium to spread false financial information due to larger audience groups. Although online forums are usually integrated with anti-spam tools, such as Akismet, moderation of posted content heavily relies on manual tasks. Unfortunately, the daily comments volume received on popular FDBs realistically prevents human moderators to watch closely and moderate possibly fraudulent content, not to mention moderators are not usually assigned with such task. Due to the absence of useful tools, it is extremely time consuming and expensive to manually read and determine whether each comment is potentially fraudulent. This paper presents novel forward and backward analysis methodologies implemented in an Information Extraction (IE) prototype system named FDBs Miner (FDBM). The methodologies aim to detect potentially illegal Pump and Dump comments on FDBs with the integration of per-minute share prices in the detection process. This can possibly reduce false positives during the detection as it categorises the potentially illegal comments into different risk levels for investigation purposes. The proposed system extracts company’s ticker symbols (i.e. unique symbol that represents and identifies each listed company on stock market), comments and share prices from FDBs based in the UK. The forward analysis methodology flags the potentially Pump and Dump comments using a predefined keywords template and labels the flagged comments with price hike thresholds. Subsequently, the backward analysis methodology employs a moving average technique to determine price abnormalities and backward analyse the flagged comments. The first detection stage in forward analysis found 9.82% of potentially illegal comments. It is unrealistic and unaffordable for human moderators or financial surveillance authorities to read these comments on a daily basis. Hence, by integrating share prices to perform backward analysis can categorise the flagged comments into different risk levels. It helps relevant authorities to prioritise and investigate into the higher risk flagged comments, which could potentially indicate a real Pump and Dump crime happening on FDBs when the system is being used in real time.